CN114442704B - Intelligent power production monitoring method and system based on big data - Google Patents
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Abstract
The invention relates to the technical field of power equipment monitoring, and particularly discloses an intelligent power production monitoring method and system based on big data, wherein the method comprises the steps of obtaining weather information in an area, and determining and predicting power generation power according to the weather information; acquiring actual generating power, comparing the actual generating power with predicted generating power, and acquiring operating parameters and temperature parameters of detection points in the power equipment according to a comparison result; and determining two risk values according to the operation parameters and the temperature parameters, and further determining an emergency scheme. The method comprises the steps of determining predicted power by obtaining weather information, comparing the predicted power with actual power, obtaining operation parameters and temperature parameters of equipment according to a comparison result, and monitoring the new energy equipment according to the operation parameters and the temperature parameters. The invention has strong adaptability, strong effectiveness and excellent monitoring effect.
Description
Technical Field
The invention relates to the technical field of power equipment monitoring, in particular to an intelligent power production monitoring method and system based on big data.
Background
Researchers at home and abroad clearly show that some renewable resources such as solar energy, wind energy and the like can be the novel energy with the greatest scale development prospect. In the last decade, the power generation infrastructures such as wind energy, solar energy and the like are increased to a great extent in the close weather, so that the new electric energy is reflected in the near future, and the new electric energy can be supplemented with energy to be gradually developed into alternative energy and finally becomes mainstream energy.
However, as for the current situation, the research of a large-scale new energy power equipment system in China has become a significant research subject, wherein an important subject is the monitoring problem of the power equipment, new energy often depends on the environment, the uncertainty of the new energy is very strong, and the traditional power equipment detection mode is difficult to adapt to the new energy power equipment, so how to design a detection system suitable for the new energy equipment is the technical problem to be solved by the technical scheme of the invention.
Disclosure of Invention
The invention aims to provide an intelligent power production monitoring method and system based on big data, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a big data based intelligent power production monitoring method, the method comprising:
acquiring weather information in an area, and determining and predicting power generation power according to the weather information;
acquiring actual generating power, calculating the deviation rate of the actual generating power and the predicted generating power, and comparing the deviation rate with a preset deviation threshold value;
when the deviation rate reaches a preset deviation threshold value, acquiring the operating parameters and temperature parameters of each detection point in the power equipment; wherein the operating parameter comprises an input signal and an output signal;
and determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value.
As a further scheme of the invention: the acquiring weather information in the area, and the determining the predicted generated power according to the weather information comprises:
acquiring historical power generation data and historical weather data in an area;
obtaining the influence rate of the weather index on the generated power of the power equipment according to the historical power generation data and the historical weather data;
acquiring weather forecast data in an area, acquiring air data in real time, and determining weather data according to the air data and the weather forecast data;
and calculating and predicting the generated power according to the weather data and the influence rate.
As a further scheme of the invention: the influence rate of the weather index on the generated power of the power equipment comprises the following steps:
when the power equipment is wind power generation equipment, the influence rate of the weather index on the wind power generation equipment comprises the following steps: the wind power index influences the highest load generation influence rate, the lowest load generation influence rate and the generated energy influence rate of the wind power generation equipment;
when the power equipment is centralized photovoltaic power generation equipment, the influence rate of the weather index on the centralized photovoltaic power generation equipment comprises: the influence rate of the weather clear index on the highest load of power generation and the influence rate of power generation amount of the centralized photovoltaic power generation;
when the power equipment is distributed photovoltaic power generation equipment, the influence rate of the weather index on the distributed photovoltaic power generation equipment comprises the following steps: the weather clear index is used for the highest load influence rate and the generated energy influence rate of the distributed photovoltaic power generation.
As a further scheme of the invention: when the deviation rate reaches a preset deviation threshold value, acquiring the operation parameters and the temperature parameters of each detection point in the power equipment comprises the following steps:
sensing the temperature inside the power equipment based on a temperature sensor inside the power equipment and generating a sensing signal;
acquiring an induction signal and generating a feedback signal based on a reader electromagnetically coupled with the temperature sensor; the reader acquires an induction signal in an electromagnetic coupling mode;
converting the feedback signal into temperature data, inputting the temperature data into a trained AE-LSTM model, and performing anomaly detection on the temperature data;
and eliminating invalid data according to the abnormal detection result to obtain the temperature parameter containing the time information.
