CN116845913A - Power system safety control method and device based on multi-time space scale frequency - Google Patents

Power system safety control method and device based on multi-time space scale frequency Download PDF

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CN116845913A
CN116845913A CN202310591995.2A CN202310591995A CN116845913A CN 116845913 A CN116845913 A CN 116845913A CN 202310591995 A CN202310591995 A CN 202310591995A CN 116845913 A CN116845913 A CN 116845913A
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power system
frequency
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洪巧章
袁太平
邹贵林
柯伟
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Energy Development Research Institute of China Southern Power Grid Co Ltd
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Energy Development Research Institute of China Southern Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
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    • H02J2300/28—The renewable source being wind energy

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Abstract

The application relates to a power system safety control method, a device, a computer device, a storage medium and a computer program product based on multi-time space scale frequency. The method comprises the following steps: determining the curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve of the power system in a historical time period; according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in the future time period; performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system; and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve. By adopting the method, the safety of the power system can be improved.

Description

Power system safety control method and device based on multi-time space scale frequency
Technical Field
The application relates to the technical field of smart grids, in particular to a power system safety control method, a device, computer equipment, a storage medium and a computer program product based on multi-time space scale frequency.
Background
In the novel power system, the increase of the capacity proportion of the new energy unit increases the frequency adjustment difficulty of the power system.
In order to ensure safe operation and production of the power system, in the conventional technology, frequency data of a power system unit is often determined in a data driving mode, for example, frequency data of different units such as a new energy unit, a wind turbine unit, a thermal power unit and the like are predicted in real time through a deep learning model, and then the unit is regulated and controlled according to the predicted frequency data. However, the data driving mode is easy to be influenced by environmental changes, so that the frequency of the power system greatly fluctuates, and the frequency instability risk of the power system is further increased due to frequent large fluctuation of the frequency, which is not beneficial to the safety of the power system.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, apparatus, computer device, computer readable storage medium, and computer program product for power system safety control based on multiple space-time scale frequencies that can improve power system safety.
In a first aspect, the application provides a power system safety control method based on multiple space-time scale frequencies. The method comprises the following steps:
Determining the curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve of the power system in a historical time period;
according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in the future time period;
performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve.
In one embodiment, the blurring processing is performed on the predicted frequency curve to obtain a candidate frequency curve of the power system, including:
fuzzification processing is carried out on the predicted frequency curve, so that a fuzzy frequency curve corresponding to the predicted frequency curve is obtained;
according to a fuzzy rule, fuzzy reasoning is carried out on the fuzzy frequency curve, and a fuzzy reasoning curve corresponding to the fuzzy frequency curve is obtained; the fuzzy rule is set according to the bearable frequency range of the power system;
and performing defuzzification processing on the fuzzy inference curve to obtain a candidate frequency curve of the power system.
In one embodiment, according to the variance of the candidate frequency curve, the candidate frequency curve is processed to obtain a target frequency curve of the power system, including:
judging whether the variance of the candidate frequency curve is larger than a preset variance threshold;
and under the condition that the variance of the candidate frequency curve is larger than the preset variance threshold, performing curve smoothing on the candidate frequency curve to obtain a target frequency curve corresponding to the candidate frequency curve.
In one embodiment, predicting a predicted frequency curve of the power system in the future time period according to the curve similarity and the current frequency curve includes:
determining the weight of the historical frequency curve according to the curve similarity;
and inputting the weights of the current frequency curve, the historical frequency curve and the historical frequency curve into a frequency time sequence prediction model to obtain a predicted frequency curve of the power system in the future time period.
In one embodiment, determining a curve similarity between a current frequency curve of the power system over a current time period and a historical frequency curve over a historical time period includes:
Performing dynamic time warping processing on the current frequency curve and the historical frequency curve to obtain a frequency data distance between the current frequency curve and the historical frequency curve;
and performing similarity conversion on the frequency data distance to obtain the curve similarity.
In one embodiment, regulating the power system according to the target frequency profile includes:
acquiring regulation and control weights of power equipment in the power system and the maximum bearing load of the power equipment;
according to the regulation weight, weighting the load capacity of the power system to obtain the load to be processed of the power equipment; the load capacity of the power system is calculated according to target frequency data in a target frequency curve;
updating the load to be processed of the power equipment according to the maximum bearing load of the power equipment to obtain the target load of the power equipment;
and adjusting power equipment in the power system according to the target load.
