Photovoltaic power generation harmonic analysis processing system

Abstract

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation harmonic analysis and processing system, which comprises the steps of performing integrated analysis on harmonic data, power generation data and power consumption data, dividing harmonic data unit intervals, selecting a reference operation proportion according to a load ratio, and then calculating a power generation harmonic analysis result based on cycle division of historical power consumption data and local time characteristic value calculation. An inertial power consumption curve is accurately determined, then the power consumption intensity is monitored in real time, and the real-time power generation amount is determined, so that the system can timely obtain power generation dynamic information, and the inertial power consumption curve, the real-time power generation amount and a reference operation proportion are combined to accurately determine adjustment parameters. And finally, the automatic adjustment module accurately adjusts and controls the parameters of the photovoltaic equipment according to the adjustment parameters. According to the invention, adaptive adjustment can be carried out according to the actual power utilization and power generation conditions of the region, and the operation efficiency and stability of the photovoltaic power generation system are significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic power generation, and particularly relates to a photovoltaic power generation harmonic analysis processing system. BACKGROUND

[0002] Photovoltaic power generation is a technology that directly converts light energy into electrical energy by using the photovoltaic effect of the semiconductor interface. In recent years, photovoltaic power generation systems have rapidly developed, providing a large amount of clean energy to the power grid and injecting a large amount of harmonics into the power grid.

[0003] The prior art CN119010092A discloses a photovoltaic power generation system frequency intelligent adjustment method, which comprises calculating the deviation between the current output frequency of the photovoltaic power generation system and the power grid frequency, generating a preliminary adjustment strategy based on the frequency deviation through an adaptive PID control algorithm, performing preliminary frequency adjustment, evaluating the stability of the frequency adjustment in real-time monitoring and feedback, and dividing it into stable and unstable frequency adjustment according to the evaluation results. When the system is in unstable frequency adjustment, the harmonic data under load change is monitored in real time through a harmonic monitoring device, abnormal harmonic frequencies are analyzed and identified, and the adjustment strategy is dynamically adjusted according to the stability of the frequency adjustment and the harmonic frequency abnormality index in combination with fuzzy logic.

[0004] However, the photovoltaic power generation system generates harmonics during operation. These harmonics not only affect the power quality of the power system, but also cause damage to electrical equipment, reduce the service life of the equipment, and even cause safety accidents. At the same time, when the photovoltaic power generation system is adjusted and controlled, the power consumption characteristics and power generation conditions of the region cannot be accurately considered, resulting in poor adjustment effect and difficulty in achieving effective suppression of harmonics and stable operation of the system. SUMMARY

[0005] The purpose of the present application is to solve the problems in the background art and provide a photovoltaic power generation harmonic analysis processing system.

[0006] In order to achieve the above-mentioned purpose, the present application adopts the following technical scheme: A photovoltaic power generation harmonic analysis processing system, comprising: A harmonic analysis module is used to analyze the historical data of a target region, extract harmonic data from the historical data, divide the harmonic data into multiple unit intervals, integrate the power generation data and power consumption data corresponding to the harmonic data at the same time point to obtain a data set, calculate the load ratio according to the power generation data and power consumption data at the same time point, calculate the overlap ratio of the load ratio in the minimum unit interval in the remaining unit intervals, and select a reference operation ratio in the minimum unit interval according to the overlap ratio. The power consumption analysis module is configured to analyze historical power consumption data, divide the historical power consumption data according to a period of time, set a local time in the period of time, set the period of time as a cycle time, calculate a characteristic value of power consumption data in each local time, and determine an inertia power consumption curve of the target area based on the characteristic value. The light energy monitoring module is configured to monitor real-time power consumption intensity of the target area and determine a real-time power generation amount. The integrated processing module is configured to determine power consumption data of the target area at a current time according to the inertia power consumption curve, combine and analyze the power consumption data, the real-time power generation amount and a benchmark operation ratio, and determine an adjustment parameter. The automatic adjustment module is configured to adjust and control the photovoltaic device parameter according to the adjustment parameter.

[0007] As a further scheme of the present application, the method for dividing the unit interval comprises: S1: obtaining historical data in an effective time; extracting harmonic data in the historical data, sequentially arranging values of the obtained harmonic data in an ascending order to obtain a harmonic sequence, identifying a minimum value XBmin and a maximum value XBmax of the harmonic data in the harmonic sequence, and setting the minimum value and the maximum value as interval end point values in the data sequence, thereby obtaining a harmonic interval [XBmin, XBmax], wherein the minimum value of the harmonic data is a left end point of the harmonic interval, and the maximum value is a right end point of the harmonic interval; S2: setting a unit value d and obtaining a left end point of the harmonic interval, and dividing the harmonic interval into a plurality of unit intervals, i.e., [XBmin, 1×b], [1×b, 2×b], …, [(n-1)×b, n×b], [n×b, XBmax], according to the unit value, wherein the unit value b is set to 1%, and n is a positive integer.

[0008] As a further scheme of the present application, the method for obtaining the data set comprises: In the historical data, a time period in which each photovoltaic device is in an operation state in a corresponding period of time is identified and marked as an operation state time period. In the historical data, time points corresponding to each harmonic data are combined and marked, and corresponding remaining historical data in the time points, including power generation data and power consumption data, are identified, the historical data at the same time point are combined, and the combined data are marked. All unit intervals are obtained, harmonic data in the operation state time period in each period of time is identified, the harmonic data is sorted and summarized according to the interval range of the unit interval, and the harmonic data in the same unit interval is integrated into the same data set according to the combined data, at this time, each unit interval has a corresponding data set.

[0009] As a further scheme of the present application, the method for determining the reference operation ratio comprises: obtaining a minimum unit interval [XBmin, 1×b) and a corresponding data set, marking the data set as a target set, extracting combined data in the target set, and dividing power generation data in the same combined data by power consumption data to obtain a load ratio FZi, wherein i represents the label of different load ratios in the target set, and i∈[1, I] represents that there are I load ratios in the target set; obtaining the remaining unit interval and the corresponding data set, and re-integrating the obtained data set to obtain a whole set, and then processing the combined data in the whole set according to the above method, at this time, each combined data in the whole set has a corresponding load ratio FCj, j represents the label of different load ratios in the whole set, and j∈[1, J] represents that there are J load ratios in the whole set; arbitrarily selecting a load ratio FZi in the target set, setting the load ratio FZi as a reference value, identifying the reference value in the load ratio corresponding to the whole set, and counting the number Nu of individuals of the reference value, and then dividing the number Nu by the total number J of load ratios in the whole set, and marking the obtained value as the coincidence ratio Hi of the load ratio FZi, that is, Hi=Nu÷J; setting all load ratios FZi in the target set as reference values, and processing according to the above method to obtain the coincidence ratio Hi of each load ratio FZi in the whole set; obtaining the coincidence ratio Hi of all load ratios FZi in the target set, and identifying the minimum value of the coincidence ratio Hi, and marking the load ratio FZi corresponding to the minimum value of Hi as the reference operation ratio.

[0010] As a further scheme of the present application, the method for determining the inertia power consumption curve comprises: obtaining historical power consumption data in the historical data, and dividing the effective time according to fixed time to obtain a plurality of cycle times; At the same time, set the unit time, further divide each cycle time according to the unit time to obtain a plurality of local time periods; Then identify the collection time of the historical power consumption data, and according to the local time period, the historical power consumption data is summarized and arranged, and the historical power consumption data corresponding to each local time period is marked as time period data; Taking a single cycle time as a cycle, obtaining all cycle times, and aligning the cycle times according to the local time period; Taking local time as an analysis unit, obtaining time period data corresponding to the same local time in all periodic times, and performing mean value processing, the obtained mean value is marked as the representative value of the local time, and the representative value of all local times is calculated based on the above method; A two-dimensional plane coordinate system is set, the abscissa is set as the local time, and the ordinate is set as the power consumption, a curve graph is drawn according to the representative value of the local time, and the curve graph is marked as the inertia power consumption curve of the target area.

[0011] As a further scheme of the application, the fixed time is set as 1 day, and the periodic time is a natural day, and the unit time is set as 1 hour.

[0012] As a further scheme of the application, before performing mean value processing, it is necessary to identify and delete abnormal data in each time period data, and the remaining normal data is subjected to mean value processing to obtain the representative value corresponding to the local time, further, the identification method of abnormal data adopts the standard deviation method, comprising: Based on the formula The standard deviation multiple Z is obtained, De represents the power consumption data, represents the mean value of all time period data in the local time corresponding to the power consumption data De, represents the standard deviation of the time period data in the local time corresponding to the power consumption data De, and then The power consumption data De corresponding to ≥k is marked as abnormal data, and the power consumption data De corresponding to <k is marked as normal data.

[0013] As a further scheme of the application, the determination method of the adjustment parameter comprises: Identify the current time point, and determine the power consumption data Ph of the target area at the current time point in the inertia power consumption curve, multiply the power consumption data Ph by the reference operation ratio value to obtain the power generation demand value, subtract the real-time power generation amount from the power generation demand value, and mark the obtained difference value as the adjustment parameter.

[0014] As a further scheme of the application, it further comprises a data storage module for storing the basic data and historical data of the target area, the basic data comprising photovoltaic equipment data and regional distribution data of the target area, the historical data comprising historical power generation data, harmonic data and historical power consumption data of the target area, and then the data storage module is respectively connected with the harmonic analysis module and the power consumption analysis module in one-way communication.

[0015] Compared with the prior art, the application has the advantages that: The application integrates and analyzes the harmonic data and power generation and power consumption data, divides the harmonic data unit interval, selects the reference operation ratio according to the load ratio, then accurately determines the inertia power consumption curve based on the period division and local time characteristic value calculation of the historical power consumption data, then makes the system obtain the power generation dynamic information in time by real-time monitoring the power consumption intensity and determining the real-time power generation, combines the inertia power consumption curve, the real-time power generation and the reference operation ratio to accurately determine the adjustment parameter, and finally the automatic adjustment module accurately adjusts and controls the photovoltaic equipment parameter according to the adjustment parameter; the system realizes comprehensive and accurate analysis and processing of the photovoltaic power generation harmonic through the collaborative work of the modules, effectively improves the power quality of the power system, guarantees the safe and stable operation of the electrical equipment, and can also adaptively adjust according to the actual power consumption and power generation of the target area, significantly improves the operation efficiency and stability of the photovoltaic power generation system. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 It is a schematic diagram of the system structure of the application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments.

[0018] Referring to Figure 1 A photovoltaic power generation harmonic analysis and processing system, comprising a data storage module, a harmonic analysis module, a power consumption analysis module, an integrated processing module, a light energy monitoring module and an automatic adjustment module; The data storage module is used for storing the basic data and historical data of the target area, further, the basic data comprises photovoltaic equipment data and regional distribution data of the target area, the historical data comprises historical power generation data, harmonic data and historical power consumption data of the target area, then the data storage module is respectively connected with the harmonic analysis module and the power consumption analysis module in one-way communication; The harmonic analysis module is used for obtaining the historical data, and combining the historical power generation data and the historical power consumption data in the historical data with the harmonic data for comprehensive analysis to determine the reference operation ratio corresponding to the minimum harmonic value, and the specific determination method of the reference operation ratio comprises: S1: obtaining the historical data in the effective time, wherein the specific value of the effective time is set by the person skilled in the art according to the big data experience, and in this embodiment, the effective time is set to 6 months; The harmonic data in the historical data is extracted, and the values of the obtained harmonic data are sequentially arranged in ascending order to obtain a harmonic sequence, the minimum value XBmin and the maximum value XBmax of the harmonic data in the harmonic sequence are identified, and the minimum value and the maximum value are respectively set as the interval end point values in the data sequence, and then the harmonic interval [XBmin, XBmax] is obtained, wherein the minimum value of the harmonic data is the left end point of the harmonic interval, and the maximum value is the right end point of the harmonic interval; Further, in the embodiment, the harmonic data refers to the total harmonic distortion rate, and the smaller the total harmonic distortion rate is, the less the harmonic pollution is, and the better the performance of the photovoltaic power generation equipment is. S2: Set a unit value d, and obtain the left end point of the harmonic interval, and start from the left end point, divide the harmonic interval into a plurality of unit intervals according to the unit value, that is, [XBmin, 1×b], [1×b, 2×b], …, [(n-1)×b, n×b], [n×b, XBmax], wherein the specific value of the unit value b is set by a person skilled in the art according to big data experience, and in the embodiment, the unit value b is set to 1%, and n is a positive integer; S3: In the historical data, identify the time period when each photovoltaic device is in the running state in the corresponding cycle time, and mark it as the running state period. It needs to be further explained that the photovoltaic device generates electricity through the photovoltaic effect, that is, when photons irradiate on a semiconductor material, electrons are excited to generate an electromotive force, so as to convert light energy into electrical energy, when the photovoltaic device is in the standby state without light at night, the degree of association between the harmonic generation state and the photovoltaic power generation is weakened, and in the embodiment, the harmonic of the photovoltaic device in the standby state is not analyzed. Then, in the historical data, the time points corresponding to each harmonic data are combined and marked, and the corresponding remaining historical data in the time point is identified, including the power generation data and the power consumption data, the historical data at the same time point is combined, and marked as combined data. Get all the unit intervals, identify the harmonic data in the running state period in each cycle time, arrange and summarize the harmonic data according to the interval range of the unit interval, and integrate the combined data corresponding to the harmonic data in the same unit interval into the same data set according to the combined data, at this time, each unit interval has a corresponding data set; S4: Obtain the minimum unit interval [XBmin, 1×b) and the corresponding data set, mark the data set as a target set, extract the combined data in the target set, and divide the power generation data by the power consumption data in the same combined data to obtain the load ratio FZi, wherein i represents the label of different load ratios in the target set, and i∈[1, I], indicating that there are I load ratios in the target set. Afterwards, the remaining unit intervals and the corresponding data sets are acquired, and the acquired data sets are re-integrated to obtain an overall set. The combined data in the overall set is processed according to the above method. At this time, each combined data in the overall set has a corresponding load ratio FCj, j represents the label of different load ratios in the overall set, and j [1, J], indicating that there are J load ratios in the overall set; S5: In the target set, an arbitrary load ratio FZi is selected and set as a reference value. Then, the reference value is identified in the load ratios corresponding to the overall set, and the number of individuals Nu of the reference value is counted. Then, the number of individuals Nu is divided by the total number of load ratios J in the overall set, and the obtained value is marked as the coincidence ratio Hi of the load ratio FZi, that is, Hi=Nu÷J; Afterwards, all load ratios FZi in the target set are set as reference values, and are processed according to the above method to obtain the coincidence ratio Hi of each load ratio FZi in the overall set; S6: Obtain the coincidence ratio Hi of all load ratios FZi in the target set, and identify the minimum value of the coincidence ratio Hi. The load ratio FZi corresponding to the minimum value of Hi is marked as the reference operation ratio. Afterwards, the harmonic analysis module transmits the reference operation ratio to the integrated processing module; The power consumption analysis module is used to acquire historical power consumption data in the historical data, and based on the historical power consumption data, to determine the inertia power consumption curve of the target area. The specific method for determining the inertia power consumption curve includes: Acquire historical power consumption data in the historical data, and divide the effective time according to a fixed time to obtain a plurality of cycle times. The specific value of the fixed time is set by a person skilled in the art according to big data experience. In this embodiment, the fixed time is set to 1 day. Further, a cycle time is a natural day. At the same time, set a unit time, and further divide each cycle time according to the unit time to obtain a plurality of local time periods. In this embodiment, the unit time is set to 1 hour. Further, the local time period includes 0-1, 1-2, 2-3, and so on. Afterwards, the collection time of the historical power consumption data is identified, and the historical power consumption data is summarized and arranged according to the local time period. The historical power consumption data corresponding to each local time period is marked as time period data. Take a single cycle time as a cycle, acquire all cycle times, and align the cycle times according to the local time period; Using local time as the unit of analysis, time period data corresponding to the same local time in all periodic times are obtained and averaged. The resulting average is marked as the representative value of this local time. Based on the above method, the representative values ​​of all local times are calculated. It should be further noted that before averaging, it is necessary to identify and delete outlier data in each time period. The remaining normal data is then averaged to obtain the representative value of the corresponding local time. In this embodiment, the outlier identification method adopts the standard deviation method, based on the formula... The standard deviation Z is obtained, and De represents the power consumption data. This represents the average of all time periods within the local time corresponding to the power consumption data De. This represents the standard deviation of the time period data corresponding to the power consumption data De, and then... The power consumption data De corresponding to ≥k is marked as abnormal data, and will be... The power consumption data De corresponding to <k is marked as normal data. The specific value of k is set by those skilled in the art based on big data experience. In this embodiment, the value of k is set to 2. Set up a two-dimensional plane coordinate system, set the horizontal axis to local time and the vertical axis to power consumption, draw a curve based on the representative value of local time, and mark this curve as the inertial power consumption curve of the target area. Then, a one-way communication connection is established between the power consumption analysis module and the integrated processing module; The solar energy monitoring module is used to monitor the solar intensity of the target area in real time and convert the solar intensity into real-time power generation according to the equipment parameters of the photovoltaic equipment. Then, the solar energy monitoring module transmits the real-time power generation to the integrated processing module. The integrated processing module is used to analyze real-time power generation and inertial power consumption curves, and based on the baseline operating ratio, determines the equipment's adjustment parameters. The specific methods for determining the adjustment parameters include: Identify the current time point and determine the power consumption data Ph of the target area at the current time point in the inertial power consumption curve. Multiply the power consumption data Ph by the baseline operating ratio to obtain the power generation demand value. