CN116308883A - Regional power grid data overall management system based on big data - Google Patents
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Abstract
The invention discloses a regional power grid data overall management system based on big data, which belongs to the technical field of power grid management and comprises a data acquisition unit, a data processing unit and a data processing unit, wherein the data acquisition unit is used for acquiring data generated by a power transmission and transformation side, data generated by a power generation side and data generated by a power utilization side in the big data; the data processing unit is used for processing the data acquired from the large database and then carrying out normalization processing; the model processing unit is used for pre-establishing a plurality of data models constructed by utilizing a data mining algorithm, classifying the normalized data and training the data models; and the overall management unit is used for overall management of the training results of the model processing module on the power transmission and transformation side, the power generation side and the power utilization side of the power grid, the database is comprehensive, a plurality of data models are trained, the calculation efficiency of each model is high, the prediction result is accurate, and the supervision efficiency is improved.
Description
Technical Field
The invention relates to the technical field of power grid management, in particular to a regional power grid data overall management system based on big data.
Background
With the continuous improvement of the voltage level of a modern power system, the continuous expansion of the power grid capacity and the continuous deep informatization and intellectualization of the power system, in a local power grid, the variety of on-line monitoring, intelligent control protection and load management systems of power equipment is increased, the method is advanced, the acquired data volume is huge in long-term and continuous monitoring and control protection, the acquired data variety is increased based on the diversification of the method, such as video, image and the like, the speed requirement of the dynamic load management system of the local power grid on data processing is also continuously improved, and the traditional method has the problems of single information acquisition, poor system reliability and the like, so that how to synthesize various data information is realized, the rapid and effective analysis of data becomes one of important researches of the dynamic load management system of the local power grid, and the large data processing method provides a new idea and method for solving the problem.
Big data refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth-rate and diversified information asset which needs a new processing mode to have stronger decision-making ability, insight discovery ability and flow optimization ability. In a conventional regional power grid, data of load management, energy scheduling, on-line monitoring, supervision and the like are usually independent and are processed by independent systems, so that overall management of various data in the power grid system is inconvenient on a macroscopic scale, and management efficiency of the power grid system is reduced.
Disclosure of Invention
The invention provides a regional power grid data overall management system based on big data, which aims to solve the problems that the independent system provided in the background art is used for processing, so that various data in a power grid system are inconvenient to overall manage on a macroscopic scale, and the management efficiency of the power grid system is reduced.
The invention provides a regional power grid data overall management system based on big data, which adopts the following technical scheme: comprising
The data acquisition unit is used for acquiring data generated by the power transmission and transformation side, data generated by the power generation side and data generated by the power utilization side from the big data;
the data processing unit is used for processing the data acquired from the large database and then carrying out normalization processing;
the model processing unit is used for pre-establishing a plurality of data models constructed by utilizing a data mining algorithm, classifying the normalized data and training the data models;
and the overall management unit is used for overall management of the power transmission and transformation side, the power generation side and the power utilization side of the power grid by training results of the model processing module.
Optionally, the data generated by the power transmission and transformation side, the data generated by the power generation side and the data generated by the power utilization side are selected on a GIS power grid data layer of a national grid GIS map in an ERP system to perform data screening in a designated area needing power grid data analysis.
Optionally, the generating step of the GIS power grid data layer includes:
s1: collecting power grid data information, and dividing the power grid data information into three forms of point elements, line elements and surface elements according to the characteristics of the power grid data of each region;
s2: constructing a data layer according to the type of the power grid data in the step S1, and classifying the data layer;
s3: storing the data layer data constructed in the step S2 to a database server;
s4: reading data of the database server in the step S3, and generating a GIS power grid data layer according to the read data of the database server in the method of the step S1;
s5: and displaying the GIS power grid data layer in the step S4 through a GIS platform.
Optionally, the data processing unit processes noise, missing value and inconsistent data of data collected by the large database, and the normalization process is that the processed data X is substituted into the following formula to calculate:
wherein Y represents the processing result, X is the processed data, X min Is the minimum value in the processed data，X max Is the maximum value in the processed data.
Optionally, the plurality of data models includes
The fault diagnosis analysis model is used for detecting abnormal data in time in the system operation stage and predicting the operation condition of the unit through analysis of the abnormal data;
the power load prediction model is used for obtaining the relation between the power demand and the power demand according to the historical data of the load and the power consumption and under the condition of combining external environment factors, so as to obtain a predicted final result;
the power grid framework planning model is used for optimizing a power grid structure in the aspects of power quality, safety and power loss of the distributed low-energy power supply according to the characteristics of the power grid;
the power stealing and losing analysis model is used for analyzing a series of monitoring system data of environmental monitoring, industrial control network and monitoring camera shooting in local power grid big data in real time, preprocessing attack traces of attackers hidden in the big data through integrating calculation and processing resource analysis, and then modeling a neural network by utilizing multiple data mining exercises to construct the power stealing and losing analysis model.