In a second aspect, the application further provides a safety control device of the electric power system based on the multi-space-time scale frequency. The device comprises:
The similarity determining module is used for determining the curve similarity between the current frequency curve of the power system in the current time period and the historical frequency curve of the power system in the historical time period;
the frequency prediction module is used for predicting and obtaining a predicted frequency curve of the power system in the future time period according to the curve similarity and the current frequency curve;
the fuzzy processing module is used for carrying out fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and the frequency determining module is used for processing the candidate frequency curve according to the variance of the candidate frequency in the candidate frequency curve to obtain a target frequency curve of the power system so as to regulate and control the power system according to the target frequency curve.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
determining the curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve of the power system in a historical time period;
According to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in the future time period;
performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
determining the curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve of the power system in a historical time period;
according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in the future time period;
performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:
determining the curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve of the power system in a historical time period;
according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in the future time period;
performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve.
The above-mentioned power system safety control method, device, computer equipment, storage medium and computer program product based on multiple space-time scale frequency, confirm the curve similarity between the current frequency curve of the power system in the current time quantum and the historical frequency curve in the historical time quantum; according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in a future time period; performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system; and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system so as to regulate and control the power system according to the target frequency curve. According to the method, the predicted frequency curve of the power system in the future time period is obtained through the preliminary prediction of the curve similarity, then the accurate candidate frequency curve is obtained through the fuzzy processing, and finally the candidate frequency curve is further adjusted based on the variance, so that the fluctuation of the obtained target frequency curve is small, the stability of the frequency of the power system is improved, and the operation safety of the power system is greatly improved.
Drawings
FIG. 1 is a flow diagram of a method for power system safety control based on multiple space-time scale frequencies in one embodiment;
FIG. 2 is a flowchart illustrating a step of obtaining candidate frequency curves of a power system according to an embodiment;
FIG. 3 is a flow chart of a method of power system safety control based on multiple space-time scale frequencies in another embodiment;
FIG. 4 is a block diagram of a power system safety control device based on multiple space-time scale frequencies in one embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, a method for controlling safety of an electric power system based on multiple space-time scale frequencies is provided, and this embodiment is exemplified by the method being applied to a server, it is understood that the method may also be applied to a control terminal of the electric power system, and may also be applied to a system including the control terminal and the server, and implemented through interaction between the control terminal and the server. The server may be implemented as a stand-alone server or as a server cluster formed by a plurality of servers. In this embodiment, the method includes the steps of:
Step S101, determining a curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve in a historical time period.
The current frequency curve is a curve constructed by current frequency data of a current time period. The historical frequency curve is a curve obtained by constructing historical frequency data of a historical time period. It will be appreciated that each frequency data (e.g., current frequency data or historical frequency data) in a frequency profile (e.g., current frequency profile or historical frequency profile) characterizes the total frequency of the power system at the corresponding time.
Specifically, the server collects frequency data of each unit in the power system in different time periods, and draws the frequency data into a frequency curve according to the time periods, so that the server obtains a current frequency curve corresponding to the current time period and a historical frequency curve corresponding to the historical time period. Wherein the units of the different time periods may be days, half days, hours or half hours, etc. And the server calculates the curve similarity between the current frequency curve and the historical frequency curve according to the similarity between each current frequency data in the current frequency curve and each historical frequency data in the historical frequency curve.
Step S102, according to the curve similarity and the current frequency curve, a predicted frequency curve of the power system in a future time period is predicted.
The predicted frequency curve is a frequency curve predicted according to a change rule of a frequency curve of the power system on a time sequence. The predicted frequency curve is composed of a plurality of predicted frequency data.
Specifically, the server predicts the current frequency curve and the historical frequency curve through the frequency time sequence prediction model, wherein the historical frequency curve can comprise historical frequency curves in a plurality of historical time periods, and further the server can assign higher weight to the historical frequency curve with higher similarity through the curve similarity, so that the frequency time sequence prediction model can more accurately extract the change rule of frequency data on a time sequence, and the prediction precision of the predicted frequency curve output by the frequency time sequence prediction model is improved.
Step S103, fuzzy processing is carried out on the predicted frequency curve, and a candidate frequency curve of the power system is obtained.
