CN117878925A - Power transmission data control method and system of smart power grid - Google Patents
Power transmission data control method and system of smart power grid Download PDFInfo
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Abstract
The invention relates to a power transmission data control method and system of a smart grid. The method comprises the following steps: acquiring real-time power data and smart grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve; carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; and performing model training on the dynamic power load peak data to generate a power demand prediction model. According to the intelligent power grid, the accuracy, stability and reliability of the intelligent power grid are improved through comprehensive data processing, dynamic load curve conversion, load peak value data extraction and prediction models, equipment information acquisition and power grid adjustment, power grid nonlinear control and data visualization.
Description
Technical Field
The invention relates to the technical field of power data control, in particular to a power transmission data control method and system of a smart grid.
Background
Conventional power systems are the starting point for smart grid technology development. These systems rely on centralized power plants to deliver power to consumers, and data control methods are limited, mainly by manual operation and simple automation techniques, and have the problems of energy waste and slow response speed. With the progress of information technology and communication technology, smart grids start to become a brand-new corner, and through big data analysis and machine learning, a grid manager can better predict load demands, optimize supply links, reduce operation cost, and improve reliability and stability of the grid. Meanwhile, the SCADA system (monitoring and data acquisition system) is widely applied, so that a power grid manager can monitor the state of the whole power grid in real time, and quickly identify and respond to faults. However, the traditional power transmission data control method may be passive in equipment information acquisition and power grid adjustment, lacks of intelligence and instantaneity, and has limitation in complexity and data presentation modes of power grid control, so that control accuracy and instantaneity are low.
Disclosure of Invention
Based on this, it is necessary to provide a method and a system for controlling power transmission data of a smart grid, so as to solve at least one of the above technical problems.
In order to achieve the above object, a method for controlling power transmission data of a smart grid includes the steps of:
step S1: acquiring real-time power data and smart grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
step S2: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
step S3: acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
Step S4: performing three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
According to the intelligent power grid energy demand prediction method, the intelligent power grid can predict and manage the energy demand more accurately through real-time comprehensive energy load curve, so that energy scheduling and distribution are optimized, and energy utilization efficiency is improved. Comprehensively considering the power data and the smart grid environment data can help the smart grid system to better cope with changing environment factors, and the reliability and stability of the power supply system are improved. Through dynamic load curve conversion, the intelligent power grid can predict load demands more accurately and adjust energy supply strategies in a targeted manner, so that energy production and distribution cost is reduced. The real-time comprehensive energy load curve provides an accurate data basis for a decision maker, and can be used for making decisions such as energy policy, planning energy facility construction, optimizing energy market operation and the like. Through comprehensive analysis of the power data and the environment data, the intelligent power grid can better support integration and utilization of renewable energy sources and promote sustainable development of an energy system. By dynamic power distribution, the system can respond to the changing load demand in real time, and the flexibility of power distribution is improved. Through accurate electric power demand prediction, excessive distribution electric power can be avoided, energy waste is reduced, and energy utilization efficiency is improved. By adopting the optimal power distribution scheme, the supply and demand relationship can be effectively balanced, and the stability and the robustness of the power system are improved. By reducing excessive distribution and improving system efficiency, the running cost of the power system can be reduced, and the overall economic benefit is improved. Through real-time power grid regulation and edge calculation control, the system can respond to power demand change more quickly, and the real-time response capability of the power system is improved. And stable power transmission data is generated through power control stability regulation, so that the stability and reliability of power transmission are improved. And equipment is regulated according to the optimal power distribution scheme, so that the equipment utilization rate is optimized, the equipment service life is prolonged, and the equipment operation cost is reduced. Through nonlinear control of the power grid, fine management of operation of the power grid is achieved, and operation efficiency and stability of the power grid are improved. The optimized power grid control strategy is beneficial to reducing the loss of a power transmission line, improving the power transmission efficiency and reducing the energy waste. By implementing the intelligent control strategy, the power grid faults can be timely found and processed, and the safety and reliability of the power grid are improved. The intelligent power grid management reduces the manual intervention requirement, reduces the operation and maintenance cost and improves the operation and maintenance efficiency. Therefore, the intelligent power grid system and the intelligent power grid system improve the accuracy, stability and reliability of the intelligent power grid through comprehensive data processing, dynamic load curve conversion, load peak value data extraction and prediction models, equipment information acquisition and power grid adjustment, power grid nonlinear control and data visualization.
In this specification, there is provided a power transmission data control system of a smart grid for executing the above-described power transmission data control method of a smart grid, the power transmission data control system of a smart grid including:
the load analysis module is used for acquiring real-time power data and intelligent power grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
the power distribution module is used for extracting load peak value data of the real-time comprehensive energy load curve based on the preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
the stable regulation and control module is used for acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
The linear control module is used for carrying out three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
The method has the beneficial effects that the comprehensive power environment data set is generated by fusing the real-time power data and the intelligent power grid environment data, so that the integrity and the accuracy of the data are improved. And through dynamic load curve conversion, a comprehensive energy load curve is generated in real time, and a foundation is provided for the subsequent power demand prediction and optimization. Through model training, a power demand prediction model is generated, so that future power demands can be predicted, and basis is provided for power distribution. Based on the power demand prediction model, dynamic power distribution is realized, an optimal power distribution scheme is generated, energy utilization efficiency is improved, and cost caused by unbalanced supply and demand is reduced. And according to the optimal power distribution scheme, real-time power grid adjustment is performed, and optimal management of the intelligent power grid is realized through the deployment information data of the edge equipment. The stability and the reliability of the power grid are improved, and the energy waste and the energy loss are reduced through the generation and the regulation and the control of the power grid edge calculation control data. And generating a power transmission three-dimensional model through three-dimensional point cloud conversion, and providing a visual basis for nonlinear control of the power grid. Based on the power transmission three-dimensional model, nonlinear control and intelligent management of the power grid are realized, and the running efficiency and safety of the power grid are improved. Through visualization of the intelligent control data of the power grid power transmission, a control report of the intelligent power grid power transmission data is generated, and decision support and management reference are provided for a power management department. Therefore, the intelligent power grid system and the intelligent power grid system improve the accuracy, stability and reliability of the intelligent power grid through comprehensive data processing, dynamic load curve conversion, load peak value data extraction and prediction models, equipment information acquisition and power grid adjustment, power grid nonlinear control and data visualization.
