Collaborative optimization method for active participation of new energy power supply in power grid frequency regulation

Abstract

The invention discloses a collaborative optimization method for a new energy power supply to actively participate in power grid frequency regulation, and particularly relates to the technical field of new energy, which comprises a basic information input step, a monitoring time division step, a power supply data acquisition step, a power supply data analysis step, a comprehensive analysis step, a comprehensive judgment processing step and an early warning step. According to the method, a power generation characteristic coefficient value, a power grid frequency fluctuation characteristic coefficient value, an electronic equipment operation coefficient value and a power grid load dynamic coefficient value are calculated by utilizing a plurality of analysis models through data acquired by power supply data, and a comprehensive analysis model is established to obtain a comprehensive power supply rationality index. And after the index is compared with a preset value, early warning and collaborative optimization adjustment are performed according to a result. The method effectively deals with the fluctuation of the new energy power supply, predicts the power fluctuation trend in advance, and reduces the impact on the power grid frequency; the adjustment accuracy and efficiency are improved, and the equipment loss and the maintenance cost are reduced; the utilization efficiency of energy storage equipment is improved, and the charge-discharge process is scientifically managed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of new energy, more particularly, it relates to a collaborative optimization method for new energy power source actively participating in grid frequency regulation. BACKGROUND

[0002] Under the background of global energy transformation, new energy has a continuously increasing share in the power supply system due to its clean and sustainable characteristics. Solar, wind, and water energy power sources have gradually become important forces in grid power supply. However, the large-scale access of new energy power sources has brought new challenges to grid operation.

[0003] Traditional grids mainly rely on conventional power sources such as thermal power and hydroelectric power to maintain stable operation. The power generation of these power sources is relatively stable, and the regulation mechanism is mature. However, new energy power sources have significant intermittency and volatility. Taking solar power as an example, its power generation depends on the intensity of light, and the power generation will fluctuate greatly at different times of the day and under the influence of cloud cover. Wind power generation is affected by wind speed and direction, and unstable weather conditions make it difficult to stabilize wind power output. This unstable power generation characteristic can cause fluctuations in grid frequency.

[0004] However, in actual use, it still has some disadvantages, such as: problems caused by the characteristics of new energy power sources: the intermittency and volatility of new energy power sources are difficult to overcome, and their power generation cannot be accurately predicted and stably controlled. This makes the grid frequency always face the risk of fluctuation, seriously affecting the quality and stability of grid power supply; limitations of traditional power generation equipment adjustment: traditional power generation equipment has slow response speed and cannot keep up with the rapid changes in new energy power. From adjusting the power generation instruction to actually changing the power, it often takes a long time, making it difficult for the grid frequency to quickly recover to stability when the new energy power suddenly changes. Moreover, frequent adjustment of the output of traditional power generation equipment will accelerate equipment wear and tear, increase maintenance costs, and shorten the service life of the equipment; defects of monitoring and management systems: the existing monitoring system data collection is not comprehensive, only focusing on a few key parameters such as grid frequency and new energy power, ignoring other data closely related to grid frequency regulation.

[0005] Therefore, there is an urgent need for a collaborative optimization method for new energy power source actively participating in grid frequency regulation to solve the above problems. SUMMARY

[0006] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a collaborative optimization method for new energy power source actively participating in grid frequency regulation, which is in the technical field of new energy, to solve the problems raised in the above background.