Specifically, the server may perform fuzzy processing on the predicted frequency curve through a fuzzifier according to a fuzzy rule; the fuzzy self-adaptive PID control processing can be carried out on the predicted frequency curve according to the fuzzy rule; the server obtains candidate frequency curves for the power system.
The fuzzy rule is a processing rule indicating a characteristic setting of frequency data of the power system. For example, the frequency data needs to be within a preset frequency range to ensure the safety of the power system, and a maximum frequency upper limit and a minimum frequency lower limit can be set for the frequency data. As another example, the fuzzy rule may also set associations between frequency data and other power data (e.g., voltage, output power, etc.).
And step S104, processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system so as to regulate and control the power system according to the target frequency curve.
The target frequency curve is a frequency curve obtained by adjusting frequency data (i.e., candidate frequency) in the candidate frequency curve.
Specifically, the server calculates the variance of the candidate frequencies in the candidate frequency curve to obtain the variance of the candidate frequency curve; and then processing the candidate frequency curve according to a comparison result between the variance and a preset variance threshold, and obtaining a target frequency curve by the server to reduce the fluctuation degree of each target frequency data in the target frequency curve, so that it can be understood that the fluctuation degree of the target frequency data in the target frequency curve is smaller than the fluctuation degree of the candidate frequency in the candidate frequency curve. And the server sends the target frequency curve to a control terminal of the power system so that the control terminal regulates and controls the power system according to the received target frequency curve.
In the above power system safety control method based on multiple space-time scale frequencies, determining the curve similarity between the current frequency curve of the power system in the current time period and the historical frequency curve in the historical time period; according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in a future time period; performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system; and processing the candidate frequency curve according to the variance of the candidate frequency in the candidate frequency curve to obtain a target frequency curve of the power system so as to regulate and control the power system according to the target frequency curve. According to the method, the predicted frequency curve of the power system in the future time period is obtained through the preliminary prediction of the curve similarity, then the accurate candidate frequency curve is obtained through the fuzzy processing, and finally the candidate frequency curve is further adjusted based on the variance, so that the fluctuation of the obtained target frequency curve is small, the frequency stability of the power system is improved, and the operation safety of the power system is further improved.
In one embodiment, as shown in fig. 2, in step S103, the predicted frequency curve is subjected to fuzzy processing to obtain a candidate frequency curve of the power system, which specifically includes the following contents:
Step S201, blurring processing is carried out on the predicted frequency curve, and a blurred frequency curve corresponding to the predicted frequency curve is obtained.
Step S202, fuzzy reasoning is carried out on a fuzzy frequency curve according to a fuzzy rule, and a fuzzy reasoning curve corresponding to the fuzzy frequency curve is obtained; the fuzzy rule is set according to the affordable frequency range of the power system.
And step S203, performing defuzzification processing on the fuzzy inference curve to obtain a candidate frequency curve of the power system.
The fuzzy rule is used for deducing candidate frequency curves according to the fuzzy inference curve in the fuzzy inference processing process.
Specifically, the server may perform blurring processing on the predicted frequency curve according to the membership function to obtain a blurred frequency curve corresponding to the predicted frequency curve. The server can set and obtain a fuzzy rule according to the bearable frequency range, the frequency error and the error rate of the power system, and then perform fuzzy reasoning on the fuzzy frequency curve according to the fuzzy rule, and calculate and obtain a fuzzy reasoning curve corresponding to the fuzzy frequency curve; and finally, performing defuzzification processing on the fuzzy reasoning curve, and obtaining a candidate frequency curve of the power system by the server.
In the embodiment, a fuzzy frequency curve corresponding to a predicted frequency curve is obtained through fuzzification; and then, according to a fuzzy rule, performing fuzzy reasoning on the fuzzy frequency curve to obtain a fuzzy reasoning curve corresponding to the fuzzy frequency curve, finally, converting the fuzzy reasoning curve into a candidate frequency curve through defuzzification processing, and further optimizing the predicted frequency curve through combining the fuzzy reasoning, so that the obtained candidate frequency curve is more accurate, and meanwhile, the situation that the predicted frequency curve does not meet the bearable frequency range of the power system can be avoided, so that the accurate control of the frequency of the power system is realized.
In one embodiment, the step S104 processes the candidate frequency curve according to the variance of the candidate frequency curve to obtain the target frequency curve of the power system, which specifically includes the following steps: judging whether the variance of the candidate frequency curve is larger than a preset variance threshold; and under the condition that the variance of the candidate frequency curve is larger than a preset variance threshold, performing curve smoothing on the candidate frequency curve to obtain a target frequency curve corresponding to the candidate frequency curve.
The preset variance threshold is a variance threshold set by the pointer to the candidate frequency curve. The variance of the candidate frequency curve is used to measure the degree of fluctuation of the frequency data in the candidate frequency curve.
Although the candidate frequency data in the candidate frequency curve meets basic conditions such as the bearable frequency range of the power system after fuzzy reasoning, the candidate frequency data in the candidate frequency curve may have a large fluctuation range, which is unfavorable for the stability and safety of the power system, and thus further optimization processing of the candidate frequency curve is required. Specifically, the server calculates the variance of the candidate frequency curve according to each candidate frequency data in the candidate frequency curve, and compares the variance of the candidate frequency curve with a preset variance threshold to judge whether the variance of the candidate frequency curve is larger than the preset variance threshold. And under the condition that the variance of the candidate frequency curve is larger than a preset variance threshold, the server performs curve smoothing processing on the candidate frequency curve, namely, performs curve fitting processing on the candidate frequency curve to smooth out the candidate frequency data with larger fluctuation, or performs moving average processing on the candidate frequency data in the candidate frequency curve, or performs exponential smoothing processing on the candidate frequency data in the candidate frequency curve, so that the server obtains a target frequency curve corresponding to the candidate frequency curve. And under the condition that the variance of the candidate frequency curve is smaller than or equal to a preset variance threshold, the server does not need to process the candidate frequency curve, and takes the candidate frequency curve as a target frequency curve.
In this embodiment, the magnitude relation between the variance of the candidate frequency curve and the preset variance threshold is determined, so that the server performs curve smoothing processing on the candidate frequency curve under the condition that the variance is greater than the preset variance threshold, thereby obtaining a target frequency curve corresponding to the candidate frequency curve, measuring the data fluctuation condition of the candidate frequency data in the candidate frequency curve by the variance, and reducing the fluctuation degree of the candidate frequency data by curve smoothing processing, thereby improving the stability and safety of the power system.
In one embodiment, the step S102 predicts a predicted frequency curve of the power system in a future time period according to the curve similarity and the current frequency curve, and specifically includes the following steps: determining the weight of a historical frequency curve according to the similarity of the curves; and inputting weights of the current frequency curve, the historical frequency curve and the historical frequency curve into a frequency time sequence prediction model to obtain a predicted frequency curve of the power system in a future time period.
The frequency time sequence prediction model is a model for predicting the frequency data of a future time period according to the change rule of the frequency data on the time sequence.
Specifically, the server gives the highest weight to the current frequency curve; meanwhile, the weights of the historical frequency curves are determined according to the similarity of the curves, the weights of the historical frequency curves can be reduced according to the sequence from high to low of the similarity of the curves, and then the server obtains the weights of the current frequency curve and the weights of the historical frequency curve. And the server inputs the weights of the current frequency curve, the historical frequency curve and the historical frequency curve into the frequency time sequence prediction model so as to perform time sequence prediction processing on the current frequency curve and the historical frequency curve according to the weights through the frequency time sequence prediction model, and the frequency time sequence prediction model outputs a predicted frequency curve corresponding to a future time period. It can be understood that by giving a higher weight to the historical frequency curve with higher curve similarity, the frequency time sequence prediction model can predict according to the change rule of the historical frequency data and the current frequency data, so that the accuracy of the predicted frequency curve is improved.
In this embodiment, firstly, according to the similarity of curves, determining the weight of a historical frequency curve; and then, the weights of the current frequency curve, the historical frequency curve and the historical frequency curve are input into a frequency time sequence prediction model to obtain a predicted frequency curve of the power system in a future time period, so that the frequency time sequence prediction model predicts according to the change rule of the historical frequency data and the current frequency data, and the accuracy of the predicted frequency curve obtained by prediction is greatly improved by means of the weights.
In one embodiment, the step S101 is a method for determining a curve similarity between a current frequency curve of a power system in a current time period and a historical frequency curve in a historical time period, and specifically includes the following steps: performing dynamic time warping processing on the current frequency curve and the historical frequency curve to obtain a frequency data distance between the current frequency curve and the historical frequency curve; and performing similarity conversion on the frequency data distance to obtain curve similarity.
The frequency data distance refers to the shortest distance between each current frequency data in the current frequency curve and each historical frequency data in the historical frequency curve. The curve similarity refers to an index for measuring the similarity between each current frequency data in the current frequency curve and each historical frequency data in the historical frequency curve.
Specifically, after the server obtains the current frequency curve corresponding to the current time period, the server can obtain the historical frequency curves corresponding to different historical time periods from a database or local storage, and then the server performs dynamic time warping (Dynamic Time Warping, DTW) on the current frequency curve and the historical frequency curve to determine all similar frequency data between the current frequency curve and the historical frequency curve, and obtains the distance between all similar frequency data as the frequency data distance between the current frequency curve and the historical frequency curve; the distance between the similar frequency data may be a euclidean distance between the similar frequency data in the current frequency curve and the corresponding similar frequency data in the historical frequency curve. And the server calculates and obtains the curve similarity between the current frequency curve and the historical frequency curve according to the frequency data distance.
In the embodiment, the frequency data distance between the current frequency curve and the historical frequency curve is obtained by carrying out dynamic time warping processing on the current frequency curve and the historical frequency curve; and then, the similarity conversion is carried out on the frequency data distance to obtain curve similarity, so that the reasonable acquisition of the curve similarity between the current frequency curve and the historical frequency curve is realized, and the curve similarity can be used as a processing basis in the subsequent step to execute the prediction step of predicting the frequency curve in the future time period.
In one embodiment, the step S104 regulates and controls the power system according to the target frequency curve, which specifically includes the following steps: acquiring regulation weight of power equipment and maximum bearing load of the power equipment in the power system; according to the regulation weight, weighting the load capacity of the power system to obtain the load to be processed of the power equipment; the load capacity of the power system is calculated according to the target frequency data in the target frequency curve; updating the load to be processed of the power equipment according to the maximum bearing load of the power equipment to obtain the target load of the power equipment; and adjusting the power equipment in the power system according to the target load.
The regulation weight is used for measuring the load size which can be born by each power equipment in the power system. The maximum load is the maximum load that the power equipment can withstand in a safe state.
It will be appreciated that the maximum load that the power device can withstand in a safe state is different from the limit load that the power device can withstand. For example, about the frequency a, a certain power device can stably and normally operate, but about the frequency B, the power device is in a limit state of overload operation, and once the duration of the limit state is long or the load continues to be increased, the power device is damaged, the frequency a is the maximum load that the power device can bear in a safe state, and the frequency B is the limit load that the power device can bear.
Specifically, after obtaining the target frequency curve of the target time period, the server can also obtain the regulation weight of the power equipment and the maximum bearing load of the power equipment in the power system; the server calculates and obtains the load capacity corresponding to each target frequency data in the target frequency curve respectively; and then, respectively carrying out weighting processing on each load capacity according to the regulation and control weight of each power equipment, and obtaining the load to be processed corresponding to each power equipment by the server. The server determines a load difference value between the load to be processed of each power device and the maximum bearing load of each power device; under the condition that the load difference value is smaller than or equal to a load difference value threshold value, obtaining a target load according to subtracting a preset load from the load to be processed; taking the maximum bearable load as a target load when the load difference is larger than a load difference threshold, and equally dividing the load difference to the rest of the power equipment; and finally, the server adjusts the corresponding power equipment according to the target load.
In the embodiment, firstly, the load capacity of the power system is calculated according to target frequency data in a target frequency curve; then, according to the regulation weight, the load capacity of the power system is weighted to obtain the load to be processed of the power equipment; updating the load to be processed of the power equipment according to the maximum bearing load of the power equipment to obtain the target load of the power equipment; finally, according to the target load, the power equipment in the power system is regulated, the load can be distributed according to the actual bearable condition of each power equipment in the power system to the load, overload carrying of the power equipment is avoided, and therefore the operation safety of the power system is improved.
In one embodiment, as shown in fig. 3, another method for controlling safety of an electric power system based on multiple space-time scale frequencies is provided, and the method is applied to a server for illustration, and includes the following steps:
step S301, performing dynamic time warping processing on a current frequency curve and a historical frequency curve to obtain a frequency data distance between the current frequency curve and the historical frequency curve; and performing similarity conversion on the frequency data distance to obtain curve similarity.
Step S302, determining the weight of a historical frequency curve according to the similarity of the curves; and inputting weights of the current frequency curve, the historical frequency curve and the historical frequency curve into a frequency time sequence prediction model to obtain a predicted frequency curve of the power system in a future time period.