Drawings
Fig. 1 is a schematic flow chart of steps of a method for controlling power transmission data of a smart grid;
FIG. 2 is a flowchart illustrating the detailed implementation of step S2 in FIG. 1;
FIG. 3 is a flowchart illustrating the detailed implementation of step S24 in FIG. 2;
FIG. 4 is a flowchart illustrating the detailed implementation of step S243 in FIG. 3;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
To achieve the above objective, referring to fig. 1 to 4, a method for controlling power transmission data of a smart grid includes the following steps:
step S1: acquiring real-time power data and smart grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
step S2: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
Step S3: acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
step S4: performing three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
According to the intelligent power grid energy demand prediction method, the intelligent power grid can predict and manage the energy demand more accurately through real-time comprehensive energy load curve, so that energy scheduling and distribution are optimized, and energy utilization efficiency is improved. Comprehensively considering the power data and the smart grid environment data can help the smart grid system to better cope with changing environment factors, and the reliability and stability of the power supply system are improved. Through dynamic load curve conversion, the intelligent power grid can predict load demands more accurately and adjust energy supply strategies in a targeted manner, so that energy production and distribution cost is reduced. The real-time comprehensive energy load curve provides an accurate data basis for a decision maker, and can be used for making decisions such as energy policy, planning energy facility construction, optimizing energy market operation and the like. Through comprehensive analysis of the power data and the environment data, the intelligent power grid can better support integration and utilization of renewable energy sources and promote sustainable development of an energy system. By dynamic power distribution, the system can respond to the changing load demand in real time, and the flexibility of power distribution is improved. Through accurate electric power demand prediction, excessive distribution electric power can be avoided, energy waste is reduced, and energy utilization efficiency is improved. By adopting the optimal power distribution scheme, the supply and demand relationship can be effectively balanced, and the stability and the robustness of the power system are improved. By reducing excessive distribution and improving system efficiency, the running cost of the power system can be reduced, and the overall economic benefit is improved. Through real-time power grid regulation and edge calculation control, the system can respond to power demand change more quickly, and the real-time response capability of the power system is improved. And stable power transmission data is generated through power control stability regulation, so that the stability and reliability of power transmission are improved. And equipment is regulated according to the optimal power distribution scheme, so that the equipment utilization rate is optimized, the equipment service life is prolonged, and the equipment operation cost is reduced. Through nonlinear control of the power grid, fine management of operation of the power grid is achieved, and operation efficiency and stability of the power grid are improved. The optimized power grid control strategy is beneficial to reducing the loss of a power transmission line, improving the power transmission efficiency and reducing the energy waste. By implementing the intelligent control strategy, the power grid faults can be timely found and processed, and the safety and reliability of the power grid are improved. The intelligent power grid management reduces the manual intervention requirement, reduces the operation and maintenance cost and improves the operation and maintenance efficiency. Therefore, the intelligent power grid system and the intelligent power grid system improve the accuracy, stability and reliability of the intelligent power grid through comprehensive data processing, dynamic load curve conversion, load peak value data extraction and prediction models, equipment information acquisition and power grid adjustment, power grid nonlinear control and data visualization.
In the embodiment of the present invention, as described with reference to fig. 1, the flow chart of the steps of a method for controlling power transmission data of a smart grid according to the present invention is shown, and in this example, the method for controlling power transmission data of a smart grid includes the following steps:
step S1: acquiring real-time power data and smart grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
in the embodiment of the invention, the electric power data are acquired in real time by using the devices such as the sensor, the intelligent ammeter and the like. And acquiring information such as the running state of the power grid, the power load and the like through a real-time data interface provided by the power company. Meteorological data, such as temperature, humidity, wind speed and the like, are collected by using environmental sensors, weather stations and other devices, and environmental factors affecting power requirements are utilized. Environmental information, such as device operating status, fault information, etc., is obtained from the smart grid device. And cleaning the real-time power data and the intelligent power grid environment data, and processing missing values, abnormal values and the like. And carrying out time synchronization on the data to ensure the time sequence consistency of the data. And fusing the cleaned real-time power data with the intelligent power grid environment data. The association may be based on a time stamp or other critical information. Data fusion algorithms such as database association queries, time series merging, etc. are used. And integrating the fused data into a comprehensive power environment data set which comprises power data and environment data. And (3) carrying out data processing on the comprehensive power environment data set, and extracting key characteristics such as power load, environment temperature, humidity and the like. And a filtering technology is used for smoothing the curve, so that noise interference is reduced. Dynamic load curves are generated using time series analysis methods such as moving averages, exponential smoothing, etc. Considering the correlation between the power data and the environmental data, it is ensured that the generated load curve reflects the changes of the power system under different environmental conditions. And integrating the generated dynamic load curves to obtain a real-time comprehensive energy load curve. The variation of different time scales is taken into account to meet the analysis of different granularities of real-time energy demands.
Step S2: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
in the embodiment of the invention, the load peak value data is identified and extracted according to the real-time comprehensive energy load curve by formulating the preset peak value screening quantity. Techniques such as sliding windows, peak detection algorithms, etc. may be used. An appropriate power demand prediction model is selected, such as a time series model (ARIMA, prophet), a machine learning model (regression model, decision tree), or a deep learning model (LSTM, GRU, transformer). And using historical dynamic power load peak value data as a training set, dividing the data into a training set and a verification set, and training and optimizing the model. And predicting the dynamic power load peak value data in a future period by using the trained model. The model is ensured to capture the characteristics of trend, periodicity and the like of the load curve. And using a prediction result obtained by the power demand prediction model for dynamic power distribution. And (3) formulating an optimal power distribution algorithm, and generating an optimal power distribution scheme by taking factors such as power supply capacity, cost, renewable energy source utilization and the like into consideration. And implementing an optimal power distribution scheme, and distributing power resources to different parts so as to meet the actual requirements of the system. Real-time power data is monitored and the distribution scheme is adjusted as required.
Step S3: acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
in the embodiment of the invention, the sensor and the monitoring equipment are deployed for collecting the data such as the state information of the edge equipment, the power grid parameters, the energy consumption and the like. And a data acquisition protocol and a communication mode are designed to ensure that the equipment can transmit data to a central control system in real time. And cleaning, processing and integrating the acquired equipment information to obtain accurate and usable edge equipment deployment information data. The intelligent power grid is adjusted in real time by utilizing the information data of the deployment of the edge equipment, and the intelligent power grid relates to control of distributed energy sources, energy storage systems, frequency modulation equipment and the like. And (3) formulating a power grid regulation algorithm, and generating a real-time power grid regulation strategy by taking factors such as maximum capacity of equipment, energy supply and demand balance, voltage and frequency control and the like into consideration. And generating power grid edge calculation control data according to the real-time power grid regulation result, wherein the data comprise power grid state information, equipment operation parameters, load distribution and the like and are used for guiding the next power control. And carrying out power control stability regulation and control by using the power grid edge calculation control data. Advanced control algorithms are used to ensure that the power system can maintain a stable voltage and frequency under different load conditions. Aiming at possible sudden load change or equipment failure, a corresponding coping strategy is implemented, and the stability of the power grid is ensured. And generating power transmission stability data according to the result of power control, wherein the power transmission stability data reflects the regulated stable state of the power system, and the information comprises voltage, frequency, load balance and the like.
Step S4: performing three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
In the embodiment of the invention, the power transmission line and the equipment are scanned by using a laser scanner or other three-dimensional scanning technologies, so that the point cloud data are obtained. And processing and analyzing the point cloud data to generate a power transmission three-dimensional model. The three-dimensional model is ensured to accurately reflect the geometric shape, equipment layout and environmental characteristics of the power transmission line. And developing a power grid nonlinear control algorithm based on the power transmission three-dimensional model. The control strategy is designed to optimize power transfer efficiency and stability in consideration of nonlinear characteristics of the power system, including load variations, line impedance, equipment limitations, and the like. And generating intelligent control data of the power grid power transmission, wherein the intelligent control data comprise information such as equipment adjusting parameters, power flow distribution and the like. And visualizing the power transmission three-dimensional model and the intelligent control data of the power grid transmission by using three-dimensional visualization software or library. And visually displaying the states and the control strategies of the power transmission lines and the equipment so that a user can intuitively understand the running condition and the control effect of the power grid. And generating a smart grid power transmission data control report according to the visualization result. The report includes information about the geometry of the power transmission lines, equipment layout, control parameters, real-time operating conditions, etc., as well as assessment and advice on grid operating efficiency and stability.