As a further scheme of the invention: the temperature sensor adopts a wireless passive flexible film temperature sensor, and the wireless passive flexible film temperature sensor is coated on the outer surface of an electric wire in the power equipment; the wireless passive flexible film temperature sensor comprises an SAW temperature sensor, wherein the SAW temperature sensor comprises a substrate, an interdigital transducer, a reflecting grating and a receiving and transmitting antenna, the interdigital transducer is arranged on the substrate, and the receiving and transmitting antenna is connected with the interdigital transducer.
As a further scheme of the invention: the steps of determining a first risk value according to the operating parameter, determining a second risk value according to the temperature parameter, and determining an emergency plan according to the first risk value and the second risk value include:
extracting an input signal in the operation parameters, inputting the input signal into a preset theoretical model, and acquiring a theoretical signal;
extracting an output signal in the operation parameter, comparing the output signal with the theoretical signal, and calculating an output-theoretical offset rate;
comparing the output-theoretical offset rate with a preset level range, and determining a first risk value according to a comparison result;
reading a temperature parameter containing time information, carrying out mobility analysis on the temperature parameter based on the time information, and determining a second risk value according to a mobility analysis result;
determining an emergency scheme according to the first risk value and the second risk value based on a preset operation rule; the operation rule at least comprises a logic operation rule.
As a further scheme of the invention: the reading of the temperature parameter containing the time information, the performing of the volatility analysis on the temperature parameter based on the time information, and the determining of the second risk value according to the volatility analysis result includes:
generating a first fluctuation curve by taking the time information as an abscissa and the temperature parameter as an ordinate, and calculating a derivative function of the first fluctuation curve to obtain a second fluctuation curve;
inserting the first fluctuation curve and the second fluctuation curve into a preset background image to obtain a binary image; wherein one value in the binary image represents the background and the other value represents the first and second fluctuation curves;
identifying the characteristic points of the binary image, and determining the position and the number of the information points according to the identification result;
and determining a second risk value according to the position of the information points and the quantity of the information points.
The technical scheme of the invention also provides an intelligent power production monitoring system based on big data, which comprises:
the generating power prediction module is used for acquiring weather information in an area and determining and predicting generating power according to the weather information;
the comparison module is used for acquiring actual generated power, calculating the offset rate of the actual generated power and the predicted generated power, and comparing the offset rate with a preset offset threshold;
the parameter acquisition module is used for acquiring the operating parameters and the temperature parameters of each detection point in the power equipment when the migration rate reaches a preset migration threshold; wherein the operating parameter comprises an input signal and an output signal;
and the scheme determining module is used for determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value.
As a further scheme of the invention: the scheme determination module comprises:
the theoretical signal calculation unit is used for extracting an input signal in the operation parameters, inputting the input signal into a preset theoretical model and acquiring a theoretical signal;
the offset rate calculation unit is used for extracting an output signal in the operation parameter, comparing the output signal with the theoretical signal and calculating an output-theoretical offset rate;
the first risk determination unit is used for comparing the output-theoretical offset rate with a preset level range and determining a first risk value according to a comparison result;
the second risk determination unit is used for reading a temperature parameter containing time information, carrying out mobility analysis on the temperature parameter based on the time information, and determining a second risk value according to a mobility analysis result;
the processing execution unit is used for determining an emergency scheme according to the first risk value and the second risk value based on a preset operation rule; the operation rule at least comprises a logic operation rule.
As a further scheme of the invention: the second risk determination unit comprises:
the curve generation subunit is used for generating a first fluctuation curve by taking the time information as an abscissa and the temperature parameter as an ordinate, and calculating a derivative function of the first fluctuation curve to obtain a second fluctuation curve;
the curve inserting subunit is used for inserting the first fluctuation curve and the second fluctuation curve into a preset background image to obtain a binary image; wherein one value in the binary image represents the background and the other value represents the first and second fluctuation curves;
the characteristic identification subunit is used for identifying characteristic points of the binary image and determining the position and the number of the information points according to an identification result;
and the analysis subunit is used for determining a second risk value according to the position of the information points and the number of the information points.
Compared with the prior art, the invention has the beneficial effects that: the method comprises the steps of determining predicted power by obtaining weather information, comparing the predicted power with actual power, obtaining operation parameters and temperature parameters of equipment according to comparison results, and monitoring new energy equipment according to the operation parameters and the temperature parameters. The invention has strong adaptability, strong effectiveness and excellent monitoring effect.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
FIG. 1 illustrates a block flow diagram of a big data based intelligent power production monitoring method.
FIG. 2 illustrates a first sub-flow block diagram of a big data based intelligent power production monitoring method.
FIG. 3 illustrates a second sub-flow block diagram of a big data based intelligent power production monitoring method.
FIG. 4 illustrates a third sub-flow block diagram of a big data based intelligent power production monitoring method.
FIG. 5 shows a block diagram of the components of a big data based intelligent power production monitoring system.
Fig. 6 is a block diagram showing a configuration of a scenario determination module in the smart power generation monitoring system based on big data.
Fig. 7 is a block diagram showing the constitutional structure of the second risk determination unit in the scenario determination module.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects to be solved by the present invention more clearly apparent, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Fig. 1 shows a flow chart of an intelligent power production monitoring method based on big data, and in an embodiment of the present invention, the method includes steps S100 to S200:
step S100: acquiring weather information in an area, and determining and predicting power generation power according to the weather information;
the mainstream power generation mode in the future is a new energy power generation mode, and the new energy refers to renewable energy developed and utilized on the basis of new technology, such as wind energy, solar energy, biomass energy, water energy and the like. The new energy sources are increasingly used in the field of power generation, wherein the most common are wind power generation and photovoltaic power generation, the uncertainty of the two is high, and the influence of weather is obvious, so that the weather factor should be considered in the monitoring of the power equipment.
Step S200: acquiring actual generating power, calculating the deviation rate of the actual generating power and the predicted generating power, and comparing the deviation rate with a preset deviation threshold value;
the purpose of step S200 is to determine whether the difference between the actual generated power and the predicted generated power is too large or too small, and if the difference is too large or too small, some problems may occur. Of course, this difference is generally too large and rarely too small.
Step S300: when the deviation rate reaches a preset deviation threshold value, acquiring the operating parameters and temperature parameters of each detection point in the power equipment; wherein the operating parameter comprises an input signal and an output signal;
step S400: and determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value.
Step S300 and step S400 are specific monitoring processes, and specifically, when there is an abnormality in the difference between the actual generated power and the predicted generated power, the power equipment is further analyzed according to the operating parameters and the temperature parameters of the power equipment, so as to determine an emergency plan.
Fig. 2 shows a first sub-flow block diagram of the intelligent power production monitoring method based on big data, and the obtaining of the weather information in the area and the determining of the predicted generated power according to the weather information comprise steps S101 to S104:
step S101: acquiring historical power generation data and historical weather data in an area;
step S102: obtaining the influence rate of the weather index on the generated power of the power equipment according to the historical power generation data and the historical weather data;
step S103: acquiring weather forecast data in an area, acquiring air data in real time, and determining weather data according to the air data and the weather forecast data;
step S104: and calculating and predicting the generating power according to the weather data and the influence rate.
Steps S101 to S104 provide a specific technical solution for determining the predicted generated power according to the weather information, wherein the influence rate of the weather index on the generated power of the power equipment includes:
when the power equipment is wind power generation equipment, the influence rate of the weather index on the wind power generation equipment comprises the following steps: the wind power index influences the highest power generation load influence rate, the lowest power generation load influence rate and the power generation amount influence rate of the wind power generation equipment;
when the power equipment is centralized photovoltaic power generation equipment, the influence rate of the weather index on the centralized photovoltaic power generation equipment comprises: the weather clear index has the influence rate on the highest load of power generation and the influence rate on the generated energy of the centralized photovoltaic power generation;
when the power equipment is distributed photovoltaic power generation equipment, the influence rate of the weather index on the distributed photovoltaic power generation equipment comprises the following steps: the weather clear index is used for the highest load influence rate and the generated energy influence rate of the distributed photovoltaic power generation.
As a preferred embodiment of the technical solution of the present invention, when the offset rate reaches a preset offset threshold, the acquiring the operation parameter and the temperature parameter of each detection point in the electrical equipment includes:
sensing the temperature inside the power equipment based on a temperature sensor inside the power equipment and generating a sensing signal;
acquiring an induction signal and generating a feedback signal based on a reader electromagnetically coupled with the temperature sensor; the reader acquires an induction signal in an electromagnetic coupling mode;
converting the feedback signal into temperature data, inputting the temperature data into a trained AE-LSTM model, and performing anomaly detection on the temperature data;
and eliminating invalid data according to the abnormal detection result to obtain the temperature parameter containing the time information.
The basic idea of the above process is to induce the temperature inside the power equipment and generate an induction signal through a temperature sensor arranged inside the power equipment, obtain the induction signal and generate a feedback signal through a reader electromagnetically coupled with the temperature sensor in an electromagnetic coupling manner, obtain the feedback signal of the reader through a server arranged at the cloud and convert the feedback signal into corresponding temperature data, train the temperature data by constructing an AE-LSTM model and based on the AE-LSTM model, and perform anomaly detection on the temperature data through the AE-LSTM model, thereby realizing the anomaly detection of the power equipment.
Furthermore, the temperature sensor adopts a wireless passive flexible film temperature sensor, and the wireless passive flexible film temperature sensor is coated on the outer surface of the electric wire in the power equipment; the wireless passive flexible film temperature sensor comprises an SAW temperature sensor, wherein the SAW temperature sensor comprises a substrate, an interdigital transducer, a reflecting grating and a receiving and transmitting antenna, the interdigital transducer is arranged on the substrate, and the receiving and transmitting antenna is connected with the interdigital transducer.
In this embodiment, gather the relevant signal of the inside temperature of power equipment through electromagnetic coupling's mode, compare in wired connection's mode and gather the signal, do not need the connecting wire, compare in wireless connection's such as bluetooth mode and gather the signal, the cost is lower.
On one hand, the feedback signals are converted into corresponding temperature data, on the other hand, an AE-LSTM model is constructed, the temperature data are trained on the basis of the AE-LSTM model, and anomaly detection is carried out on the temperature data through the AE-LSTM model.
Specifically, the AE-LSTM model automatically learns the intrinsic dependence relationship in the temperature sensing data in an unsupervised mode, and extracts the data features. Because the data dimension in the original temperature data is large and sparse, an automatic coding machine is adopted to automatically learn the inherent dependence relationship in the data in an unsupervised mode, and characteristic data are extracted.
The detection results for the AE-LSTM model are set to be only two: abnormal data and normal data. This can be identified by 1 bit, 0 or 1. The output layer of the model is therefore all neuron-only, and it is specified that an output of 1 represents abnormal data and an output of 0 represents no normal data. However, the output of the artificial neural network varies according to the activation function, and is a decimal number between 0 and 1 in most cases, so that abnormal data is specified when the output exceeds a certain threshold, and normal data is specified otherwise. The abnormal data is invalid data.
Fig. 3 shows a second sub-flow block diagram of the intelligent big data-based power production monitoring method, wherein the steps of determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency plan according to the first risk value and the second risk value include steps S401 to S405:
step S401: extracting an input signal in the operation parameters, inputting the input signal into a preset theoretical model, and acquiring a theoretical signal;
step S402: extracting an output signal in the operation parameters, comparing the output signal with the theoretical signal, and calculating an output-theoretical offset rate;
step S403: comparing the output-theoretical offset rate with a preset level range, and determining a first risk value according to a comparison result;
step S404: reading a temperature parameter containing time information, carrying out volatility analysis on the temperature parameter based on the time information, and determining a second risk value according to a volatility analysis result;
step S405: determining an emergency scheme according to the first risk value and the second risk value based on a preset operation rule; the operation rule at least comprises a logic operation rule.
The purpose of steps S401 to S405 is to determine two risk values from the operating parameter and the temperature parameter, from which an emergency scenario is determined.
The steps of obtaining the risk value according to the operating parameter are steps S401 to S403, and in the process, the actual signal is mainly detected by a theoretical model of the power equipment.
As to how to determine the emergency plan according to the obtained two risk values, there are many specific ways, for example, the specific gravity of the two risk values is adjusted according to a preset weight formula, for example, one data with too high or too low risk value is removed from the two risk values, and the emergency plan is extracted through the remaining data.
Fig. 4 shows a third sub-flowchart of the intelligent power production monitoring method based on big data, where reading the temperature parameter with time information, performing mobility analysis on the temperature parameter based on the time information, and determining the second risk value according to the mobility analysis result includes steps S4041 to S4044:
step S4041: generating a first fluctuation curve by taking the time information as an abscissa and the temperature parameter as an ordinate, and calculating a derivative function of the first fluctuation curve to obtain a second fluctuation curve;
step S4042: inserting the first fluctuation curve and the second fluctuation curve into a preset background image to obtain a binary image; wherein one value in the binary image represents the background and the other value represents the first fluctuation curve and the second fluctuation curve;
step S4043: identifying the characteristic points of the binary image, and determining the position and the number of the information points according to the identification result;
step S4044: and determining a second risk value according to the position of the information points and the quantity of the information points.
The determination process of the second risk value is further limited in steps S4041 to S4044, the temperature parameter is first converted into a function image of time, and then some derivation operations are performed to obtain a plurality of curves based on the temperature parameter, the curves reflect the characteristics of the temperature parameter, the curves are identified based on an image identification mode, information points can be determined, and the second risk value is determined according to the positions and the number of the information points. The specific determination mode needs to be determined according to actual conditions, and on the premise of acquiring the positions and the number of the information points, the second risk value needs to be determined only by manually setting an empirical formula.
Example 2
Fig. 5 is a block diagram illustrating a configuration of an intelligent power generation monitoring system based on big data, in an embodiment of the present invention, the intelligent power generation monitoring system based on big data includes:
the generating power prediction module is used for acquiring weather information in an area and determining predicted generating power according to the weather information;
the comparison module is used for acquiring actual generated power, calculating the offset rate of the actual generated power and the predicted generated power, and comparing the offset rate with a preset offset threshold;
the parameter acquisition module is used for acquiring the operating parameters and the temperature parameters of each detection point in the power equipment when the migration rate reaches a preset migration threshold value; wherein the operating parameter comprises an input signal and an output signal;
and the scheme determining module is used for determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value.
FIG. 6 is a block diagram illustrating the structure of a scenario determination module in the smart power generation monitoring system based on big data, the scenario determination module including:
the theoretical signal calculation unit is used for extracting an input signal in the operation parameters, inputting the input signal into a preset theoretical model and acquiring a theoretical signal;
the offset rate calculation unit is used for extracting an output signal in the operation parameter, comparing the output signal with the theoretical signal and calculating an output-theoretical offset rate;
the first risk determination unit is used for comparing the output-theoretical offset rate with a preset level range and determining a first risk value according to a comparison result;
the second risk determination unit is used for reading a temperature parameter containing time information, carrying out mobility analysis on the temperature parameter based on the time information, and determining a second risk value according to a mobility analysis result;
the processing execution unit is used for determining an emergency scheme according to the first risk value and the second risk value based on a preset operation rule; the operation rule at least comprises a logic operation rule.
Fig. 7 is a block diagram showing a configuration of a second risk determining unit in the scenario determining module, the second risk determining unit including:
the curve generating subunit is configured to generate a first fluctuation curve by using the time information as an abscissa and the temperature parameter as an ordinate, and calculate a derivative function of the first fluctuation curve to obtain a second fluctuation curve;
the curve inserting subunit is used for inserting the first fluctuation curve and the second fluctuation curve into a preset background image to obtain a binary image; wherein one value in the binary image represents the background and the other value represents the first fluctuation curve and the second fluctuation curve;
the feature identification subunit is used for identifying feature points of the binary image and determining the position and the quantity of the information points according to an identification result;
and the analysis subunit is used for determining a second risk value according to the position of the information points and the number of the information points.
The functions which can be realized by the big data based intelligent electric power production monitoring method are all completed by a computer device which comprises one or more processors and one or more memories, wherein at least one program code is stored in the one or more memories, and the program code is loaded and executed by the one or more processors to realize the functions of the big data based intelligent electric power production monitoring method.
The processor fetches instructions and analyzes the instructions one by one from the memory, then completes corresponding operations according to the instruction requirements, generates a series of control commands, enables all parts of the computer to automatically, continuously and coordinately act to form an organic whole, realizes the input of programs, the input of data, the operation and the output of results, and the arithmetic operation or the logic operation generated in the process is completed by the arithmetic unit; the Memory comprises a Read-Only Memory (ROM) for storing a computer program, and a protection device is arranged outside the Memory.
Illustratively, the computer program may be partitioned into one or more modules, stored in memory and executed by a processor, to implement the invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the service device is merely exemplary and not limiting of the terminal device, and may include more or less components than those described, or combine certain components, or different components, such as may include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory mainly comprises a storage program area and a storage data area, wherein the storage program area can store an operating system, application programs (such as an information acquisition template display function, a product information publishing function and the like) required by at least one function and the like; the storage data area may store data created according to the use of the berth-state display system (e.g., product information acquisition templates corresponding to different product types, product information that needs to be issued by different product providers, etc.), and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiment may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the embodiments of the system. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution media, and the like.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (4)
1. An intelligent power production monitoring method based on big data, characterized by comprising:
acquiring weather information in an area, and determining and predicting power generation power according to the weather information;
acquiring actual generating power, calculating the deviation rate of the actual generating power and the predicted generating power, and comparing the deviation rate with a preset deviation threshold value;
when the deviation rate reaches a preset deviation threshold value, acquiring the operating parameters and temperature parameters of each detection point in the power equipment; wherein the operating parameter comprises an input signal and an output signal;
determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value;
the obtaining weather information in the area, and determining the predicted power generation power according to the weather information comprises:
acquiring historical power generation data and historical weather data in an area;
obtaining the influence rate of the weather index on the generated power of the power equipment according to the historical power generation data and the historical weather data;
acquiring weather forecast data in an area, acquiring air data in real time, and determining weather data according to the air data and the weather forecast data;
calculating and predicting the power generation power according to the weather data and the influence rate;
the influence rate of the weather index on the generated power of the power equipment comprises the following steps:
when the power equipment is wind power generation equipment, the influence rate of the weather index on the wind power generation equipment comprises the following steps: the wind power index influences the highest load generation influence rate, the lowest load generation influence rate and the generated energy influence rate of the wind power generation equipment;
when the power equipment is centralized photovoltaic power generation equipment, the influence rate of the weather index on the centralized photovoltaic power generation equipment comprises: the influence rate of the weather clear index on the highest load of power generation and the influence rate of power generation amount of the centralized photovoltaic power generation;
when the power equipment is distributed photovoltaic power generation equipment, the influence rate of the weather index on the distributed photovoltaic power generation equipment comprises the following steps: the weather clear index is used for the highest power generation load influence rate and the generated energy influence rate of the distributed photovoltaic power generation;
determining a first risk value according to the operating parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value comprises:
extracting an input signal in the operation parameters, inputting the input signal into a preset theoretical model, and acquiring a theoretical signal;
extracting an output signal in the operation parameters, comparing the output signal with the theoretical signal, and calculating an output-theoretical offset rate;
comparing the output-theoretical offset rate with a preset level range, and determining a first risk value according to a comparison result;
reading a temperature parameter containing time information, carrying out volatility analysis on the temperature parameter based on the time information, and determining a second risk value according to a volatility analysis result;
determining an emergency scheme according to the first risk value and the second risk value based on a preset operation rule; the operation rule at least comprises a logic operation rule;
the reading of the temperature parameter containing the time information, the performing of the volatility analysis on the temperature parameter based on the time information, and the determining of the second risk value according to the volatility analysis result include:
generating a first fluctuation curve by taking the time information as an abscissa and the temperature parameter as an ordinate, and calculating a derivative function of the first fluctuation curve to obtain a second fluctuation curve;
inserting the first fluctuation curve and the second fluctuation curve into a preset background image to obtain a binary image; wherein one value in the binary image represents the background and the other value represents the first fluctuation curve and the second fluctuation curve;
identifying the characteristic points of the binary image, and determining the position and the number of the information points according to the identification result;
and determining a second risk value according to the position of the information points and the number of the information points.
2. The intelligent big data-based power production monitoring method according to claim 1, wherein the obtaining the operating parameters and the temperature parameters of each detection point in the power equipment when the migration rate reaches a preset migration threshold comprises:
sensing the temperature inside the power equipment based on a temperature sensor inside the power equipment and generating a sensing signal;
acquiring an induction signal and generating a feedback signal based on a reader electromagnetically coupled with the temperature sensor; the reader acquires an induction signal in an electromagnetic coupling mode;
converting the feedback signal into temperature data, inputting the temperature data into a trained AE-LSTM model, and performing anomaly detection on the temperature data;
and eliminating invalid data according to the abnormal detection result to obtain the temperature parameter containing the time information.
3. The intelligent big data-based power production monitoring method according to claim 2, wherein the temperature sensor is a wireless passive flexible thin film temperature sensor, and the wireless passive flexible thin film temperature sensor is coated on the outer surface of the electric wire inside the power equipment; the wireless passive flexible film temperature sensor comprises an SAW temperature sensor, wherein the SAW temperature sensor comprises a substrate, an interdigital transducer, a reflecting grating and a receiving and transmitting antenna, the interdigital transducer is arranged on the substrate, and the receiving and transmitting antenna is connected with the interdigital transducer.
4. An intelligent big data based power production monitoring system, the system comprising:
the generating power prediction module is used for acquiring weather information in an area and determining predicted generating power according to the weather information;
the comparison module is used for acquiring actual generated power, calculating the offset rate of the actual generated power and the predicted generated power, and comparing the offset rate with a preset offset threshold;
the parameter acquisition module is used for acquiring the operating parameters and the temperature parameters of each detection point in the power equipment when the migration rate reaches a preset migration threshold; wherein the operating parameter comprises an input signal and an output signal;
the scheme determining module is used for determining a first risk value according to the operation parameter, determining a second risk value according to the temperature parameter, and determining an emergency scheme according to the first risk value and the second risk value;
the scheme determination module comprises:
the theoretical signal calculation unit is used for extracting an input signal in the operation parameters, inputting the input signal into a preset theoretical model and acquiring a theoretical signal;
the offset rate calculation unit is used for extracting an output signal in the operation parameter, comparing the output signal with the theoretical signal and calculating an output-theoretical offset rate;
the first risk determination unit is used for comparing the output-theoretical offset rate with a preset level range and determining a first risk value according to a comparison result;
the second risk determination unit is used for reading a temperature parameter containing time information, carrying out mobility analysis on the temperature parameter based on the time information, and determining a second risk value according to a mobility analysis result;
the processing execution unit is used for determining an emergency scheme according to the first risk value and the second risk value based on a preset operation rule; the operation rule at least comprises a logic operation rule;
the second risk determination unit comprises:
the curve generation subunit is used for generating a first fluctuation curve by taking the time information as an abscissa and the temperature parameter as an ordinate, and calculating a derivative function of the first fluctuation curve to obtain a second fluctuation curve;
the curve inserting subunit is used for inserting the first fluctuation curve and the second fluctuation curve into a preset background image to obtain a binary image; wherein one value in the binary image represents the background and the other value represents the first fluctuation curve and the second fluctuation curve;
the characteristic identification subunit is used for identifying characteristic points of the binary image and determining the position and the number of the information points according to an identification result;
the analysis subunit is used for determining a second risk value according to the position and the number of the information points;
the generated power prediction module includes:
historical power generation data and historical weather data are used for determining predicted power generation power according to the weather information, and the influence rate of weather indexes on the power generation power of the power equipment is obtained according to the historical power generation data and the historical weather data;
weather forecast data, which is used for acquiring air data in real time, determining weather data according to the air data and the weather forecast data, and calculating and predicting power generation power according to the weather data and the influence rate;
the influence rate of the weather index on the generated power of the power equipment comprises the following steps: when the power equipment is wind power generation equipment, the influence rate of the weather index on the wind power generation equipment comprises the following steps: the wind power index influences the highest power generation load influence rate, the lowest power generation load influence rate and the power generation amount influence rate of the wind power generation equipment;
when the power equipment is centralized photovoltaic power generation equipment, the influence rate of the weather index on the centralized photovoltaic power generation equipment comprises: the weather clear index has the influence rate on the highest load of power generation and the influence rate on the generated energy of the centralized photovoltaic power generation;
when the power equipment is distributed photovoltaic power generation equipment, the influence rate of the weather index on the distributed photovoltaic power generation equipment comprises the following steps: the weather clear index is used for the highest load influence rate and the generated energy influence rate of the distributed photovoltaic power generation.
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Application publication date: 20220506 Assignee: HUANENG JINAN HUANGTAI POWER GENERATION CO.,LTD. Assignor: HUANENG SHANDONG POWER GENERATION Co.,Ltd. Contract record no.: X2023980051458 Denomination of invention: A method and system for intelligent power production monitoring based on big data Granted publication date: 20230328 License type: Common License Record date: 20231220 |