[0007] To achieve the above object, the application provides the following technical scheme: a new energy power source actively participates in the coordinated optimization method of power grid frequency regulation, comprising: basic information input step, monitoring time division step, power source data acquisition step, power source data analysis step, comprehensive analysis step, comprehensive judgment processing step and early warning step: S1: basic information input: used for importing the basic information of the new energy power source into the database through analysis; S2: monitoring time division: used for determining the monitoring time of the new energy power source sample as a target time region, dividing the target time region into each sub-region by the equal time division method, and marking them as 1, 2, …, n in turn; S3: power source data acquisition: used for acquiring the power generation characteristic data, power grid frequency fluctuation characteristic data, electronic equipment operation data and power grid load dynamic data of each sub-time region, and transmitting the acquired data to the power source data analysis step; S4: power source data analysis: used for analyzing the data transmitted by the power source data acquisition step, and transmitting the analysis result to the comprehensive analysis step; comprising a power generation characteristic analysis unit, a power grid frequency analysis unit, an electronic equipment operation analysis unit and a power grid load analysis unit; S5: comprehensive analysis: used for establishing a comprehensive analysis model, importing the data transmitted by the power source data analysis step into the comprehensive analysis model, calculating the comprehensive power source rationality index of each sub-region, and transmitting it to the comprehensive judgment processing step; comprising a comprehensive analysis unit; S6: comprehensive judgment processing: used for establishing a comprehensive power source rationality index preset value, judging the comprehensive power source rationality index by the comprehensive power source rationality index preset value, and transmitting the judgment result to the early warning step; S7: early warning: used for receiving the early warning signal and performing coordinated optimization adjustment by the relevant management personnel.

[0008] Preferably, the power generation characteristic data includes photovoltaic panel temperature, energy storage battery charge amount and power source power ramp rate, and is marked as Dj, Dc and De respectively, the power grid frequency fluctuation characteristic data includes power grid frequency change rate, frequency harmonic content and frequency fluctuation period, and is marked as Wc, Wz and Wt respectively, the electronic equipment operation data includes inverter switching loss, converter DC side voltage and filter impedance frequency, and is marked as Kj, Kc and Kv respectively, and the power grid load dynamic data includes load power mutation frequency, load power day-night change difference and power grid frequency, and is marked as Fj, Fc and Fk respectively.

[0009] Preferably, the power generation characteristic analysis unit is used for establishing a power generation characteristic analysis model, importing the data processed by the power source data acquisition step into the power generation characteristic analysis model, and calculating the power generation characteristic coefficient value of each sub-region, which is specifically represented as: , D i represents the power generation characteristic coefficient value of the i-th sub-region, Dj i-1 represents the photovoltaic panel temperature of the i-1-th sub-region, Dj i represents the photovoltaic panel temperature of the i-th sub-region, Dj i represents the energy storage battery charge amount of the i-th sub-region, Dj i represents the power supply power ramp rate of the i-th sub-region, n represents the total number of sub-region groups.

[0010] Preferably, the power grid frequency analysis unit is used to establish a power grid frequency analysis model, and the data obtained by the power supply data acquisition step is introduced into the power grid frequency analysis model to calculate the power grid frequency fluctuation characteristic coefficient value of each sub-region, which is specifically represented as: , W i represents the power grid frequency fluctuation characteristic coefficient value of the i-th sub-region, Wc i represents the power grid frequency change rate of the i-th sub-region, Wz i represents the frequency harmonic content of the i-th sub-region, Wt i represents the frequency fluctuation period of the i-th sub-region, n represents the total number of sub-region groups.

[0011] Preferably, the electronic device operation analysis unit is used to establish an electronic device operation analysis model, and the data obtained by the power supply data acquisition step is introduced into the electronic device operation analysis model to calculate the electronic device operation coefficient value of each sub-region, which is specifically represented as: , K i represents the electronic device operation coefficient value of the i-th sub-region, Kj i represents the inverter switching loss of the i-th sub-region, Kc i represents the converter DC side voltage of the i-th sub-region, Kv i represents the filter impedance frequency of the i-th sub-region, n represents the number of sub-regions.

[0012] Preferably, the power grid load analysis unit is used to establish a power grid load analysis model, and the data obtained by the power supply data acquisition step is introduced into the power grid load analysis model to calculate the power grid load dynamic coefficient value of each sub-region, which is specifically represented as: , F i represents the power grid load dynamic coefficient value of the i-th sub-region, Fj i represents the load power mutation frequency of the i-th sub-region, Fci represents the load power diurnal variation difference of the i-th sub-region, Fk i represents the grid frequency of the i-th sub-region, and n represents the number of sub-regions.