Preferably, step S1 comprises the steps of:
step S11: acquiring real-time power data and smart grid environment data by using a sensor;
step S12: carrying out data format standardization on the real-time power data and the intelligent power grid environment data to generate real-time power standard data and intelligent power grid standard environment data; filling data missing values of the real-time power standard data and the smart grid standard environment data to generate real-time power complete data and smart grid complete environment data;
step S13: carrying out data fusion on the real-time electric power complete data and the intelligent power grid complete environment data to generate a comprehensive electric power environment data set; carrying out power grid operation dynamic load data screening on the comprehensive power environment data set to generate a comprehensive power dynamic load data set;
step S14: performing data dimension reduction on the comprehensive power dynamic load data set by a principal component analysis method to generate a comprehensive power dynamic load dimension reduction data set; and performing curve conversion on the comprehensive power dynamic load dimension reduction data set so as to generate a real-time comprehensive energy load curve.
According to the invention, through the steps S11 and S12, the system can acquire the electric power data and the intelligent power grid environment data in real time, and perform standardized processing on the electric power data and the intelligent power grid environment data, fill the missing values, and ensure the accuracy and the integrity of the data. Through step S13, the system fuses the power data and the environment data to generate a comprehensive power environment data set, provides comprehensive data support for power grid operation, generates a comprehensive power dynamic load data set through screening, and provides a basis for subsequent analysis. Through step S14, the system utilizes the principal component analysis method to carry out dimension reduction processing on the comprehensive power dynamic load data set, simplifies the data structure, improves the processing efficiency, and converts the data into a real-time comprehensive energy load curve, so that the data is more visual and easy to understand. The automatic processing of the whole process can improve the processing speed and accuracy of the energy data, and provide more reliable support for the operation of the power system and the energy management, thereby improving the efficiency and accuracy of the energy management.
In the embodiment of the invention, the sensor network is deployed to acquire real-time power data and smart grid environment data, and the sensor can comprise a power metering device, a temperature sensor, a humidity sensor, a wind speed sensor and the like and is used for collecting various data related to power production and distribution and environment data. Developing an algorithm and a program for data format standardization, and carrying out standardization processing on real-time data acquired by a sensor to ensure consistency and compatibility of data formats. And a data missing value filling algorithm is realized, filling is carried out according to the data characteristics and the historical data, and the integrity of the real-time power data and the intelligent power grid environment data is ensured. And designing a data fusion algorithm, and fusing the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set. And developing a power grid operation dynamic load data screening algorithm, and screening the comprehensive power environment data set according to the power grid operation state and the requirements to generate a comprehensive power dynamic load data set. The Principal Component Analysis (PCA) method is realized, the dimension reduction processing is carried out on the comprehensive power dynamic load data set, the main characteristics of the data are extracted, and the data dimension is reduced. And developing a curve conversion algorithm to convert the comprehensive power dynamic load reduced-dimension data set into a real-time comprehensive energy load curve so as to more intuitively display the load condition of the power system.
Preferably, step S2 comprises the steps of:
step S21: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; historical data collection is carried out on the dynamic power load peak value data, and historical dynamic power load peak value data are generated;
step S22: carrying out data set division on historical dynamic power load peak data to generate a model training set and a model testing set; model training is carried out on the model training set according to a long-term and short-term memory network algorithm, and a power demand training model is generated; performing model optimization iteration on the power demand training model by using a model test set so as to generate a power demand prediction model;
step S23: the dynamic power load peak value data is imported into a power demand prediction model to predict power demand, and power demand prediction data is generated;
step S24: carrying out power supply and demand quantification on the power demand prediction data by utilizing a power supply and demand analysis formula to obtain a power supply and demand value; and dynamically distributing the power demand prediction data according to the power supply and demand value, so as to generate an optimal power distribution scheme.
According to the invention, the dynamic power load peak value data is extracted from the real-time comprehensive energy load curve, and the historical dynamic power load peak value data is generated through historical data collection, so that the system can better know the fluctuation and periodicity of the power load. The system uses long-term memory network (LSTM) algorithms for model training to generate a power demand training model. By means of iterative optimization of the model test set, a more accurate power demand prediction model is generated, and the accurate prediction capability of power demand is improved. The dynamic power load peak data is imported into the power demand prediction model to generate power demand prediction data, so that trend and change of future power demand can be known in real time, and preparation is made for power supply. The system utilizes the power supply and demand analysis formula to quantize the power demand prediction data to obtain a power supply and demand value, and provides real-time quantization evaluation on the power demand, so that the system can dynamically distribute power according to the power supply and demand value to generate an optimal power distribution scheme. Through dynamic power distribution, the system can adjust power distribution according to real-time power demand conditions so as to meet demands to the greatest extent and avoid resource waste, thereby being beneficial to improving the efficiency and the sustainability of the power system and utilizing available resources to the greatest extent.
As an example of the present invention, referring to fig. 2, the step S2 in this example includes:
step S21: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; historical data collection is carried out on the dynamic power load peak value data, and historical dynamic power load peak value data are generated;
in the embodiment of the invention, the data of the real-time comprehensive energy load curve is acquired, and the data are related to a monitoring system, a sensor or other data acquisition equipment so as to acquire the load conditions of various energy sources (such as electric power, natural gas, solar energy and the like) in real time. Based on the preset peak screening quantity, the real-time comprehensive energy load curve is analyzed and processed to extract load peak data, and the load peak data can be realized through an algorithm or a data processing technology, such as a sliding window method, a peak detection algorithm and the like. Dynamic power load peak data is screened from the extracted load peak data, and the classification and processing of the load data of different energy sources are involved to ensure that the extracted data accords with the characteristics of the power load. The dynamic power load peak data is stored and the historical data is collected, which can be realized through a database, a data warehouse or other data storage systems, so that the lasting storage and management of the historical data are ensured. Generating historical dynamic power load peak data according to the collected historical data, and involving the steps of data cleaning, data preprocessing, data analysis and the like to ensure the quality and accuracy of the generated historical data.
Step S22: carrying out data set division on historical dynamic power load peak data to generate a model training set and a model testing set; model training is carried out on the model training set according to a long-term and short-term memory network algorithm, and a power demand training model is generated; performing model optimization iteration on the power demand training model by using a model test set so as to generate a power demand prediction model;
in the embodiment of the invention, the historical dynamic power load peak value data is divided into a model training set and a model testing set. In general, a sliding window method or a random sampling method in time sequence data can be adopted for dividing, so that the data of a training set and a testing set are guaranteed to have representativeness and certain randomness. Model training is performed on the model training set using a long short term memory network (LSTM) algorithm. LSTM is a Recurrent Neural Network (RNN) variant suitable for processing and predicting time series data that is capable of effectively capturing long-term dependencies in time series data. In the model training process, network structure, super parameters (such as learning rate, hidden layer node number, etc.) and loss functions need to be determined. Model parameters may be updated using a back propagation algorithm and optimizers (e.g., adam, SGD, etc.) to minimize the loss function. And evaluating and optimizing iteration is carried out on the power demand training model by using the model test set. By verifying on the test set, the performance and generalization capability of the model can be evaluated, and the structure and parameters of the model can be adjusted accordingly, so that the prediction accuracy and stability of the model can be improved. Cross-validation, hyper-parametric search, etc. techniques may be employed to further optimize the model to find the optimal model configuration. And generating a final power demand prediction model after multiple rounds of iterative optimization. The model can accurately predict the power demand in a certain time period in the future according to historical dynamic power load peak data.
Step S23: the dynamic power load peak value data is imported into a power demand prediction model to predict power demand, and power demand prediction data is generated;
in the embodiment of the invention, the data is required to be preprocessed by ensuring that the format and structure of the dynamic power load peak data are matched with the data format input by the model, so that the quality and the integrity of the data are ensured. Inputting the prepared dynamic power load peak data into the power demand prediction model for prediction typically involves inputting the data into a model that has been trained and obtaining the prediction results of the model output. And predicting the dynamic power load peak data by using a power demand prediction model to generate power demand prediction data. The forecast data may include power demand values for each point in time over a future period of time, typically in the form of a time series. The generated electricity demand forecast data is used for practical applications including adjusting electricity production plans, optimizing electricity supply chains, developing electricity market strategies, etc., to meet future electricity demands.
