A power grid regulation system based on big data

Abstract

The present invention discloses a power grid regulation system based on big data, which relates to the technical field of power systems. It solves the technical problems that it is difficult to analyze the acquired data through various technical methods and extract corresponding features to obtain feature data, and it is also difficult to add the power value in the power output limit constraint condition to the comprehensive evaluation value of the power grid state obtained from the feature data. It includes the following modules: The power grid information acquisition module is used to acquire source-grid-load-storage data; the power grid constraint condition construction module is used to construct a power output limit constraint condition; the power grid big data analysis module is used to extract data such as the prediction value of the deviation degree of power consumption, the comprehensive power fluctuation degree, and the charge and discharge fluctuation degree of energy storage devices through various technical methods; the power prediction model module is used to obtain the comprehensive evaluation value of the power grid state according to the extracted data and power value; the power grid early warning regulation module is used to compare the comprehensive evaluation value of the power grid state with the early warning threshold.

Description

Technical Field

[0001] The present invention belongs to the technical field of power systems, and particularly relates to a power grid regulation system based on big data. Background Art

[0002] With the continuous development of technologies such as the Internet of Things and cloud computing, big data analysis technology has been widely applied in various fields. As one of the important infrastructures for the national economic development, the power system has gradually introduced big data analysis technology, which can obtain power grid information in real time and analyze data to achieve modeling prediction and high-level warning. For this reason, a power grid regulation system based on big data has emerged. By obtaining the source-grid-load-storage data, corresponding characteristic data are analyzed and extracted through various technical methods respectively. According to the Nyquist sampling theorem, power output limit constraint conditions are constructed. The characteristic data and the power value for judging whether the constraint conditions are satisfied are combined to obtain a comprehensive evaluation value of the power grid state, and the comprehensive evaluation value of the power grid state is compared with the warning threshold, realizing real-time monitoring, intelligent evaluation and warning of the power grid operation state.

[0003] Although the existing power regulation systems have achieved power regulation to a certain extent, the existing power regulation often relies on manual experience and simple data analysis, making it difficult to cope with the complex power grid operation states, difficult to analyze the obtained source-grid-load-storage data through various technical methods respectively and extract the corresponding characteristic data, and also difficult to construct power output limit constraint conditions according to the Nyquist sampling theorem, combine the characteristic data and the power value for judging whether the constraint conditions are satisfied to obtain a comprehensive evaluation value of the power grid state, lacking the comparison of the comprehensive evaluation value of the power grid state with the warning threshold and the analysis of the comparison results. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention proposes a power grid regulation system based on big data, which is used to solve the following technical problems:

[0005] Although the existing power regulation systems have achieved power regulation to a certain extent, the existing power regulation often relies on manual experience and simple data analysis, making it difficult to cope with the complex power grid operation states, difficult to analyze the obtained source-grid-load-storage data through various technical methods respectively and extract the corresponding characteristic data, and also difficult to construct power output limit constraint conditions according to the Nyquist sampling theorem, combine the characteristic data and the power value for judging whether the constraint conditions are satisfied to obtain a comprehensive evaluation value of the power grid state, lacking the comparison of the comprehensive evaluation value of the power grid state with the warning threshold and the analysis of the comparison results.

[0006] To solve the above problems, the first aspect of the present invention provides a power grid regulation system based on big data, including the following modules:

[0007] Grid information acquisition module: obtains the power supply side, grid side, load side and energy storage measurement data in the grid information, pre-processes the data, and uploads the pre-processed data to the cloud storage service;

[0008] Grid constraint condition building module: The sampling frequency is set by the Nyquist sampling theorem, and the power output limit constraint condition is built according to the signal and the number of sampling points to obtain the maximum and minimum power values;

[0009] Power grid big data analysis module: Receives data collected by the power grid information acquisition module, extracts features from the data using the time domain analysis method, and constructs a model based on the deviation degree of periodic power consumption and seasonal power consumption to obtain the predicted value of power consumption deviation degree;

[0010] The voltage fluctuation degree, current fluctuation degree and frequency fluctuation degree are assigned different weights and combined to obtain the comprehensive power fluctuation degree. The charging and discharging times are extracted using the sliding window technology and combined with the peak and valley values ​​extracted from the hourly data to obtain the charging and discharging fluctuation degree of the energy storage device.

[0011] Power forecast model module: By assigning corresponding weights to the power consumption deviation prediction value, the comprehensive power fluctuation degree and the energy storage equipment charging and discharging fluctuation degree data, weighted addition is performed, and the power value is combined to obtain the comprehensive evaluation value of the power grid status;

[0012] Power grid early warning and adjustment module: compares the comprehensive evaluation value of the power grid status with the early warning threshold and performs analysis;

[0013] The receiving power grid information acquisition module collects data, extracts features from the data using a time domain analysis method, and constructs a model to obtain a power consumption deviation prediction value by using the periodic power consumption deviation degree and the seasonal power consumption deviation degree, including the following steps:

[0014] Obtain the raw data of electricity consumption during peak hours, normal hours, and off-peak hours, as well as the total electricity consumption per day, week, and month, assign a timestamp to each raw data, sort the data points by timestamp, convert the raw data into a time series format, extract the key features of the time series through time domain analysis methods, including periodicity and seasonal fluctuations, identify the periodic components in the data by drawing a time series graph, and for each identified period, calculate the average electricity consumption within each period;

[0015] The degree of deviation of cycle power consumption is obtained by the ratio of power consumption on one day in the cycle to the average power consumption of the cycle;

[0016] The degree of seasonal electricity consumption deviation is obtained by the ratio of the average electricity consumption in each season to the average electricity consumption throughout the year.

[0017] As a further solution of the present invention, acquiring data on the power supply side, grid side, load side, and energy storage side of the power grid information, preprocessing the data, and uploading the preprocessed data to the cloud storage service includes the following steps:

[0018] For the power supply side, real-time power generation data, including power generation amount and power generation time, is collected by docking with the automation system interface of the power plant; for the grid side, grid operation data, including voltage, current, and frequency, is collected by intelligent sensor devices at each node of the grid; for the load side, user electricity consumption data, including electricity consumption during peak hours, normal hours, and off-peak hours, and total electricity consumption per day, week, and month, is collected by deploying intelligent electricity meters; for the energy storage side, operation data of the energy storage device, including the number of charging and discharging times of the battery, charging amount, and discharging amount, is obtained through the energy storage management system; the collected data is preprocessed, including removing outliers and filling in missing data, and converting data from different sources into a unified format; the processed data is uploaded to the cloud storage service, and the grid big data analysis module downloads the data from the cloud for analysis.

[0019] As a further solution of the present invention, setting the sampling frequency through the Nyquist sampling theorem, constructing power output limit constraint conditions based on the signal and the number of sampling points, and obtaining the maximum and minimum power values includes the following steps:

[0020] By sampling the signal, setting the sampling frequency according to the Nyquist sampling theorem, discretely sampling the analog signal using sampling software to obtain digital sample points, and calculating the power between each data point by dividing the sum of the squares of the signal by the number of sampling points;

[0021] Power calculation formula:

[0022]

[0023] Wherein, is the power corresponding to the sample point, is each sample point, is the index of the sample point, is the total number of sampling points;

[0024] Traverse all the calculated power values, and take the found maximum power value as the maximum power in signal processing, and take the found minimum power value as the minimum power in signal processing;

[0025] Construct power output limit constraint conditions through the obtained maximum and minimum power values:

[0026]

[0027] Among them, is the minimum power value, is the power value at a certain time point, is the maximum power value.

[0028] As a further solution of the present invention, the data collected by the power grid information acquisition module is received, and the time-domain analysis method is used to extract the features of the data. The predicted value of the power consumption deviation degree is obtained by constructing a model through the cycle power consumption deviation degree and the seasonal power consumption deviation degree. It further includes:

[0029] Calculation formula for the average cycle power consumption:

[0030]

[0031] Among them, is the average cycle power consumption, is the power consumption on the th day, is the total number of days;

[0032] Calculation formula for the cycle power consumption deviation degree:

[0033]

[0034] Among them, is the cycle power consumption deviation degree, is the power consumption on the th day, is the average cycle power consumption;

[0035] Calculation formula for the seasonal power consumption deviation degree:

[0036]

[0037] Among them, is the seasonal power consumption deviation degree. When = 1, is the average spring power consumption, is the spring power consumption deviation degree. When = 2, is the average summer power consumption, is the summer power consumption deviation degree. When = 3, is the average autumn power consumption, is the autumn power consumption deviation degree. When = 4, is the average winter power consumption, is the winter power consumption deviation degree, is the average annual power consumption;

[0038] Collect historical data including the deviation degree of periodic electricity consumption and the deviation degree of seasonal electricity consumption. After preprocessing the data, use the historical data to fit the ARIMAX model to obtain the preliminary form of the electricity consumption deviation model. Verify the model through white noise test. Repeat the fitting and verification process to obtain a trained model. Use the trained ARIMAX model to predict the new input data to obtain the predicted value of the electricity consumption deviation degree.

[0039] As a further solution of the present invention, the step of obtaining the comprehensive power fluctuation degree by combining the voltage fluctuation degree, the current fluctuation degree and the frequency fluctuation degree with different weights includes the following steps:

[0040] Collect power grid operation data, including voltage, current and frequency data, through intelligent sensor devices at each node of the power grid. Extract statistical features, including extreme values and average values, from the obtained data through descriptive statistical analysis. Obtain the voltage fluctuation degree, the current fluctuation degree and the frequency fluctuation degree respectively according to the statistical features and real-time data. Organize an expert team in the power field. According to the experts' experience in power system operation, detection and analysis, evaluate the relative importance of voltage, current and frequency in power grid stability, and determine the weight coefficients corresponding to the three fluctuation degrees;

[0041] Comprehensive power fluctuation degree calculation formula:

[0042]

[0043] Wherein, is the comprehensive power fluctuation degree, is the number of measurements, is the voltage value obtained from the th measurement, is the maximum voltage value, is the minimum voltage value, is the current value obtained from the th measurement, is the maximum current value, is the minimum current value, 、 and are the weight coefficients corresponding to voltage, current and frequency.