Optionally, the fault diagnosis analysis model combines a rayleigh entropy, a wavelet packet decomposition method and a Teager energy operator, extracts monitoring equipment state monitoring data in a power grid system and sets the monitoring equipment state monitoring data as a fault feature vector, and identifies the fault type of the monitoring equipment by using a probabilistic neural network algorithm.
Optionally, the power load prediction model is constructed on a Hadoop platform to realize the K-Means clustering algorithm on a parallel algorithm based on a MapRe-facility framework.
Optionally, the power grid architecture planning model performs statistics on the data of the area power consumption, the load value and the electricity loss value of the data of the designated area, and compares the data with the past historical data value; according to the analysis of each item of data of the power grid in the appointed area, the change of each item of data value is counted, and a solution is made according to the change of each item of data value.
In summary, the present invention includes at least one of the following beneficial effects:
the invention uniformly collects the data generated by the power transmission and transformation side, the data generated by the power generation side and the data generated by the power utilization side of the power grid based on big data through the data collection unit, the data are more collected and comprehensive, then the data are processed through the data processing unit and the model processing unit and then trained with a plurality of data models, each model has high calculation efficiency and accurate prediction result, and finally the data of the local power grid can be intuitively displayed through the overall management unit so as to improve the supervision efficiency.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of a system of the present invention;
FIG. 2 is a flow chart of the generation of a GIS grid data layer according to the present invention;
FIG. 3 is a diagram of a number of data models built in advance in a model processing unit of the present invention using a data mining algorithm.
Detailed Description
The invention is described in further detail below with reference to fig. 1-3.
Examples:
referring to fig. 1, the invention discloses a regional power grid data overall management system based on big data, which comprises:
the data acquisition unit is used for acquiring data generated by the power transmission and transformation side, data generated by the power generation side and data generated by the power utilization side from big data, wherein the data comprises a plurality of power generation side information, user side information, equipment detection information, external environment monitoring information and the like;
in an ERP system, selecting a designated area needing to be subjected to power grid data analysis on a GIS power grid data layer of a national grid GIS map for data screening,
the GIS system is a database of a power grid planning platform, provides geographical information and power grid information for a power grid, supports the functions of load prediction, point setting and volume fixing, influence analysis, effect evaluation and the like of power grid planning, and performs statistics on main transformer capacity, power supply range and influence on the periphery according to regions; the system has a monitoring function on the formulated plan, and can know the implementation result and influence of the plan at any time.
Referring to fig. 2, the steps for generating the gis power grid data layer are as follows:
s1: the type of the power grid data is collected, and the power grid data is displayed in three forms of point elements, line elements and surface elements according to the characteristics of the power grid data of each region;
s2: according to the type of the power grid data in the step S1, including different power grid data layer types of a power plant, a transformer substation and a circuit, constructing a data layer, and classifying the data layer;
s3: storing the data layer data constructed in the step S2 to a database server;
s4: reading data of the database server in the step S3, and generating a GIS power grid data layer according to the read data of the database server in the method of the step S1;
s5: and displaying the GIS power grid data layer in the step S4 through a GIS platform.
And the data processing unit is used for processing noise, missing values and inconsistent data of the data acquired by the large database, for example, the temperature of a certain moment point of the transformer in the transformer is 500 ℃, the temperature value is obviously not a normal temperature value, and the data is judged to be noise for removal. When processing data noise, the temperature value of a transformer may be deleted, so that partial data is empty, and at this time, missing data needs to be filled by interpolation or median average, and inconsistent data is classified.
The normalization processing is that the processed data X is substituted into the following formula to calculate:
wherein Y represents the processing result, X is the processed data, X min X is the minimum value in the processed data max Is the maximum value in the processed data.
Substituting the data into the formula (1) to perform standard normalization processing on the data, thereby reducing the calculation amount.