Step S24: carrying out power supply and demand quantification on the power demand prediction data by utilizing a power supply and demand analysis formula to obtain a power supply and demand value; and dynamically distributing the power demand prediction data according to the power supply and demand value, so as to generate an optimal power distribution scheme.
In the embodiment of the invention, by determining a formula for power supply and demand analysis, a plurality of factors such as power supply capacity, predicted power demand, system reliability requirement and the like are considered, and common formulas include a supply and demand balance equation, a load curve and the like. Quantifying the power demand prediction data according to the selected power supply and demand analysis formula involves converting the prediction data into appropriate units (e.g., power, energy) to match the requirements of the formula. And combining the quantized power demand prediction data with the existing power supply condition by using a power supply and demand analysis formula, and calculating to obtain a power supply and demand value, wherein the power supply and demand value reflects the power supply and demand relation in the current period. And (3) formulating a dynamic power distribution scheme according to the calculated power supply and demand values, wherein the dynamic power distribution scheme relates to measures such as adjusting the capacity of a power production facility, optimizing the configuration of a power transmission network, adjusting the distribution of power loads and the like so as to ensure the power supply and demand balance and meet the power demands of all parts in the system as much as possible. By dynamically analyzing and optimizing the power supply and demand values, an optimal power distribution scheme is generated, and a plurality of factors including cost, system reliability, environmental influence and the like, and various constraint conditions such as equipment capacity, power supply area requirements and the like need to be considered.
Preferably, step S24 includes the steps of:
step S241: carrying out power supply and demand quantification on the power demand prediction data by utilizing a power supply and demand analysis formula to obtain a power supply and demand value; performing simulated power transmission on the power demand prediction data according to the power supply and demand value to generate simulated power transmission data; extracting transmission grid nodes from the simulated power transmission data to generate power transmission grid node data, wherein the power transmission grid node data comprises center power node data and edge power node data;
step S242: performing distributed computation on the edge power node data to generate distributed intelligent power transmission path data; local power distribution adjustment is carried out on the power transmission grid node data based on the distributed intelligent power transmission path data, and local power adjustment data are generated; transmitting the local power adjustment data to the central power node data through a preset communication protocol to perform power global scheduling to generate global circuit adjustment data;
step S243: performing power constraint condition analysis on the global circuit adjustment data to generate global power constraint condition data;
step S244: and dynamically distributing the power demand prediction data according to the global power constraint condition data and the local power adjustment data, so as to generate an optimal power distribution scheme.
The invention can more accurately know the power supply and demand conditions by quantizing the power demand prediction data and generating the simulated transmission data, and provides a reliable data basis for subsequent power allocation. The power transmission efficiency can be optimized by extracting the transmission grid node data and generating the distributed intelligent power transmission path data, and the reliability and stability of power transmission are improved. By performing distributed computation on the edge power node data, distributed intelligent power transmission path data is generated, and local power distribution adjustment is performed, so that power transmission is more flexible and efficient. By performing a global power schedule at the central power node, generating global circuit adjustment data, power distribution may be optimized throughout the power network. The power constraint condition analysis on the global circuit adjustment data is helpful for identifying and processing potential power constraint problems, and the feasibility and the safety of a power distribution scheme are ensured. According to the global power constraint condition data and the local power adjustment data, an optimal power distribution scheme can be generated to meet power requirements and improve efficiency and reliability of the power system to the greatest extent.
As an example of the present invention, referring to fig. 3, the step S24 in this example includes:
Step S241: carrying out power supply and demand quantification on the power demand prediction data by utilizing a power supply and demand analysis formula to obtain a power supply and demand value; performing simulated power transmission on the power demand prediction data according to the power supply and demand value to generate simulated power transmission data; extracting transmission grid nodes from the simulated power transmission data to generate power transmission grid node data, wherein the power transmission grid node data comprises center power node data and edge power node data;
in the embodiment of the invention, the power supply and demand analysis formula can be established by counting historical power demand data and combining current economic, social and environmental factors, wherein the formula comprises various factors such as seasonal change, weather conditions, industrial production activities and the like so as to quantify the power demand. And simulating power transmission of the power demand prediction data according to the power supply and demand value by using simulation software or a self-developed simulation tool, wherein the tool takes physical characteristics of power transmission, such as voltage loss, line capacity, load balance and the like, into consideration to generate simulated power transmission data. By modeling the power transmission data, nodes in the transmission grid, which may be power plants, substations, distribution stations or other important power facilities, may be identified and extracted. The extracted nodes can be divided into a central node, which is typically the core of the grid, and edge nodes, which are located at the periphery or edges of the grid. The method utilizes data processing and analysis technology to process the simulated power transmission data and extract node information, and relates to methods such as data cleaning, feature extraction, cluster analysis and the like so as to effectively extract the power transmission grid node data.
Step S242: performing distributed computation on the edge power node data to generate distributed intelligent power transmission path data; local power distribution adjustment is carried out on the power transmission grid node data based on the distributed intelligent power transmission path data, and local power adjustment data are generated; transmitting the local power adjustment data to the central power node data through a preset communication protocol to perform power global scheduling to generate global circuit adjustment data;
in the embodiment of the invention, the distributed computation is performed on the edge power node data, and a distributed computation framework such as Apache Hadoop or Spark is used for processing large-scale data and performing parallel computation. In this process, an intelligent algorithm, such as a genetic algorithm, a simulated annealing algorithm, or a deep learning model, may be used to determine the optimal power transmission path to minimize energy loss and improve the efficiency of the grid. Based on the generated distributed intelligent power transmission path data, local power distribution adjustment is carried out on the power transmission grid node data, local power distribution decision is carried out on each node, so that balanced distribution of power and stable operation of the power grid are ensured, and factors such as load conditions, line capacity, voltage stability and the like among the nodes need to be considered. The transmission of local power adjustment data to a central power node through a preset communication protocol involves the use of network communication techniques, such as message queues, socket communications, or HTTP protocols, to ensure safe and reliable transmission of the data. The global power dispatching is performed in the central power node, namely the received local power adjustment data are integrated and optimized to generate global circuit adjustment data, and the global circuit adjustment data relate to an optimization method such as linear programming, integer programming or a heuristic algorithm-based optimization method so as to maximize the overall efficiency and stability of the power grid.
Step S243: performing power constraint condition analysis on the global circuit adjustment data to generate global power constraint condition data;
in the embodiment of the present invention, the global circuit adjustment data obtained in step S242 is used for analysis of power constraint conditions, including information such as power transmission paths, node load conditions, line capacities, and the like. The analysis of the global circuit adjustment data for power constraints involves the inspection and evaluation of various constraints of the power system to ensure that the adjusted power distribution meets the safety, stability and reliability requirements of the system, including: node voltage limit: ensuring that the voltage of each node is within a safe range; line capacity limitation: ensuring that the current of each power line does not exceed its rated capacity; system stability: by considering factors such as power balance and tide direction among nodes, the system is ensured to be stable under various operation conditions; node load balancing: the load of each node is ensured to be in a reasonable range, and overload or unbalanced load is avoided. And generating global power constraint condition data according to the result of the power constraint condition analysis, wherein the global power constraint condition data describes various constraint conditions of the system under different operation conditions and specific limitation of each constraint condition, and the data needs to be arranged and recorded in a structured format for subsequent power dispatching and operation management.