[0013] Preferably, the comprehensive analysis unit is used to establish a comprehensive analysis model, import the power data analysis data into the comprehensive analysis model, and calculate a comprehensive power rationality index, which is specifically represented as: wherein P represents the comprehensive power rationality index, D i represents the power generation characteristic coefficient value of the i-th sub-region, W i represents the grid frequency fluctuation characteristic coefficient value of the i-th sub-region, K i represents the electronic equipment operation coefficient value of the i-th sub-region, F i represents the grid load dynamic coefficient value of the i-th sub-region, and α1, α2, α3, and α4 represent weight coefficients and are all greater than zero, α1, α2, α3, and α4 are equal to one, wherein μ represents other influence factors of the comprehensive power rationality index.

[0014] Preferably, the preset comprehensive power rationality index is an average rational value obtained according to the comprehensive evaluation of previous years; and the specific discrimination is as follows: B1: if the comprehensive power rationality index is greater than the preset comprehensive power rationality index, a warning signal is sent out; B2: if the comprehensive power rationality index is less than the preset comprehensive power rationality index, a good signal is sent out.

[0015] Technical effects and advantages of the present application: 1. The present application can track the power generation state change of new energy power supply in real time by collecting power generation characteristic data such as photovoltaic panel temperature, energy storage battery charge amount, and power supply power ramp rate, calculate the power generation characteristic coefficient value by using the power generation characteristic analysis model, comprehensively evaluate the power generation stability of new energy power supply, predict the power fluctuation trend in advance, gain time for grid frequency regulation, and reduce the impact of new energy power fluctuation on grid frequency; 2. The present application can more accurately judge the influence degree of new energy power supply on grid frequency by comprehensively analyzing various data and calculating a comprehensive power rationality index. According to these accurate data, the output adjustment of traditional power generation equipment can be reasonably arranged, the accuracy and efficiency of regulation can be improved, the unnecessary regulation times of traditional power generation equipment can be reduced, the equipment wear and maintenance cost can be reduced, and the equipment service life can be prolonged; ​3. This invention comprehensively collects multi-dimensional data on power generation characteristics, grid frequency fluctuations, electronic equipment operation, and grid load dynamics, and utilizes multiple analytical models to conduct in-depth analysis of this data. This not only provides a comprehensive understanding of the operating status of new energy power sources and the grid, but also calculates a comprehensive power source rationality index through integrated analytical models, providing comprehensive and accurate decision support for grid frequency regulation, improving the intelligence level of the grid monitoring and management system, and ensuring the safe and stable operation of the grid. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the method structure of the present invention.

[0017] Figure 2 This is a schematic diagram of the power data analysis structure of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] As attached Figure 1 The diagram shows a structural schematic of a collaborative optimization method for new energy power sources to actively participate in grid frequency regulation, including S1-S7.

[0020] As attached Figure 2 The diagram shown is a schematic diagram of the power data analysis structure.

[0021] S1: Basic Information Input: Used to import basic information of new energy power sources into the database through analysis.

[0022] In this embodiment, it should be specifically explained that the basic information of the new energy power source is imported into the database through analysis, which specifically includes: first, obtaining and organizing the basic information of the new energy power source through the APP; then, relevant management personnel log in to the database through QR code and verification code; and finally, generating the corresponding new energy information and importing it into the database.

[0023] S2: Monitoring time division: used to determine the monitoring time of new energy power samples as the target time area. The target time area is divided into sub-regions by equal time division and marked as 1, 2...n in sequence.

[0024] S3: Power data collection: used to collect power generation characteristic data, power grid frequency fluctuation characteristic data, electronic device operation data and power grid load dynamic data of each sub-time region, and transmit the collected data to the power data analysis step.

[0025] In this embodiment, it is specifically pointed out that the power generation characteristic data includes photovoltaic panel temperature, energy storage battery charge amount and power power ramp rate, which are marked as Dj, Dc and De respectively, the power grid frequency fluctuation characteristic data includes power grid frequency change rate, frequency harmonic content and frequency fluctuation period, which are marked as Wc, Wz and Wt respectively, the electronic device operation data includes inverter switching loss, converter DC side voltage and filter impedance frequency, which are marked as Kj, Kc and Kv respectively, and the power grid load dynamic data includes load power mutation frequency, load power day-night change difference and power grid frequency, which are marked as Fj, Fc and Fk respectively.

[0026] S4: Power data analysis: used to analyze the data transmitted by the power data collection step, and transmit the analysis result to the comprehensive analysis step; including a power generation characteristic analysis unit, a power grid frequency analysis unit, an electronic device operation analysis unit and a power grid load analysis unit.

[0027] In this embodiment, it is specifically pointed out that the intelligent model includes: a power generation characteristic analysis model, a power grid frequency analysis model, an electronic device operation analysis model and a power grid load analysis model.

[0028] In this embodiment, it is specifically pointed out that the power generation characteristic analysis unit is used to establish a power generation characteristic analysis model, and the data obtained by processing the power data collection step is imported into the power generation characteristic analysis model, and the power generation characteristic coefficient value of each sub-region is calculated, which is specifically represented as: , D i represents the power generation characteristic coefficient value of the i-th sub-region, Dj i-1 represents the photovoltaic panel temperature of the i-1-th sub-region, Dj i represents the photovoltaic panel temperature of the i-th sub-region, Dc i represents the energy storage battery charge amount of the i-th sub-region, De i represents the power power ramp rate of the i-th sub-region, and n represents the total number of sub-region groups.

[0029] In this embodiment, it is specifically pointed out that the power grid frequency analysis unit is used to establish a power grid frequency analysis model, and the data obtained by processing the power data collection step is imported into the power grid frequency analysis model, and the power grid frequency fluctuation characteristic coefficient value of each sub-region is calculated, which is specifically represented as: , W i Wc represents the grid frequency fluctuation characteristic coefficient value of the i-th sub-region i Wz represents the grid frequency change rate of the i-th sub-region i Wt represents the frequency harmonic content of the i-th sub-region i n represents the total number of sub-region groups.

[0030] In this embodiment, it is specifically pointed out that the electronic device operation analysis unit is used to establish an electronic device operation analysis model, and the data obtained by processing the power data collection step is imported into the electronic device operation analysis model, and the electronic device operation coefficient value of each sub-region is calculated, which is specifically represented as: , K i Kj represents the electronic device operation coefficient value of the i-th sub-region i Kc represents the inverter switching loss of the i-th sub-region i Kv represents the converter DC side voltage of the i-th sub-region i n represents the number of sub-regions.

[0031] In this embodiment, it is specifically pointed out that the grid load analysis unit is used to establish a grid load analysis model, and the data obtained by processing the power data collection step is imported into the grid load analysis model, and the grid load dynamic coefficient value of each sub-region is calculated, which is specifically represented as: , F i Fj represents the grid load dynamic coefficient value of the i-th sub-region i Fc represents the load power sudden change frequency of the i-th sub-region i Fk represents the load power day-night change difference value of the i-th sub-region i n represents the number of sub-regions.

[0032] S5: Comprehensive analysis: used to establish a comprehensive analysis model, and the data transmitted by the power data analysis step is imported into the comprehensive analysis model, and the comprehensive power rationality index of each sub-region is calculated and transmitted to the comprehensive judgment processing step; including a comprehensive analysis unit.

[0033] In this embodiment, it is specifically pointed out that the comprehensive analysis unit is used to establish a comprehensive analysis model, and the data of the power data analysis is imported into the comprehensive analysis model, and the comprehensive power rationality index is calculated, which is specifically represented as: , Wherein P represents the comprehensive power supply rationality index, D i represents the power generation characteristic coefficient value of the i-th sub-region, W i represents the power grid frequency fluctuation characteristic coefficient value of the i-th sub-region, K i represents the electronic device operation coefficient value of the i-th sub-region, F i represents the power grid load dynamic coefficient value of the i-th sub-region, α1, α2, α3, α4 represent weight coefficients and are all greater than zero, α1, α2, α3, α4 and equal to one, wherein μ represents other influence factors of the comprehensive power supply rationality index.

[0034] S6: Comprehensive judgment processing: obtain a preset comprehensive power supply rationality index, compare the comprehensive power supply rationality index with the preset comprehensive power supply rationality index, output a digital signal after comparison, and transmit the digital signal to the early warning step.