[0044] As a further solution of the present invention, the step of using the sliding window technique to extract the charge and discharge fluctuations of the energy storage device by combining the charge and discharge times with the peak and valley values extracted from the hourly data includes the following steps:

[0045] Collect data of energy storage devices at different time scales from the energy storage management system in real time, including key parameters such as the number of charging times, the number of discharging times, the charging amount, and the discharging amount of the energy storage devices. After cleaning the collected data, extract time-related features. Extract the peak and valley features of each day from the hourly data, and use the sliding window technique to extract the average values of the number of charging times and the number of discharging times within a continuous time period. Combine these with the peak and valley features extracted from the hourly data to obtain the charging and discharging fluctuation degree of the energy storage device;

[0046] Calculation formula for the charging and discharging fluctuation degree of the energy storage device:

[0047]

[0048] Among them, is the charging and discharging fluctuation degree of the energy storage device, is the valley value of electricity consumption on the th day, is the peak value of electricity consumption on the th day, is the total number of days, is the number of charging times on the th day, is the number of discharging times on the th day.

[0049] As a further solution of the present invention, by assigning corresponding weights to the prediction value of the degree of deviation of electricity consumption, the comprehensive power floating degree, and the charging and discharging fluctuation degree data of the energy storage device, perform weighted addition, and combine with the power value to obtain the comprehensive evaluation value of the power grid state, including the following steps:

[0050] After standardizing the three data of the degree of deviation of electricity consumption, the comprehensive power floating degree, and the charging and discharging fluctuation degree of the energy storage device, calculate the covariance matrix of the data to obtain the eigenvalues and eigenvectors of the covariance matrix. After determining the weights of each principal component according to the contribution rate of the eigenvalues, obtain the weight coefficients of each index;

[0051] Calculation formula for the comprehensive evaluation value of the power grid state:

[0052]

[0053] Among them, is the comprehensive evaluation value of the power grid state, is the prediction value of the degree of deviation of electricity consumption, is the comprehensive power floating degree, is the charging and discharging fluctuation degree data of the energy storage device, , and are the corresponding weight coefficients; among them, is the power value at a certain time point. When When the constraints are met, The value of is 0, when When the constraints are not met, is assigned a value of 1.

[0054] As a further solution of the present invention, the comprehensive evaluation value of the power grid state is compared with the warning threshold value and analyzed, including the following steps:

[0055] Based on historical data and grid operation experience, the 95th percentile of the comprehensive evaluation value of the grid status in historical data is selected as the warning threshold;

[0056] If the comprehensive evaluation value of the power grid status is ≥ the warning threshold, the system will issue a warning signal;

[0057] If the comprehensive evaluation value of the power grid status is less than the warning threshold, the system maintains real-time monitoring.

[0058] Beneficial effects of the present invention:

[0059] The present invention sets the sampling frequency through the Nyquist sampling theorem, constructs the power output limit constraint condition according to the signal and the number of sampling points, obtains the maximum and minimum power values, and determines the upper and lower limits of the constraint conditions; and uses the time domain analysis method, descriptive statistical analysis and sliding window technology to analyze and extract features of the acquired source grid load storage data to obtain a variety of feature data, including the predicted value of the degree of deviation of power consumption, the comprehensive power floating degree and the degree of charging and discharging fluctuation of the energy storage equipment, and combines the feature data and the power value to obtain the comprehensive evaluation value of the power grid state, thereby realizing comprehensive monitoring and comprehensive evaluation of the power grid state. According to the statistical analysis results of historical data and combined with the power grid operation experience, the 95% quantile of the comprehensive evaluation value of the power grid state in the historical data is selected as the early warning threshold, and the currently calculated comprehensive evaluation value of the power grid state is compared with the preset early warning threshold, thereby realizing the monitoring and early warning mechanism, so that when an abnormal situation occurs in the power grid, the early warning system can respond quickly and adjust the system in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 It is a module flow chart of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] See also Figure 1 As shown, the present invention is a power grid regulation system based on big data, comprising the following modules:

[0064] Grid information acquisition module: obtains the power supply side, grid side, load side and energy storage measurement data in the grid information, pre-processes the data, and uploads the pre-processed data to the cloud storage service;

[0065] Grid constraint condition building module: The sampling frequency is set by the Nyquist sampling theorem, and the power output limit constraint condition is built according to the signal and the number of sampling points to obtain the maximum and minimum power values;

[0066] Power grid big data analysis module: Receives data collected by the power grid information acquisition module, extracts features from the data using the time domain analysis method, and constructs a model based on the deviation degree of periodic power consumption and seasonal power consumption to obtain the predicted value of power consumption deviation degree;

[0067] The voltage fluctuation degree, current fluctuation degree and frequency fluctuation degree are assigned different weights and combined to obtain the comprehensive power fluctuation degree. The charging and discharging times are extracted using the sliding window technology and combined with the peak and valley values ​​extracted from the hourly data to obtain the charging and discharging fluctuation degree of the energy storage device.

[0068] Power forecast model module: By assigning corresponding weights to the power consumption deviation prediction value, the comprehensive power fluctuation degree and the energy storage equipment charging and discharging fluctuation degree data, weighted addition is performed, and the power value is combined to obtain the comprehensive evaluation value of the power grid status;

[0069] Power grid early warning and adjustment module: compares the comprehensive evaluation value of the power grid status with the early warning threshold and performs analysis;

[0070] The receiving power grid information acquisition module collects data, extracts features from the data using a time domain analysis method, and constructs a model to obtain a power consumption deviation prediction value by using the periodic power consumption deviation degree and the seasonal power consumption deviation degree, including the following steps:

[0071] Obtain the original data of electricity consumption during peak hours, normal hours, and off-peak hours, as well as the total daily, weekly, and monthly electricity consumption. Assign a timestamp to each original data point, sort the data points according to the timestamp, convert the original data into a time series format, extract the key features in the time series through time domain analysis methods, including periodic and seasonal fluctuations, identify the periodic components in the data by plotting the time series graph, and for each identified period, calculate the average electricity consumption within each period;

[0072] Obtain the degree of deviation of the periodic electricity consumption by calculating the ratio of the electricity consumption on one day within the period to the average electricity consumption of the period;

[0073] Obtain the degree of deviation of the seasonal electricity consumption by calculating the ratio of the average electricity consumption in each season to the average annual electricity consumption.

[0074] Specifically, obtain the corresponding data by installing devices on the power source side, grid side, load side, and energy storage side of the power grid respectively, preprocess the collected data, and upload the processed data to the cloud storage service. The grid big data analysis module needs to regularly download the latest data from the cloud for analysis; set the sampling frequency according to the Nyquist sampling theorem, calculate the power between each data point by dividing the sum of the squares of the signals by the number of sampling points, determine the maximum power value and the minimum power value, and construct the power output limit constraint conditions; fit the ARIMAX model with the historical data of the degree of deviation of the periodic electricity consumption and the degree of deviation of the seasonal electricity consumption to obtain the preliminary form of the electricity consumption deviation model, and conduct verification. Repeat the fitting and verification process to obtain a trained model. Use the trained ARIMAX model to predict the new input data to obtain the predicted value of the degree of deviation of the electricity consumption. Combine the peak-valley value feature and the average number of charge-discharge times feature to obtain a multi-dimensional feature data, and combine them respectively to obtain the voltage fluctuation degree, current fluctuation degree, and frequency fluctuation degree. Organize an expert team in the power field, and according to the experts' experience in the operation, detection, and analysis of the power system, evaluate the relative importance of voltage, current, and frequency in the power grid stability, and determine the weight coefficients corresponding to the three fluctuation degrees to describe the charge-discharge fluctuation degree of the energy storage device; combine the predicted value of the degree of deviation of the electricity consumption, the comprehensive power fluctuation degree, and the data of the charge-discharge fluctuation degree of the energy storage device with the power value to obtain the comprehensive evaluation value of the power grid state; compare the comprehensive evaluation value of the power grid state with the warning threshold and conduct analysis.

[0075] In one embodiment of the present invention, the steps of obtaining the data on the power source side, grid side, load side, and energy storage side in the power grid information, preprocessing the data, and uploading the preprocessed data to the cloud storage service include the following steps:

[0076] For the power supply side, power generation data is collected in real time by interfacing with the automation system of the power plant, including power generation volume and power generation time; for the power grid side, power grid operation data is collected by intelligent sensor devices at each node of the power grid, including voltage, current, and frequency; for the load side, user power consumption data is collected by deploying smart meters, including power consumption during peak hours, normal hours, and off-peak hours, as well as the total power consumption per day, week, and month; for the energy storage side, operation data of energy storage devices is obtained through an energy storage management system, including the number of charge and discharge cycles of the battery, charge amount, and discharge amount; the collected data is preprocessed, including removing outliers and filling in missing data, and converting data from different sources into a unified format; the processed data is uploaded to a cloud storage service, and the power grid big data analysis module downloads the data from the cloud for analysis.

[0077] Specifically, by installing devices on the power supply side, power grid side, load side, and energy storage side respectively to obtain corresponding data, the collected data is preprocessed, statistical methods are used to identify and remove outliers, and the missing data is filled in by median filling. Since the data comes from different sources and has different formats and units, data conversion is performed so that all data is represented in a unified format and unit. A cloud storage space is set, and the processed data is uploaded to the cloud storage service. The power grid big data analysis module needs to regularly download the latest data from the cloud for analysis.

[0078] In one embodiment of the present invention, the sampling frequency is set by the Nyquist sampling theorem, and power output limit constraint conditions are constructed based on the signal and the number of sampling points to obtain the maximum and minimum power values, including the following steps:

[0079] By sampling the signal, the sampling frequency is set according to the Nyquist sampling theorem, and the analog signal is discretely sampled using sampling software to obtain digital sample points. The power between each data point is obtained by calculating the sum of the squares of the signal divided by the number of sampling points.

[0080] Power calculation formula:

[0081]

[0082] Wherein, is the power corresponding to the sample point, is each sample point, is the index of the sample point, is the total number of sampling points;

[0083] Traverse all the calculated power values, and take the found maximum power value as the maximum power in signal processing, and take the found minimum power value as the minimum power in signal processing;

[0084] Construct a power output limit constraint condition based on the obtained maximum power value and minimum power value:

[0085]

[0086] Among them, is the minimum power value, is the power value at a certain time point, is the maximum power value.

[0087] Specifically, set the sampling frequency according to the Nyquist sampling theorem, use sampling software to discretely sample the analog signal to obtain digital sample points, each sample point represents the amplitude value of the signal at a specific time point, calculate the power between each data point by dividing the sum of the squares of the signal by the number of sampling points, determine the maximum power value and the minimum power value, and construct a power output limit constraint condition.

[0088] In one embodiment of the present invention, the data collected by the receiving grid information acquisition module is used, and the time-domain analysis method is used to extract the features of the data, and the predicted value of the power consumption deviation degree is obtained by constructing a model through the power consumption deviation degree in the cycle and the power consumption deviation degree in the season. It also includes:

[0089] Calculation formula for the average power consumption in a cycle:

[0090]

[0091] Among them, is the average power consumption in a cycle, is the power consumption on the th day, is the total number of days;

[0092] Calculation formula for the power consumption deviation degree in a cycle:

[0093]

[0094] Among them, is the power consumption deviation degree in a cycle, is the power consumption on the th day, is the average power consumption in a cycle;

[0095] Calculation formula for the power consumption deviation degree in a season:

[0096]

[0097] Among them, is the power consumption deviation degree in a season. When = 1, is the average power consumption in spring, is the deviation degree of spring electricity consumption. When = 2, is the average summer electricity consumption, is the deviation degree of summer electricity consumption. When = 3, is the average autumn electricity consumption, is the deviation degree of autumn electricity consumption. When = 4, is the average winter electricity consumption, is the deviation degree of winter electricity consumption, is the annual average electricity consumption;

[0098] Collect historical data including the deviation degree of periodic electricity consumption and the deviation degree of seasonal electricity consumption. After preprocessing the data, use the historical data to fit the ARIMAX model to obtain the preliminary form of the electricity consumption deviation model. Verify the model through white noise test. Repeat the fitting and verification process to obtain the trained model. Use the trained ARIMAX model to predict the new input data to obtain the predicted value of the electricity consumption deviation degree.

[0099] Specifically, obtain the electricity consumption data at different time periods from the power grid operation system, assign a timestamp to each original data point and sort them to determine the timeliness of the data. Use the time domain analysis method to extract the periodic components in the time series, analyze the seasonal fluctuations in the time series. Obtain the deviation degree of periodic electricity consumption according to the ratio of the electricity consumption on one day within the period to the average electricity consumption of the period. Obtain the deviation degree of seasonal electricity consumption through the ratio of the average electricity consumption of each season to the annual average electricity consumption. Use the historical data to fit the ARIMAX model to obtain the preliminary form of the electricity consumption deviation model. Verify the model through white noise test. Repeat the fitting and verification process to obtain the trained model. Use the trained ARIMAX model to predict the new input data to obtain the predicted value of the electricity consumption deviation degree.

[0100] In one embodiment of the present invention, the step of obtaining the comprehensive power fluctuation degree by combining the voltage fluctuation degree, the current fluctuation degree and the frequency fluctuation degree with different weights includes the following steps:

[0101] Collect the power grid operation data including voltage, current and frequency data through the intelligent sensor devices at each node of the power grid. Extract the statistical features including extreme values and averages from the obtained data through descriptive statistical analysis. Obtain the voltage fluctuation degree, the current fluctuation degree and the frequency fluctuation degree respectively according to the combination of the statistical features and the real-time data. Organize an expert team in the power field. According to the experts' experience in the operation, detection and analysis of the power system, evaluate the relative importance of voltage, current and frequency in the power grid stability and determine the weight coefficients corresponding to the three fluctuation degrees;

[0102] The calculation formula of comprehensive power fluctuation degree is:

[0103]

[0104] in, The comprehensive power fluctuation degree, is the number of measurements, For the The voltage value obtained by the measurement is is the maximum voltage value, is the minimum voltage value, For the The current value obtained by the measurement is is the maximum current value, is the minimum current value, For the The frequency value obtained by the measurement is is the maximum frequency value, is the minimum frequency value, , and are the weight coefficients corresponding to voltage, current and frequency.

[0105] Specifically, the grid operation data, including voltage, current and frequency data, are collected by intelligent sensor devices at each node of the grid. Statistical features are extracted from the acquired data through descriptive statistical analysis. The maximum and minimum values ​​of voltage, current and frequency are extracted from historical data to understand the extreme conditions of grid operation. The average values ​​of voltage, current and frequency are calculated to reflect the overall level of grid operation. The corresponding floating degree is calculated according to the extreme values ​​and average values ​​of voltage, current and frequency. An expert team in the power field is organized to evaluate the relative importance of voltage, current and frequency in grid stability based on the experts' experience in power system operation, detection and analysis. The weight coefficients corresponding to the three floating degrees are determined, and the three floating degrees are weighted averaged according to their respective weights to obtain the comprehensive power floating degree. , and are the weight coefficients corresponding to voltage, current and frequency, is 0.3, is 0.5, is 0.2.

[0106] In one embodiment of the present invention, the method of extracting the number of charge and discharge times using the sliding window technology and the peak and valley values ​​extracted from the hourly data to obtain the degree of charge and discharge fluctuation of the energy storage device includes the following steps:

[0107] Real-time collect data of energy storage devices at different time scales from the energy storage management system, including key parameters such as the number of charging times, discharging times, charging amount, and discharging amount of the energy storage device. After cleaning the collected data, extract time-related features. Extract the peak and valley features of each day from the hourly data, and use the sliding window technique to extract the average values of the number of charging times and discharging times within a continuous time period. Combine the peak and valley features extracted from the hourly data to obtain the charging and discharging fluctuation degree of the energy storage device;

[0108] After standardizing the three data of the degree of deviation of power consumption, the degree of comprehensive power fluctuation, and the charging and discharging fluctuation degree of the energy storage device, calculate the covariance matrix of the data to obtain the eigenvalues and eigenvectors of the covariance matrix. After determining the weights of each principal component according to the contribution rate of the eigenvalues, obtain the weight coefficients of each index;

[0109] Calculation formula for the charging and discharging fluctuation degree of the energy storage device:

[0110]

[0111] Among them, is the charging and discharging fluctuation degree of the energy storage device, is the valley value of power consumption on the day, is the peak value of power consumption on the day, is the total number of charging times on the day, is the total number of discharging times on the

[0112] Specifically, real-time collect data of energy storage devices at different time scales from the energy storage management system, clean the collected data to remove outliers and noise, process the hourly data, obtain the peak and valley features of the daily power consumption through the hourly data, aggregate the hourly data into daily data, calculate the total number of charging times and total discharging times per day, use the sliding window technique to move on the time series, calculate the average values of the number of charging times and discharging times within each window, and combine the peak and valley value features and the average value features of the charging and discharging times to obtain a multi-dimensional feature data for describing the charging and discharging fluctuation degree of the energy storage device.

[0113] In one embodiment of the present invention, by assigning corresponding weights to the predicted value of the degree of deviation of power consumption, the degree of comprehensive power fluctuation, and the data of the charging and discharging fluctuation degree of the energy storage device, performing weighted addition, and combining with the power value to obtain the comprehensive evaluation value of the power grid state, including the following steps:

[0114] Calculation formula for the comprehensive evaluation value of the power grid state:

[0115]

[0116] in, is the comprehensive evaluation value of the power grid status, is the predicted value of the power consumption deviation degree, The comprehensive power fluctuation degree, It is the charging and discharging fluctuation data of energy storage equipment. , and is the corresponding weight coefficient; among them, is the power value at a certain time point, when When the constraints are met, The value of is 0, when When the constraints are not met, is assigned a value of 1.

[0117] Specifically, different weights are assigned to the data of power consumption deviation degree, comprehensive power floating degree and energy storage equipment charging and discharging fluctuation degree, and the power values ​​are added together to obtain the comprehensive evaluation value of the power grid state. According to the power output restriction constraints, the maximum output power and minimum output power of the model are determined, and it is judged whether the power value meets the constraint conditions. Different values ​​are assigned to the power value according to the two situations of satisfaction and non-satisfaction, and the comprehensive evaluation value of the power grid state is obtained. is 0.4, is 0.5, is 0.5.

[0118] In one embodiment of the present invention, the comparing the comprehensive evaluation value of the power grid state with the warning threshold and analyzing the same comprises the following steps:

[0119] Based on historical data and grid operation experience, the 95th percentile of the comprehensive evaluation value of the grid status in historical data is selected as the warning threshold;

[0120] If the comprehensive evaluation value of the power grid status is ≥ the warning threshold, the system will issue a warning signal;

[0121] If the comprehensive evaluation value of the power grid status is less than the warning threshold, the system maintains real-time monitoring.

[0122] Specifically, based on the statistical analysis results of historical data and combined with the power grid operation experience, the 95% quantile of the comprehensive evaluation value of the power grid status in the historical data is selected as the warning threshold, and the currently calculated comprehensive evaluation value of the power grid status is compared with the preset warning threshold. If the comprehensive evaluation value is greater than or equal to the warning threshold, the system will issue a warning signal; if the comprehensive evaluation value is less than the warning threshold, the system will maintain real-time monitoring.

[0123] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A power grid regulation system based on big data, characterized in that: Includes the following modules: Grid information acquisition module: obtains the power supply side, grid side, load side and energy storage measurement data in the grid information, pre-processes the data, and uploads the pre-processed data to the cloud storage service; Grid constraint condition building module: The sampling frequency is set by the Nyquist sampling theorem, and the power output limit constraint condition is built according to the signal and the number of sampling points to obtain the maximum and minimum power values; Power grid big data analysis module: Receives data collected by the power grid information acquisition module, extracts features from the data using the time domain analysis method, and constructs a model based on the periodic power consumption deviation degree and the seasonal power consumption deviation degree to obtain the power consumption deviation degree prediction value; The voltage fluctuation degree, current fluctuation degree and frequency fluctuation degree are assigned different weights and combined to obtain the comprehensive power fluctuation degree. The charging and discharging times are extracted using the sliding window technology and combined with the peak and valley values ​​extracted from the hourly data to obtain the charging and discharging fluctuation degree of the energy storage device. Power forecasting model module: By assigning corresponding weights to the power consumption deviation prediction value, the comprehensive power fluctuation degree and the energy storage equipment charging and discharging fluctuation degree data, weighted addition is performed, and the power value is combined to obtain the comprehensive evaluation value of the power grid status; Power grid early warning and adjustment module: compares the comprehensive evaluation value of the power grid status with the early warning threshold and performs analysis; The receiving power grid information acquisition module collects data, extracts features from the data using a time domain analysis method, and constructs a model to obtain a power consumption deviation prediction value by using the periodic power consumption deviation degree and the seasonal power consumption deviation degree, including the following steps: Obtain the raw data of electricity consumption during peak hours, normal hours, and off-peak hours, as well as the total electricity consumption per day, week, and month, assign a timestamp to each raw data, sort the data points by timestamp, convert the raw data into a time series format, extract the key features of the time series through time domain analysis methods, including periodicity and seasonal fluctuations, identify the periodic components in the data by drawing a time series graph, and for each identified period, calculate the average electricity consumption within each period; The degree of deviation of cycle power consumption is obtained by the ratio of power consumption on one day in the cycle to the average power consumption of the cycle; The degree of seasonal electricity consumption deviation is obtained by the ratio of the average electricity consumption in each season to the average electricity consumption throughout the year.

2. The power grid regulation system based on big data according to claim 1, characterized in that: The method of obtaining the power supply side, grid side, load side and energy storage measurement data in the grid information, preprocessing the data, and uploading the preprocessed data to the cloud storage service includes the following steps: On the power supply side, power generation data, including power generation and power generation time, are collected in real time by connecting to the power plant's automation system interface. On the grid side, grid operation data, including voltage, current and frequency, are collected by intelligent sensor devices at each node of the grid. On the load side, user electricity consumption data, including power consumption during peak, normal and off-peak hours and total daily, weekly and monthly electricity consumption, are collected by deploying smart meters. For energy storage measurement, the operation data of energy storage equipment is obtained through the energy storage management system, including the number of battery charges, discharges, charge and discharge amounts. The collected data is pre-processed, including removing outliers and filling in missing data, and converting data from different sources into a unified format. The processed data is uploaded to the cloud storage service, and the grid big data analysis module downloads the data from the cloud for analysis.

3. The power grid regulation system based on big data according to claim 1, characterized in that: The method of setting the sampling frequency by the Nyquist sampling theorem, constructing the power output limit constraint condition according to the signal and the number of sampling points, and obtaining the maximum and minimum power values ​​includes the following steps: By sampling the signal, setting the sampling frequency according to the Nyquist sampling theorem, using sampling software to discretize the analog signal to obtain digital sample points, and calculating the square sum of the signal divided by the number of sampling points to obtain the power between each data point; Power calculation formula: ; in, is the power corresponding to the sample point, For each sample point, is the index of the sample point, is the total number of sampling points; Traverse all the calculated power values ​​and find the maximum power value As the maximum power in signal processing, the minimum power value found As the minimum power in signal processing; The power output limit constraint is constructed by obtaining the maximum power value and the minimum power value: ; in, is the minimum power value, is the power value at a certain time point, is the maximum power value.

4. The power grid regulation system based on big data according to claim 1, characterized in that: The receiving module receives data collected by the power grid information acquisition module, extracts features from the data using a time domain analysis method, and constructs a model to obtain a predicted value of the power consumption deviation degree through the periodic power consumption deviation degree and the seasonal power consumption deviation degree, and also includes: The calculation formula of average power consumption in a cycle is: ; in, is the average power consumption during the period, For the Daily electricity consumption, is the total number of days; The calculation formula of the deviation degree of cycle power consumption is: ; in, is the degree of deviation of cycle power consumption, For the Daily electricity consumption, is the average power consumption during the period; Calculation formula for seasonal electricity consumption deviation: ; in, is the seasonal electricity consumption deviation degree, when =1, is the average electricity consumption in spring, is the deviation degree of electricity consumption in spring. =2, is the average electricity consumption in summer, is the degree of deviation of electricity consumption in summer. =3, is the average electricity consumption in autumn, is the deviation degree of electricity consumption in autumn. =4, is the average electricity consumption in winter, is the deviation degree of winter electricity consumption, is the average annual electricity consumption; Historical data including the deviation degree of periodic electricity consumption and seasonal electricity consumption are collected. After preprocessing the data, the ARIMAX model is fitted using the historical data to obtain the preliminary form of the electricity consumption deviation model. The model is verified by white noise test, and the fitting and verification process is repeated to obtain a trained model. The trained ARIMAX model is used to predict new input data to obtain the predicted value of the electricity consumption deviation degree.

5. The power grid regulation system based on big data according to claim 1, characterized in that: The step of assigning different weights to the voltage floating degree, the current floating degree and the frequency floating degree to obtain the comprehensive power floating degree comprises the following steps: By collecting grid operation data, including voltage, current and frequency data, through intelligent sensor devices at each node of the grid, statistical features, including extreme values ​​and average values, are extracted from the acquired data through descriptive statistical analysis. The voltage fluctuation degree, current fluctuation degree and frequency fluctuation degree are obtained based on the statistical features and real-time data. An expert team in the power field is organized to evaluate the relative importance of voltage, current and frequency in grid stability based on the experts' experience in power system operation, detection and analysis, and determine the weight coefficients corresponding to the three fluctuation degrees. The calculation formula of comprehensive power fluctuation degree is: ; in, The comprehensive power fluctuation degree, is the number of measurements, For the The voltage value obtained by the measurement is is the maximum voltage value, is the minimum voltage value, For the The current value obtained by the measurement is is the maximum current value, is the minimum current value, For the The frequency value obtained by the measurement is is the maximum frequency value, is the minimum frequency value, , and are the weight coefficients corresponding to voltage, current and frequency.

6. The power grid regulation system based on big data according to claim 1, characterized in that: The method of extracting the number of charge and discharge times using the sliding window technology and the peak and valley values ​​extracted from the hourly data to obtain the degree of charge and discharge fluctuation of the energy storage device includes the following steps: Collect data of energy storage devices on different time scales in real time from the energy storage management system, including key parameters of the energy storage devices, such as the number of charges, discharges, charge capacity, and discharge capacity. After cleaning the collected data, extract time-related features, extract daily peak and valley features from hourly data, and use sliding window technology to extract the average value of the number of charges and discharges in a continuous time period and combine it with the peak and valley features extracted from hourly data to obtain the degree of fluctuation in the charge and discharge of energy storage devices. The calculation formula for the fluctuation degree of charging and discharging of energy storage equipment is: ; in, is the charging and discharging fluctuation degree of the energy storage device. For the The peak value of electricity consumption per day, For the Peak electricity consumption per day, is the total number of days, For the Number of charges per day, For the The number of discharges per day.

7. The power grid regulation system based on big data according to claim 1, characterized in that: The method allocates corresponding weights to the power consumption deviation degree prediction value, the comprehensive power floating degree and the energy storage device charging and discharging fluctuation degree data, performs weighted addition, and obtains the comprehensive evaluation value of the power grid state by combining the power values, including the following steps: After standardizing the three data of power consumption deviation, comprehensive power fluctuation and energy storage equipment charging and discharging fluctuation, the covariance matrix of the data is calculated to obtain the eigenvalues ​​and eigenvectors of the covariance matrix. After determining the weights of each principal component according to the contribution rate of the eigenvalue, the weight coefficients of each indicator are obtained. Calculation formula for comprehensive evaluation value of power grid status: ; in, is the comprehensive evaluation value of the power grid status, is the predicted value of the power consumption deviation degree, The comprehensive power fluctuation degree, It is the charging and discharging fluctuation data of energy storage equipment. , and is the corresponding weight coefficient; among them, is the power value at a certain time point, when When the constraints are met, The value of is 0, when When the constraints are not met, is assigned a value of 1.

8. The power grid regulation system based on big data according to claim 1, characterized in that: The method of comparing the comprehensive evaluation value of the power grid state with the early warning threshold and analyzing the same comprises the following steps: Based on historical data and grid operation experience, the 95th percentile of the comprehensive evaluation value of the grid status in historical data is selected as the warning threshold; If the comprehensive evaluation value of the power grid status is ≥ the warning threshold, the system will issue a warning signal; If the comprehensive evaluation value of the power grid status is less than the warning threshold, the system maintains real-time monitoring.

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