Step S244: and dynamically distributing the power demand prediction data according to the global power constraint condition data and the local power adjustment data, so as to generate an optimal power distribution scheme.
In the embodiment of the invention, the power demand of each area or node in a future period is determined by preparing the prediction data of the power demand before dynamic power distribution and predicting based on the factors such as historical power consumption, seasonal change, weather conditions and the like. The required information is obtained from the global power constraint data and the local power adjustment data obtained in step S243, the global power constraint data describing various constraints of the system, and the local power adjustment data providing power adjustment information for a specific area or node. The design of a power distribution algorithm suitable for a dynamic environment needs to consider the continuous change of power requirements, the dynamic adjustment of system constraint conditions and the influence of local power adjustment, and common algorithms comprise dynamic power distribution based on load prediction, optimal scheduling based on an optimization algorithm and the like. According to the power demand prediction data, the global power constraint condition data and the local power adjustment data, an optimal power distribution scheme is generated by utilizing a designed dynamic power distribution algorithm, and the process involves reasonably distributing power resources to meet the demands of all areas or nodes on the premise of meeting the constraint condition of the system.
Preferably, the power supply and demand analysis formula in step S241 is specifically as follows:
in the method, in the process of the invention,expressed as +.>Power supply and demand value of->Expressed as time +.>Is>Expressed as time +.>Renewable energy supply of +.>Expressed as time +.>Voltage of>Expressed as time +.>Current of->Expressed as the response speed of the power system, +.>Represented as a power supply and demand analysis time range.
The invention analyzes and integrates an electric power supply and demand analysis formula, and the time in the formulaIs>Is the renewable energy supply of>When increased, this means increased power demand, and more is needed by the systemThe power supply is to meet the demand, thus +.>And will increase accordingly. At the same time, when->When the renewable energy source available in the system is increased, the power demand can be partially met, thereby reducing +.>For->Contribution of (2) such that->And (3) reducing. The voltage and current variations directly affect the energy transmission and supply capabilities of the system, and thus the balance of power supply and demand. In the formulaIt is understood that the power transfer efficiency of the system, when this value goes to minus infinity, the response of the system to the power demand becomes very fast, and when this value goes to plus infinity, the response of the system to the power demand becomes very slow. / >The smaller the response speed of the system, the faster the system can adjust and balance the power supply-demand relationship. On the contrary, let(s)>The larger the response speed of the system, the slower the system will respond, and the system will adjust the power demand relatively slowly. When the power supply and demand analysis formula conventional in the art is used, the time +.>By applying the power supply and demand analysis formula provided by the invention, the power supply and demand value in time can be calculated more accurately>Is a power supply and demand value of (a). The formula comprehensively considers a plurality of factors such as power demand, renewable energy supply, voltage and current, and the like, and can more comprehensively analyze the power supply and demand conditions. The balance between power supply and demand can be dynamically adjusted through integral terms in the formula, so that the system is more flexible and quick in response. +.>Is indicated at +.>By predicting and analyzing the power supply and demand values of the system, the power distribution scheme can be optimized, and the efficiency and stability of the system can be improved. The function among the parameters in the formula enables the system to adjust the power supply in real time, meets the power requirement, simultaneously maximally utilizes renewable energy sources, and is beneficial to reducing the energy cost and reducing the dependence on traditional energy sources.
Preferably, step S243 includes the steps of:
step S2431: performing transmission spectrum conversion on the global circuit adjustment data by using a fast Fourier transform method to generate a global circuit transmission spectrum diagram; carrying out frequency period analysis on the global circuit power transmission spectrogram to generate a power transmission frequency curve; extracting transmission time intervals of the power transmission frequency curve to obtain transmission time interval data;
step S2432: detecting the sequence number of a transmission data packet of the global circuit adjustment data according to the transmission time interval data, and generating power transmission data packet sequence data; performing network delay anomaly test according to the power transmission data packet sequence data, thereby generating power transmission delay anomaly data;
step S2433: comparing the power transmission delay abnormal data with a preset delay abnormal threshold, and marking the power transmission delay abnormal data as network communication fault data when the power transmission delay abnormal data is larger than or equal to the preset delay abnormal threshold; when the power transmission delay abnormal data is smaller than a preset delay abnormal threshold value, marking the power transmission delay abnormal data as network communication delay data;
step S2434: and integrating the network communication fault data and the network communication delay data to generate global power constraint condition data.
According to the invention, the frequency spectrum characteristics of the circuit can be more effectively analyzed by carrying out transmission frequency spectrum conversion on the global circuit adjustment data by utilizing the fast Fourier transform method, so that accurate transmission frequency curve data is provided for frequency period analysis of the power system. The transmission time interval data obtained by extracting the transmission time interval of the power transmission frequency curve can better reflect the time interval characteristics in the power transmission process, and provides a basis for subsequent data analysis. According to the power transmission data packet sequence data generated by the transmission time interval data, network delay abnormality test is carried out, so that delay abnormality in power transmission can be effectively detected, and potential communication faults or delay problems can be found timely. And comparing the power transmission delay abnormal data with a preset delay abnormal threshold value, and marking the data as network communication fault data or network communication delay data according to the result, thereby being beneficial to further analyzing and processing abnormal conditions. And integrating the network communication fault data and the network communication delay data to generate global power constraint condition data, and providing basic data support for subsequent power system adjustment and optimization.
As an example of the present invention, referring to fig. 4, the step S243 in this example includes:
step S2431: performing transmission spectrum conversion on the global circuit adjustment data by using a fast Fourier transform method to generate a global circuit transmission spectrum diagram; carrying out frequency period analysis on the global circuit power transmission spectrogram to generate a power transmission frequency curve; extracting transmission time intervals of the power transmission frequency curve to obtain transmission time interval data;
in an embodiment of the invention, the global circuit adjustment data is input into a Fast Fourier Transform (FFT) algorithm. And converting the time domain signal into a frequency domain signal by using an FFT algorithm to obtain a power transmission spectrogram of the global circuit. In the spectrogram, the frequency axis represents the intensities of different frequency components, and the amplitude axis represents the amplitudes of corresponding frequency components. And carrying out frequency period analysis on the power transmission spectrogram of the global circuit, and determining a transmission frequency curve of the circuit by searching main frequency components in the spectrogram. The main frequency component is typically a peak or significant energy concentration point in the frequency spectrum. According to the generated transmission frequency curve, the time interval between adjacent frequency components can be calculated, the time interval represents the time interval of data transmission in the power system, and the frequency and the speed of data transmission can be reflected.
Step S2432: detecting the sequence number of a transmission data packet of the global circuit adjustment data according to the transmission time interval data, and generating power transmission data packet sequence data; performing network delay anomaly test according to the power transmission data packet sequence data, thereby generating power transmission delay anomaly data;
in the embodiment of the invention, the transmission time interval between the data packets can be deduced according to the transmission time interval data. By analyzing the pattern of change of the transmission time interval data, the transmission time point of the data packet can be determined. Each data packet is assigned a sequence number according to the transmission time point, thereby generating power transmission data packet sequence data. And simulating the data transmission process of the power system by using the generated power transmission data packet sequence data. The time of transmission of each packet is monitored, as well as the time the packet arrives at the destination. The difference between the actual transmission time and the expected transmission time is analyzed to detect whether there is a network delay anomaly. The detection of the abnormality may be performed based on a threshold setting, a statistical method, machine learning, or the like.
Step S2433: comparing the power transmission delay abnormal data with a preset delay abnormal threshold, and marking the power transmission delay abnormal data as network communication fault data when the power transmission delay abnormal data is larger than or equal to the preset delay abnormal threshold; when the power transmission delay abnormal data is smaller than a preset delay abnormal threshold value, marking the power transmission delay abnormal data as network communication delay data;
In the embodiment of the invention, in the system design stage, a proper delay abnormal threshold is set according to the characteristics and requirements of the power system, and the threshold is determined according to the performance requirements, real-time requirements and other factors of the system. And comparing each generated power transmission delay abnormal data with a preset delay abnormal threshold value. Whether the delay abnormal data is greater than or equal to a threshold or less than the threshold is determined. If the power transmission delay abnormality data is greater than or equal to a preset delay abnormality threshold: the data is marked as network communication failure data. If the power transmission delay abnormality data is smaller than a preset delay abnormality threshold value: the data is marked as network communication delay data.
Step S2434: and integrating the network communication fault data and the network communication delay data to generate global power constraint condition data.
In the embodiment of the invention, the network communication fault data and the network communication delay data are collected. And integrating the two types of data into a unified data structure to ensure the consistency of the data format. To clearly identify and distinguish between network communication failure data and network communication delay data, an identification field or classification field may be added to the data structure. For example, a flag bit or a specific value may be used to indicate a fault and delay. And further processing the integrated data according to the power constraint condition and the requirement of the system to generate global power constraint condition data, wherein the global power constraint condition data comprises weighting, normalizing or other mathematical processing of different types of data so as to obtain a comprehensive power constraint condition.
Preferably, step S3 comprises the steps of:
step S31: deploying the grid edge servers based on the optimal power distribution scheme, and acquiring equipment information according to the grid edge servers to obtain edge equipment deployment information data;
step S32: according to the edge equipment deployment information data, actual power distribution data monitoring is carried out on the intelligent power grid, and real-time power distribution monitoring data are generated; performing data cleaning on the real-time power distribution monitoring data to generate real-time power distribution monitoring cleaning data;
step S33: performing power equipment control on the real-time power distribution monitoring cleaning data through an edge computing technology to generate power equipment control data; real-time power grid adjustment is carried out on the control data of the power equipment based on a feedback control theory, and power grid edge calculation control data are generated;
step S34: carrying out power grid transmission safety analysis on the power grid edge calculation control data to generate power grid transmission safety analysis data; and carrying out power control stability regulation and control on the real-time power distribution monitoring data according to the power grid transmission safety analysis data, and generating power transmission stability data.
According to the invention, the power grid edge server deployment is performed based on the optimal power distribution scheme, so that the optimal configuration of power resources can be realized, and the overall efficiency and performance of the power grid are improved. The generation and cleaning of the real-time power distribution monitoring data and the real-time control and regulation of the power equipment in the step S33 realize the real-time monitoring and regulation capacity of the intelligent power grid, and are beneficial to guaranteeing the stable operation of the power grid. The edge computing technology is used for controlling the power equipment (step S33), so that the data transmission delay can be reduced, the response speed can be improved, meanwhile, the burden of a central server can be reduced, and the overall performance and efficiency of the system can be improved. And safety analysis is carried out on the power grid edge calculation control data, so that potential safety hazards can be found timely, and corresponding measures are taken to deal with the potential safety hazards. And (3) performing power control stability regulation according to the analysis data (step S34), so as to be beneficial to ensuring the safety and stability of the operation of the power grid. Through the organic combination of the steps S31 to S34, the intelligent management and regulation of the power resources are realized, the utilization efficiency of the power resources can be improved, and the running cost of the system is reduced, so that the purposes of energy conservation and emission reduction are achieved. By adopting a real-time monitoring and regulating mechanism, the system can more flexibly cope with the change of the external environment and the state change of the internal equipment, and the response speed and the flexibility of the system are improved.
In the embodiment of the invention, the optimal power distribution scheme is determined through power grid planning and data analysis, and the server is deployed at the edge of the power grid according to the scheme, so that the purchasing and arrangement of server hardware are involved to ensure that the coverage range and the performance meet the requirements. After the edge server deployment is completed, software or sensors for collecting equipment information, including smart meters, sensors and the like, are required to be installed and configured on the server, and the equipment is used for collecting data related to power grid operation, such as current, voltage, load and the like. The collected equipment information data is monitored in real time through software or application programs installed on the edge server, and data transmission and processing are involved so as to ensure timeliness and accuracy of the data. The monitored data may have noise, outliers, etc. and need to be cleaned and processed to ensure data quality. Cleaning data involves techniques such as anomaly detection, data filtering, and the like. The edge computing technology is utilized to process and analyze the real-time monitoring data so as to reduce data transmission delay and network load, including data compression, local data processing and other operations. Based on the processed data, the power equipment is controlled and regulated to realize the optimization and stability of power distribution, and the design and realization of a control algorithm and the communication and interaction with the power equipment are involved. Security analysis is performed on the edge computing control data to identify potential security threats and vulnerabilities, including data encryption, access control, and other techniques. And carrying out power control stability regulation and control on the real-time power distribution monitoring data according to the safety analysis result so as to ensure the stability and reliability of power transmission, and relating to the model and simulation of a power system and the optimization and adjustment of control parameters.
Preferably, step S34 includes the steps of:
step S341: extracting response time of the power grid edge calculation control data to obtain power transmission response time data; performing abnormal log capturing on the grid edge server according to the power transmission response time data to generate paralysis analysis data of the power transmission system;
step S342: carrying out data access log extraction on the grid edge calculation control data to obtain power transmission data access behavior data; according to the power transmission access behavior data, performing abnormal flow capture on the grid edge server to generate power transmission information leakage analysis data;
step S343: calculating and controlling the extraction of the data model power system parameters of the power grid edge to obtain power transmission system data, wherein the power transmission system data comprise power transmission current data and power transmission voltage data; according to the power transmission current data and the power transmission voltage data, capturing abnormal equipment loads of the grid edge server, and generating power transmission stability analysis data;
step S344: carrying out data time sequence combination on the power transmission system paralysis analysis data, the power transmission information leakage analysis data and the power transmission stability analysis data to generate power grid transmission safety analysis data; and carrying out power control stability regulation and control on the real-time power distribution monitoring data according to the power grid transmission safety analysis data, and generating power transmission stability data.
By monitoring the response time of the power transmission, the system can timely detect potential problems such as delay or abnormality of the response time, so that fault diagnosis and treatment can be performed more rapidly, the stability and usability of the power transmission system can be improved, and the risk of system paralysis can be reduced. The capture of the anomaly log helps to analyze and track the cause of system paralysis, and prevent potential problems in advance. Monitoring access behavior of power transfer data may help detect abnormal data traffic, including unusual access patterns or a large number of access requests. By capturing abnormal traffic, the system can quickly identify potential security threats, such as malicious attacks or unauthorized data access, which helps to improve the security of the power transmission system. Extracting parameters of the power transmission system, such as current and voltage data, may be used to monitor the operating state and performance of the device. By capturing the abnormal load of the equipment, the system can timely react when the equipment has a problem, prevent the equipment from being overloaded or invalid and improve the stability of the power transmission system. Combining different types of analysis data in time sequence can provide a more comprehensive analysis of the transmission safety of the power grid. The comprehensive analysis result is helpful for the system to more comprehensively understand the safety condition of the power transmission, so that corresponding measures are taken to optimize the power control stability. By means of power control stability regulation and control of the real-time power distribution monitoring data, the system can achieve dynamic adjustment of power transmission, and response capacity of the system is improved. Generating stability data helps ensure the stability of the power transmission system under different loads and conditions, providing more reliable power services.
In the embodiment of the invention, the response time of the power grid edge calculation control data is monitored in real time by using a special monitoring tool or software. The logging system is configured to capture an anomaly log of the grid edge servers and to build an automated program to analyze the log data. The data processing flow is designed to ensure that the process from extracting response time data to exception log capture is automated and efficient. A network monitoring tool is used to monitor the data access behavior of grid edge servers and record access logs. A traffic analysis system is deployed for detecting abnormal traffic patterns and unusual data access behavior. A real-time monitoring system is established so as to discover abnormal traffic in time and implement corresponding response measures, such as preventing malicious traffic or alerting related personnel. And the sensor and the monitoring equipment are configured and used for monitoring parameters such as current and voltage of the grid edge server in real time. And constructing a data acquisition and processing system for extracting and analyzing the parameter data of the power system and monitoring the working state of the equipment in real time. An abnormal load detection algorithm is developed or employed for identifying abnormal load conditions of the device, such as overload or abnormal voltage. The deployment data integration and analysis platform is used for carrying out time sequence combination and comprehensive analysis on analysis data of different sources. And designing a power control system and formulating a corresponding regulation strategy to ensure the stability and safety of the power transmission system. And a real-time monitoring and feedback mechanism is established, so that the system can quickly respond to the change of the power transmission system and perform corresponding regulation and control.
Preferably, step S4 comprises the steps of:
step S41: performing three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; performing self-adaptive control system design based on the power transmission three-dimensional model to generate power grid transmission control design data;
step S42: carrying out nonlinear control on the power transmission control design data of the power grid through a fuzzy control theory to generate fuzzy controller data of the power grid; performing reinforcement learning on the power grid fuzzy controller data to generate power grid transmission intelligent control data; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
According to the invention, the three-dimensional point cloud conversion is carried out on the power transmission stable data, so that a three-dimensional model of the power transmission system can be generated, the model can provide more visual and comprehensive power grid structure information, and the topology structure and layout characteristics of the power transmission system can be better understood. The self-adaptive control system design is carried out based on the power transmission three-dimensional model, so that a control system with flexibility and adaptability is designed according to the actual condition of the power grid, the stability and safety of the power transmission system can be improved, and the energy utilization efficiency is optimized. The nonlinear control is carried out on the power transmission control design data of the power grid through the fuzzy control theory, complex and fuzzy environment and uncertainty factors existing in the power transmission system can be dealt with, the control method can be better adapted to the change and fluctuation in the power grid operation process, and the stability and the robustness of the system are improved. The data of the power grid fuzzy controller is subjected to reinforcement learning, and the control strategy can be optimized through continuous trial and error and learning, so that the system can make more intelligent and effective decisions when facing different conditions, and the energy utilization efficiency and the system response speed in the power grid transmission process can be improved. The intelligent control data of the power grid transmission is subjected to data visualization, and a control report of the intelligent power grid transmission data is generated, so that operation and maintenance personnel and decision makers can be helped to more intuitively know the operation state and control effect of the power transmission system, timely finding of problems and optimization of operation strategies are facilitated, and scientific basis is provided for decision making.
In the embodiment of the invention, the power transmission stability data is acquired by using a suitable sensor (such as a laser radar). And performing three-dimensional point cloud conversion on the acquired data by using a point cloud processing algorithm, and ensuring the accuracy and the integrity of the point cloud so as to generate a reliable three-dimensional power transmission model. Based on the generated power transmission three-dimensional model, an adaptive control system is designed, and knowledge in the fields of control theory, system dynamics and the like is related. And determining input, output and feedback loops of the control system, and generating power grid transmission control design data, including information such as controller parameters and feedback strategies, by considering characteristics of the power transmission system, including load, line loss, voltage stability and the like. And processing power transmission control design data of the power grid by using a fuzzy control theory, determining a fuzzy rule and a membership function in consideration of nonlinearity and fuzziness existing in the power system, and establishing a power grid fuzzy controller, so that the operation characteristics of the power system are required to be deeply analyzed. And (3) optimizing the power grid fuzzy controller data by using a reinforcement learning algorithm, such as deep reinforcement learning (Deep Reinforcement Learning), defining a reward function, training the intelligent controller to adapt to the dynamic environment of the power system, repeatedly learning and optimizing to improve the performance of the system in different scenes, and performing data visualization on the power grid transmission intelligent control data after reinforcement learning optimization. And displaying the information such as the running state, the control effect and the like of the power transmission system by using a proper visual tool, and generating a smart grid power transmission data control report including key indexes, optimization results and the like for subsequent analysis and decision.
In this specification, there is provided a power transmission data control system of a smart grid for executing the above-described power transmission data control method of a smart grid, the power transmission data control system of a smart grid including:
the load analysis module is used for acquiring real-time power data and intelligent power grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
the power distribution module is used for extracting load peak value data of the real-time comprehensive energy load curve based on the preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
the stable regulation and control module is used for acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
The linear control module is used for carrying out three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
The method has the beneficial effects that the comprehensive power environment data set is generated by fusing the real-time power data and the intelligent power grid environment data, so that the integrity and the accuracy of the data are improved. And through dynamic load curve conversion, a comprehensive energy load curve is generated in real time, and a foundation is provided for the subsequent power demand prediction and optimization. Through model training, a power demand prediction model is generated, so that future power demands can be predicted, and basis is provided for power distribution. Based on the power demand prediction model, dynamic power distribution is realized, an optimal power distribution scheme is generated, energy utilization efficiency is improved, and cost caused by unbalanced supply and demand is reduced. And according to the optimal power distribution scheme, real-time power grid adjustment is performed, and optimal management of the intelligent power grid is realized through the deployment information data of the edge equipment. The stability and the reliability of the power grid are improved, and the energy waste and the energy loss are reduced through the generation and the regulation and the control of the power grid edge calculation control data. And generating a power transmission three-dimensional model through three-dimensional point cloud conversion, and providing a visual basis for nonlinear control of the power grid. Based on the power transmission three-dimensional model, nonlinear control and intelligent management of the power grid are realized, and the running efficiency and safety of the power grid are improved. Through visualization of the intelligent control data of the power grid power transmission, a control report of the intelligent power grid power transmission data is generated, and decision support and management reference are provided for a power management department. Therefore, the intelligent power grid system and the intelligent power grid system improve the accuracy, stability and reliability of the intelligent power grid through comprehensive data processing, dynamic load curve conversion, load peak value data extraction and prediction models, equipment information acquisition and power grid adjustment, power grid nonlinear control and data visualization.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The power transmission data control method of the smart grid is characterized by comprising the following steps of:
step S1: acquiring real-time power data and smart grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
Step S2: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
step S3: acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
step S4: performing three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
2. The power transmission data control method of a smart grid according to claim 1, wherein step S1 includes the steps of:
Step S11: acquiring real-time power data and smart grid environment data by using a sensor;
step S12: carrying out data format standardization on the real-time power data and the intelligent power grid environment data to generate real-time power standard data and intelligent power grid standard environment data; filling data missing values of the real-time power standard data and the smart grid standard environment data to generate real-time power complete data and smart grid complete environment data;
step S13: carrying out data fusion on the real-time electric power complete data and the intelligent power grid complete environment data to generate a comprehensive electric power environment data set; carrying out power grid operation dynamic load data screening on the comprehensive power environment data set to generate a comprehensive power dynamic load data set;
step S14: performing data dimension reduction on the comprehensive power dynamic load data set by a principal component analysis method to generate a comprehensive power dynamic load dimension reduction data set; and performing curve conversion on the comprehensive power dynamic load dimension reduction data set so as to generate a real-time comprehensive energy load curve.
3. The power transmission data control method of a smart grid according to claim 1, wherein step S2 includes the steps of:
step S21: carrying out load peak value data extraction on the real-time comprehensive energy load curve based on a preset peak value screening quantity to obtain dynamic power load peak value data; historical data collection is carried out on the dynamic power load peak value data, and historical dynamic power load peak value data are generated;
Step S22: carrying out data set division on historical dynamic power load peak data to generate a model training set and a model testing set; model training is carried out on the model training set according to a long-term and short-term memory network algorithm, and a power demand training model is generated; performing model optimization iteration on the power demand training model by using a model test set so as to generate a power demand prediction model;
step S23: the dynamic power load peak value data is imported into a power demand prediction model to predict power demand, and power demand prediction data is generated;
step S24: carrying out power supply and demand quantification on the power demand prediction data by utilizing a power supply and demand analysis formula to obtain a power supply and demand value; and dynamically distributing the power demand prediction data according to the power supply and demand value, so as to generate an optimal power distribution scheme.
4. A power transmission data control method of a smart grid according to claim 3, wherein step S24 includes the steps of:
step S241: carrying out power supply and demand quantification on the power demand prediction data by utilizing a power supply and demand analysis formula to obtain a power supply and demand value; performing simulated power transmission on the power demand prediction data according to the power supply and demand value to generate simulated power transmission data; extracting transmission grid nodes from the simulated power transmission data to generate power transmission grid node data, wherein the power transmission grid node data comprises center power node data and edge power node data;
Step S242: performing distributed computation on the edge power node data to generate distributed intelligent power transmission path data; local power distribution adjustment is carried out on the power transmission grid node data based on the distributed intelligent power transmission path data, and local power adjustment data are generated; transmitting the local power adjustment data to the central power node data through a preset communication protocol to perform power global scheduling to generate global circuit adjustment data;
step S243: performing power constraint condition analysis on the global circuit adjustment data to generate global power constraint condition data;
step S244: and dynamically distributing the power demand prediction data according to the global power constraint condition data and the local power adjustment data, so as to generate an optimal power distribution scheme.
5. The power transmission data control method of a smart grid according to claim 4, wherein the power supply and demand analysis formula in step S241 is as follows:
，
in the method, in the process of the invention,expressed as +.>Power supply and demand value of->Expressed as time +.>Is>Expressed as time +.>Renewable energy supply of +.>Expressed as time +.>Voltage of>Expressed as time +. >Current of->Expressed as the response speed of the power system, +.>Represented as a power supply and demand analysis time range.
6. The power transmission data control method of a smart grid as claimed in claim 4, wherein the step S243 includes the steps of:
step S2431: performing transmission spectrum conversion on the global circuit adjustment data by using a fast Fourier transform method to generate a global circuit transmission spectrum diagram; carrying out frequency period analysis on the global circuit power transmission spectrogram to generate a power transmission frequency curve; extracting transmission time intervals of the power transmission frequency curve to obtain transmission time interval data;
step S2432: detecting the sequence number of a transmission data packet of the global circuit adjustment data according to the transmission time interval data, and generating power transmission data packet sequence data; performing network delay anomaly test according to the power transmission data packet sequence data, thereby generating power transmission delay anomaly data;
step S2433: comparing the power transmission delay abnormal data with a preset delay abnormal threshold, and marking the power transmission delay abnormal data as network communication fault data when the power transmission delay abnormal data is larger than or equal to the preset delay abnormal threshold; when the power transmission delay abnormal data is smaller than a preset delay abnormal threshold value, marking the power transmission delay abnormal data as network communication delay data;
Step S2434: and integrating the network communication fault data and the network communication delay data to generate global power constraint condition data.
7. The power transmission data control method of a smart grid according to claim 1, wherein step S3 includes the steps of:
step S31: deploying the grid edge servers based on the optimal power distribution scheme, and acquiring equipment information according to the grid edge servers to obtain edge equipment deployment information data;
step S32: according to the edge equipment deployment information data, actual power distribution data monitoring is carried out on the intelligent power grid, and real-time power distribution monitoring data are generated; performing data cleaning on the real-time power distribution monitoring data to generate real-time power distribution monitoring cleaning data;
step S33: performing power equipment control on the real-time power distribution monitoring cleaning data through an edge computing technology to generate power equipment control data; real-time power grid adjustment is carried out on the control data of the power equipment based on a feedback control theory, and power grid edge calculation control data are generated;
step S34: carrying out power grid transmission safety analysis on the power grid edge calculation control data to generate power grid transmission safety analysis data; and carrying out power control stability regulation and control on the real-time power distribution monitoring data according to the power grid transmission safety analysis data, and generating power transmission stability data.
8. The power transmission data control method of a smart grid according to claim 7, wherein step S34 includes the steps of:
step S341: extracting response time of the power grid edge calculation control data to obtain power transmission response time data; performing abnormal log capturing on the grid edge server according to the power transmission response time data to generate paralysis analysis data of the power transmission system;
step S342: carrying out data access log extraction on the grid edge calculation control data to obtain power transmission data access behavior data; according to the power transmission access behavior data, performing abnormal flow capture on the grid edge server to generate power transmission information leakage analysis data;
step S343: calculating and controlling the extraction of the data model power system parameters of the power grid edge to obtain power transmission system data, wherein the power transmission system data comprise power transmission current data and power transmission voltage data; according to the power transmission current data and the power transmission voltage data, capturing abnormal equipment loads of the grid edge server, and generating power transmission stability analysis data;
step S344: carrying out data time sequence combination on the power transmission system paralysis analysis data, the power transmission information leakage analysis data and the power transmission stability analysis data to generate power grid transmission safety analysis data; and carrying out power control stability regulation and control on the real-time power distribution monitoring data according to the power grid transmission safety analysis data, and generating power transmission stability data.
9. The power transmission data control method of a smart grid according to claim 1, wherein step S4 includes the steps of:
step S41: performing three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; performing self-adaptive control system design based on the power transmission three-dimensional model to generate power grid transmission control design data;
step S42: carrying out nonlinear control on the power transmission control design data of the power grid through a fuzzy control theory to generate fuzzy controller data of the power grid; performing reinforcement learning on the power grid fuzzy controller data to generate power grid transmission intelligent control data; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
10. A power transmission data control system of a smart grid for performing the power transmission data control method of a smart grid as set forth in claim 1, the power transmission data control system of a smart grid comprising:
the load analysis module is used for acquiring real-time power data and intelligent power grid environment data; carrying out data fusion on the real-time power data and the intelligent power grid environment data to generate a comprehensive power environment data set; performing dynamic load curve conversion on the comprehensive power environment data set so as to generate a real-time comprehensive energy load curve;
The power distribution module is used for extracting load peak value data of the real-time comprehensive energy load curve based on the preset peak value screening quantity to obtain dynamic power load peak value data; model training is carried out on the dynamic power load peak value data, and a power demand prediction model is generated; dynamic power distribution is carried out on the dynamic power load peak value data through a power demand prediction model, so that an optimal power distribution scheme is generated;
the stable regulation and control module is used for acquiring equipment information based on an optimal power distribution scheme to obtain edge equipment deployment information data; carrying out real-time power grid adjustment on the intelligent power grid according to the edge equipment deployment information data to generate power grid edge calculation control data; performing power control stability regulation and control on the power grid edge calculation control data to generate power transmission stability data;
the linear control module is used for carrying out three-dimensional point cloud conversion on the power transmission stable data to generate a power transmission three-dimensional model; carrying out nonlinear control on the power grid based on the power transmission three-dimensional model, and generating intelligent control data of power transmission of the power grid; and carrying out data visualization on the intelligent control data of the power grid power transmission, thereby generating a control report of the intelligent power grid power transmission data.
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