The model processing unit is used for pre-establishing a plurality of data models constructed by utilizing a data mining algorithm, classifying the normalized data, and training the data models;
referring to FIG. 3, several data models include
The fault diagnosis analysis model is used for detecting abnormal data in time in the system operation stage and predicting the operation condition of the unit through analysis of the abnormal data;
under normal conditions, the time complexity of the frequency component in the signal transient state is accurately represented by using the Rayleigh wavelet packet singular entropy, the instantaneous characteristic of the Teager energy operator is matched, the energy of the instantaneous signal is calculated by using the Teager energy operator, the fault diagnosis of the power equipment in the power system can be well completed, for example, 4 common fault signals of a power transformer are simulated, a db5 mode wavelet transformation form is adopted to decompose a target fault signal, the layering level is 3 layers, during simulation, the width of a sliding window is set to be 50, the corresponding coefficient is set to be 1, the wavelet packet singular entropy is calculated for a wavelet packet decomposition system of the window, the reconstruction system TEO under lower frequency is obtained, the wavelet packet singular entropy and the TEO are regarded as fault characteristic signals of the power equipment, the monitoring equipment state monitoring data in the power grid system are extracted and set as fault characteristic vectors, and the fault type of the monitoring equipment is identified by using a probability neural network algorithm.
The change of the power load can be influenced by a plurality of external factors, so that the power load is predicted by combining the actual situation of the current area, the power demand data amount is usually larger, valuable data such as the historical data and the power consumption of the load are moved in massive data by utilizing a big data technology, the relation between the power demand and the power demand is obtained by combining the situation of the external environment factors, the final predicted result is obtained, the power load prediction method is continuously appeared for many years, such as a time sequence method, a trend extrapolation method, a neural network, a linear regression method, a wavelet analysis method and the like, but the methods have limitations, the neural network method is difficult to avoid the defects of insufficient learning and slow convergence in the training process, the time sequence method has high requirements on the accuracy of the historical data, the short-term power load prediction caused by factors such as weather conditions and areas are difficult to solve, the problem of inaccurate short-term load prediction caused by the factors such as weather conditions and the area is difficult to be found by researching the traditional K-Means of a clustering algorithm, the algorithm can be highly accurately predicted when facing a small amount of data, the data is very low in the situation, and the distance is required to be greatly influenced by the data cluster, so that the distance is required to be greatly influenced by the data. In summary, aiming at the continuous increase of the current power load data quantity, the processing process of a large amount of data is low in efficiency, the consumed time is too long, the power load prediction precision is low, the obtained result cannot be well calculated and stored, the power load prediction model is realized on a parallel algorithm based on a MapRe-reduce framework by constructing a cluster on a Hadoop platform, the data processing of the cluster can solve the problem of the current mass power load data, and the precision of the proposed parallel algorithm can also meet the load prediction requirement.
The power grid framework planning model is used for optimizing the power grid structure according to the characteristics of a power grid in terms of the power quality, the safety and the grid loss of a distributed low-energy power supply, so that the reliability and the safety of the power grid structure are greatly improved;
for example, the data of the designated area is subjected to data value statistics of the area power consumption, the load value and the power loss value, and is compared with the past historical data value; according to the analysis of each item of data of the power grid in the appointed area, the change of each item of data value is counted, and a solution is made according to the change of each item of data value.
The power stealing and losing analysis model is used for analyzing a series of monitoring system data of environmental monitoring, industrial control network and monitoring camera shooting in local power grid big data in real time, preprocessing attack traces of attackers hidden in the big data through integrating calculation and processing resource analysis, and then modeling a neural network by utilizing multiple data mining exercises to construct the power stealing and losing analysis model. Through analyzing actual electric power user true practical electric power data, adopting big data mining technology to realize anti-electricity-theft recognition analysis, finding the suspected electricity-theft users, reducing the number of the suspected electricity-theft users and the detection range of electricity-theft, improving the work efficiency of anti-electricity-theft, and based on the established electricity-theft feedback data analysis model, the suspected electricity-theft users can be determined, and the follow-up detection is convenient to determine the actual electricity-theft users from the suspected users.
And the overall management unit is used for overall management of the power transmission and transformation side, the power generation side and the power utilization side of the power grid by training results of the model processing module. The fault diagnosis analysis model, the power load prediction model, the power grid architecture planning model and the power stealing and losing analysis model are comprehensively managed, and data of a local power grid are macroscopically managed, so that the data can be intuitively displayed, and the supervision efficiency is improved.
The above embodiments are not intended to limit the scope of the present invention, so: all equivalent changes in structure, shape and principle of the invention should be covered in the scope of protection of the invention.