Step S303, blurring processing is carried out on the predicted frequency curve, and a blurred frequency curve corresponding to the predicted frequency curve is obtained; and carrying out fuzzy reasoning on the fuzzy frequency curve according to the fuzzy rule to obtain a fuzzy reasoning curve corresponding to the fuzzy frequency curve.
Wherein the fuzzy rule is set according to the bearable frequency range of the power system.
And step S304, performing defuzzification processing on the fuzzy inference curve to obtain a candidate frequency curve of the power system.
Step S305, judging whether the variance of the candidate frequency curve is larger than a preset variance threshold.
And step S306, performing curve smoothing processing on the candidate frequency curve to obtain a target frequency curve corresponding to the candidate frequency curve under the condition that the variance of the candidate frequency curve is larger than a preset variance threshold.
Step S307, acquiring a regulation weight of the power equipment and a maximum bearing load of the power equipment in the power system.
Step S308, carrying out weighting treatment on the load capacity of the power system according to the regulation weight to obtain the load to be treated of the power equipment; the load capacity of the power system is calculated according to the target frequency data in the target frequency curve.
Step S309, updating the load to be processed of the power equipment according to the maximum bearing load of the power equipment to obtain the target load of the power equipment; and adjusting the power equipment in the power system according to the target load.
The power system safety control method based on the multi-space-time scale frequency can realize the following beneficial effects: the method can obtain the predicted frequency curve of the power system in the future time period through the preliminary prediction of the curve similarity, then obtain the accurate candidate frequency curve through the fuzzy processing, and finally further adjust the candidate frequency curve based on the variance, so that the obtained target frequency curve has small fluctuation, the stability of the frequency of the power system is improved, and the operation safety of the power system is greatly improved.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a multi-time space scale frequency-based power system safety control device for realizing the multi-time space scale frequency-based power system safety control method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the power system safety control device based on multiple space-time scale frequencies provided below may be referred to the limitation of the power system safety control method based on multiple space-time scale frequencies hereinabove, and will not be repeated here.
In one embodiment, as shown in fig. 4, there is provided a power system safety control device 400 based on multiple space-time scale frequencies, comprising: a similarity determination module 401, a frequency prediction module 402, a blur processing module 403, and a frequency determination module 404, wherein:
the similarity determining module 401 is configured to determine a curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve in a historical time period.
The frequency prediction module 402 is configured to predict and obtain a predicted frequency curve of the power system in a future time period according to the curve similarity and the current frequency curve.
And the blurring processing module 403 is configured to perform blurring processing on the predicted frequency curve to obtain a candidate frequency curve of the power system.
The frequency determining module 404 is configured to process the candidate frequency curve according to the variance of the candidate frequency in the candidate frequency curve, so as to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve.
In one embodiment, the blurring processing module 403 is further configured to perform blurring processing on the predicted frequency curve, so as to obtain a blurred frequency curve corresponding to the predicted frequency curve; according to the fuzzy rule, fuzzy reasoning is carried out on the fuzzy frequency curve, and a fuzzy reasoning curve corresponding to the fuzzy frequency curve is obtained; the fuzzy rule is set according to the bearable frequency range of the power system; and performing defuzzification processing on the fuzzy reasoning curve to obtain a candidate frequency curve of the power system.
In one embodiment, the frequency determining module 404 is further configured to determine whether the variance of the candidate frequency curve is greater than a preset variance threshold; and under the condition that the variance of the candidate frequency curve is larger than a preset variance threshold, performing curve smoothing on the candidate frequency curve to obtain a target frequency curve corresponding to the candidate frequency curve.
In one embodiment, the frequency prediction module 402 is further configured to determine a weight of the historical frequency curve according to the curve similarity; and inputting weights of the current frequency curve, the historical frequency curve and the historical frequency curve into a frequency time sequence prediction model to obtain a predicted frequency curve of the power system in a future time period.
In one embodiment, the similarity determining module 401 is further configured to perform a dynamic time warping process on the current frequency curve and the historical frequency curve to obtain a frequency data distance between the current frequency curve and the historical frequency curve; and performing similarity conversion on the frequency data distance to obtain curve similarity.
In one embodiment, the frequency determining module 404 is further configured to obtain a regulation weight of the power device and a maximum bearing load of the power device in the power system; according to the regulation weight, weighting the load capacity of the power system to obtain the load to be processed of the power equipment; the load capacity of the power system is calculated according to the target frequency data in the target frequency curve; updating the load to be processed of the power equipment according to the maximum bearing load of the power equipment to obtain the target load of the power equipment; and adjusting the power equipment in the power system according to the target load.
The above-mentioned various modules in the safety control device for the electric power system based on the multi-space-time scale frequency can be fully or partially implemented by software, hardware and the combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data such as a current frequency curve, a historical frequency curve, a predicted frequency curve, a target frequency curve and the like. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method for power system safety control based on multiple spatio-temporal scale frequencies.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and the data (including but not limited to data for analysis, stored data, presented data, etc.) related to the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the related laws and regulations and standards of the related country and region.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.

Claims (10)

1. A method for safely controlling an electric power system based on a multi-time space scale frequency, the method comprising:
determining the curve similarity between a current frequency curve of the power system in a current time period and a historical frequency curve of the power system in a historical time period;
according to the curve similarity and the current frequency curve, predicting to obtain a predicted frequency curve of the power system in the future time period;
Performing fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and processing the candidate frequency curve according to the variance of the candidate frequency curve to obtain a target frequency curve of the power system, so as to regulate and control the power system according to the target frequency curve.
2. The method of claim 1, wherein blurring the predicted frequency profile to obtain a candidate frequency profile for the power system comprises:
fuzzification processing is carried out on the predicted frequency curve, so that a fuzzy frequency curve corresponding to the predicted frequency curve is obtained;
according to a fuzzy rule, fuzzy reasoning is carried out on the fuzzy frequency curve, and a fuzzy reasoning curve corresponding to the fuzzy frequency curve is obtained; the fuzzy rule is set according to the bearable frequency range of the power system;
and performing defuzzification processing on the fuzzy inference curve to obtain a candidate frequency curve of the power system.
3. The method according to claim 1, wherein the processing the candidate frequency profile according to the variance of the candidate frequency profile to obtain the target frequency profile of the power system comprises:
Judging whether the variance of the candidate frequency curve is larger than a preset variance threshold;
and under the condition that the variance of the candidate frequency curve is larger than the preset variance threshold, performing curve smoothing on the candidate frequency curve to obtain a target frequency curve corresponding to the candidate frequency curve.
4. The method according to claim 1, wherein predicting a predicted frequency curve of the power system in the future time period based on the curve similarity and the current frequency curve comprises:
determining the weight of the historical frequency curve according to the curve similarity;
and inputting the weights of the current frequency curve, the historical frequency curve and the historical frequency curve into a frequency time sequence prediction model to obtain a predicted frequency curve of the power system in the future time period.
5. The method of claim 1, wherein determining a curve similarity between a current frequency curve of the power system over a current time period and a historical frequency curve over a historical time period comprises:
performing dynamic time warping processing on the current frequency curve and the historical frequency curve to obtain a frequency data distance between the current frequency curve and the historical frequency curve;
And performing similarity conversion on the frequency data distance to obtain the curve similarity.
6. The method according to any one of claims 1 to 5, wherein said regulating said power system according to said target frequency profile comprises:
acquiring regulation and control weights of power equipment in the power system and the maximum bearing load of the power equipment;
according to the regulation weight, weighting the load capacity of the power system to obtain the load to be processed of the power equipment; the load capacity of the power system is calculated according to target frequency data in a target frequency curve;
updating the load to be processed of the power equipment according to the maximum bearing load of the power equipment to obtain the target load of the power equipment;
and adjusting power equipment in the power system according to the target load.
7. A multi-time space scale frequency based power system safety control device, the device comprising:
the similarity determining module is used for determining the curve similarity between the current frequency curve of the power system in the current time period and the historical frequency curve of the power system in the historical time period;
The frequency prediction module is used for predicting and obtaining a predicted frequency curve of the power system in the future time period according to the curve similarity and the current frequency curve;
the fuzzy processing module is used for carrying out fuzzy processing on the predicted frequency curve to obtain a candidate frequency curve of the power system;
and the frequency determining module is used for processing the candidate frequency curve according to the variance of the candidate frequency in the candidate frequency curve to obtain a target frequency curve of the power system so as to regulate and control the power system according to the target frequency curve.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
CN202310591995.2A 2023-05-23 2023-05-23 Power system safety control method and device based on multi-time space scale frequency Pending CN116845913A (en)

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