[0035] In this embodiment, it is specifically pointed out that the preset comprehensive power supply rationality index is an average reasonable value obtained according to the comprehensive evaluation of previous years; the specific discrimination is as follows: B1: If the comprehensive power supply rationality index is greater than the preset comprehensive power supply rationality index, an early warning signal is sent out; B2: If the comprehensive power supply rationality index is less than the preset comprehensive power supply rationality index, a good signal is sent out.

[0036] The digital signal includes: an early warning signal and a good signal.

[0037] S7: Early warning: used for receiving an early warning signal and performing collaborative optimization adjustment by relevant management personnel.

[0038] In this embodiment, it is specifically pointed out that when the early warning signal is received, the method will perform collaborative adjustment and optimization processing according to the specific situation of the power supply.

[0039] The application can track the generation state change of the new energy power supply in real time by collecting power generation characteristic data. The generation stability of the new energy power supply is comprehensively evaluated by calculating the generation characteristic coefficient value by using the generation characteristic analysis model, and the power fluctuation trend is predicted in advance, so as to gain time for grid frequency regulation, reduce the impact of new energy power fluctuation on the grid frequency, calculate the comprehensive power supply rationality index by comprehensively analyzing various data, and more accurately judge the influence degree of the new energy power supply on the grid frequency. According to the accurate data, the output adjustment of the traditional power generation equipment is reasonably arranged, the accuracy and efficiency of the adjustment are improved, the unnecessary adjustment times of the traditional power generation equipment are reduced, the equipment wear and maintenance cost are reduced, and the service life of the equipment is prolonged. By comprehensively collecting multi-dimensional data such as power generation characteristics, grid frequency fluctuation, electronic equipment operation and grid load dynamics, and using multiple analysis models to deeply analyze the data, not only the operation state of the new energy power supply and the grid can be comprehensively mastered, but also the comprehensive power supply rationality index can be calculated by the comprehensive analysis model, the comprehensive and accurate decision support for the grid frequency regulation can be provided, the intelligent level of the grid monitoring and management system is improved, and the safe and stable operation of the grid is ensured.

[0040] Secondly, only the structures related to the disclosed embodiments are involved in the drawings of the disclosed embodiments, other structures can be referred to the general design, and the same embodiments and different embodiments of the application can be combined with each other under the condition of no conflict. The above only describes the preferred embodiments of the application and is not used to limit the application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the application should be included in the protection scope of the application.

Claims

1. A collaborative optimization method for active participation of new energy power sources in grid frequency regulation, characterized in that, include: S1: Basic Information Input: Used to import basic information of new energy power sources into the database through analysis; S2: Monitoring time division: used to determine the monitoring time of new energy power samples as the target time area, and divide the target time area into sub-regions by equal time division, and mark them as 1, 2...n in sequence; S3: Power Data Acquisition: Used to collect power generation characteristic data, grid frequency fluctuation characteristic data, electronic equipment operation data, and grid load dynamic data for each sub-time zone, and transmit the collected data to the power data analysis step; S4: Power Data Analysis: Used to analyze the data transmitted in the power data acquisition step and transmit the analysis results to the comprehensive analysis step; It includes a power generation characteristic analysis unit, a power grid frequency analysis unit, an electronic equipment operation analysis unit, and a power grid load analysis unit; S5: Comprehensive Analysis: Used to establish a comprehensive analysis model, import the data transmitted from the power data analysis step into the comprehensive analysis model, calculate the comprehensive power rationality index of each sub-region, and transmit it to the comprehensive judgment and processing step. Includes a comprehensive analysis unit; S6: Comprehensive Judgment and Processing: Obtain the preset comprehensive power supply rationality index, compare the comprehensive power supply rationality index with the preset comprehensive power supply rationality index, output a digital signal, and transmit the digital signal to the early warning step; S7: Early Warning: Used to receive early warning signals and for relevant management personnel to coordinate and optimize adjustments.

2. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The power generation characteristic data includes photovoltaic panel temperature, energy storage battery charge, and power ramp-up rate, labeled as Dj, Dc, and De, respectively. The grid frequency fluctuation characteristic data includes grid frequency change rate, frequency harmonic content, and frequency fluctuation period, labeled as Wc, Wz, and Wt, respectively. The electronic equipment operation data includes inverter switching losses, converter DC side voltage, and filter impedance frequency, labeled as Kj, Kc, and Kv, respectively. The grid load dynamic data includes load power mutation frequency, load power diurnal variation difference, and grid frequency, labeled as Fj, Fc, and Fk, respectively.

3. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The power generation characteristic analysis unit is used to establish a power generation characteristic analysis model. It imports the data obtained from the power data acquisition step into the power generation characteristic analysis model and calculates the power generation characteristic coefficient values ​​for each sub-region, specifically as follows: ， D i Dj represents the power generation characteristic coefficient value of the i-th sub-region. i-1 Dj represents the temperature of the photovoltaic panel in the (i-1)th sub-region. i Dc represents the temperature of the photovoltaic panel in the i-th sub-region. i De represents the charge of the energy storage battery in the i-th sub-region. i represents the power ramp-up rate of the i-th sub-region, and n represents the total number of sub-region groups.

4. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The power grid frequency analysis unit is used to establish a power grid frequency analysis model. It imports the data obtained from the power data acquisition step into the power grid frequency analysis model and calculates the power grid frequency fluctuation characteristic coefficient values ​​for each sub-region, specifically as follows: ， W i Wc represents the characteristic coefficient value of the power grid frequency fluctuation in the i-th sub-region. i Wz represents the rate of change of the power grid frequency in the i-th sub-region. i Wt represents the frequency harmonic content of the i-th sub-region. i Let represent the frequency fluctuation period of the i-th sub-region, and n represent the total number of sub-region groups.

5. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The electronic device operation analysis unit is used to establish an electronic device operation analysis model. It imports the data obtained from the power data acquisition step into the electronic device operation analysis model and calculates the electronic device operation coefficient values ​​for each sub-region, specifically as follows: ， K i Kj represents the electronic equipment operating coefficient value of the i-th sub-region. i Kc represents the inverter switching loss in the i-th sub-region. i Kv represents the DC-side voltage of the converter in the i-th sub-region. i Let n represent the filter impedance frequency of the i-th sub-region, and n represent the number of sub-regions.

6. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The power grid load analysis unit is used to establish a power grid load analysis model. It imports the data obtained from the power source data acquisition step into the power grid load analysis model and calculates the dynamic coefficient values ​​of the power grid load for each sub-region, specifically as follows: ， F i Fj represents the dynamic coefficient value of the power grid load in the i-th sub-region. i Fc represents the frequency of load power abrupt changes in the i-th sub-region. i Fk represents the diurnal variation of load power in the i-th sub-region. i Let n represent the power grid frequency of the i-th sub-region, and n represent the number of sub-regions.

7. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The comprehensive analysis unit is used to establish a comprehensive analysis model, import the power data analysis data into the comprehensive analysis model, and calculate the comprehensive power rationality index, which is specifically expressed as follows: ， Where P represents the comprehensive power supply rationality index, and D... i W represents the power generation characteristic coefficient value of the i-th sub-region. i K represents the characteristic coefficient value of the power grid frequency fluctuation in the i-th sub-region. i F represents the electronic device operating coefficient value of the i-th sub-region. i Let α1, α2, α3, and α4 represent the dynamic coefficient of the power grid load in the i-th sub-region, and α1, α2, α3, and α4 represent the weight coefficients, all of which are greater than zero. The sum of α1, α2, α3, and α4 is equal to one, where μ represents other influencing factors of the comprehensive power supply rationality index.

8. The collaborative optimization method for active participation of new energy power sources in grid frequency regulation according to claim 1, characterized in that: The preset comprehensive power supply rationality index is an average rational value derived from comprehensive assessments over the years; the specific criteria are as follows: B1: If the comprehensive power supply rationality index is greater than the preset comprehensive power supply rationality index, an early warning signal will be issued; B2: If the overall power supply rationality index is less than the preset overall power supply rationality index, a good signal will be issued.

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