Claims (8)
1. Regional power grid data overall management system based on big data is characterized in that: comprising the following steps:
the data acquisition unit is used for acquiring data generated by the power transmission and transformation side, data generated by the power generation side and data generated by the power utilization side from the big data;
the data processing unit is used for processing the data acquired from the large database and then carrying out normalization processing;
the model processing unit is used for pre-establishing a plurality of data models constructed by utilizing a data mining algorithm, classifying the normalized data and training the data models;
and the overall management unit is used for overall management of the power transmission and transformation side, the power generation side and the power utilization side of the power grid by training results of the model processing module.
2. The regional power grid data overall management system based on big data as claimed in claim 1, wherein: and selecting a designated area needing to be subjected to power grid data analysis on a GIS power grid data layer of a national grid GIS map in an ERP system by the data generated by the power transmission and transformation side, the data generated by the power generation side and the data generated by the power utilization side.
3. The regional power grid data overall management system based on big data as claimed in claim 2, wherein: the generation steps of the GIS power grid data layer are as follows:
s1: collecting power grid data information, and dividing the power grid data information into three forms of point elements, line elements and surface elements according to the characteristics of the power grid data of each region;
s2: constructing a data layer according to the type of the power grid data in the step S1, and classifying the data layer;
s3: storing the data layer data constructed in the step S2 to a database server;
s4: reading data of the database server in the step S3, and generating a GIS power grid data layer according to the read data of the database server in the method of the step S1;
s5: and displaying the GIS power grid data layer in the step S4 through a GIS platform.
4. The regional power grid data overall management system based on big data as claimed in claim 1, wherein: the data processing unit processes noise, missing values and inconsistent data of data acquired by the large database, and the normalization processing is that the processed data X is substituted into the following formula to calculate:
wherein Y represents the processing result, X is the processed data, X min X is the minimum value in the processed data max Is the maximum value in the processed data.
5. The regional power grid data overall management system based on big data as claimed in claim 1, wherein: the plurality of data models comprises
The fault diagnosis analysis model is used for detecting abnormal data in time in the system operation stage and predicting the operation condition of the unit through analysis of the abnormal data;
the power load prediction model is used for obtaining the relation between the power demand and the power demand according to the historical data of the load and the power consumption and under the condition of combining external environment factors, so as to obtain a predicted final result;
the power grid framework planning model is used for optimizing a power grid structure in the aspects of power quality, safety and power loss of the distributed low-energy power supply according to the characteristics of the power grid;
the power stealing and losing analysis model is used for analyzing a series of monitoring system data of environmental monitoring, industrial control network and monitoring camera shooting in local power grid big data in real time, preprocessing attack traces of attackers hidden in the big data through integrating calculation and processing resource analysis, and then modeling a neural network by utilizing multiple data mining exercises to construct the power stealing and losing analysis model.
6. The regional power grid data overall management system based on big data according to claim 5, wherein: the fault diagnosis analysis model combines the Ruili entropy, the wavelet packet decomposition method and the Teager energy operator, extracts monitoring equipment state monitoring data in the power grid system and sets the monitoring equipment state monitoring data as fault feature vectors, and identifies the fault type of the monitoring equipment by using a probabilistic neural network algorithm.
7. The regional power grid data overall management system based on big data according to claim 5, wherein: the power load prediction model is formed by constructing clusters on a Hadoop platform, and a K-Means clustering algorithm is realized on a parallel algorithm based on a MapRe-facility framework.
8. The regional power grid data overall management system based on big data according to claim 5, wherein: the power grid architecture planning model performs regional power consumption, load value and electricity loss value data value statistics on the data of the designated region, and compares the regional power consumption, load value and electricity loss value data value statistics with the past historical data value; according to the analysis of each item of data of the power grid in the appointed area, the change of each item of data value is counted, and a solution is made according to the change of each item of data value.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894064A (en) * | 2023-07-18 | 2023-10-17 | 国网信息通信产业集团有限公司北京分公司 | Intelligent integrated power-transformation auxiliary control data system and method |
| CN118042679A (en) * | 2024-04-11 | 2024-05-14 | 江苏秋洋智慧科技集团有限公司 | Self-adaptive urban illumination operation and maintenance system based on environment analysis |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116894064A (en) * | 2023-07-18 | 2023-10-17 | 国网信息通信产业集团有限公司北京分公司 | Intelligent integrated power-transformation auxiliary control data system and method |
| CN116894064B (en) * | 2023-07-18 | 2024-04-09 | 国网信息通信产业集团有限公司北京分公司 | Intelligent integrated power-transformation auxiliary control data system and method |
| CN118042679A (en) * | 2024-04-11 | 2024-05-14 | 江苏秋洋智慧科技集团有限公司 | Self-adaptive urban illumination operation and maintenance system based on environment analysis |
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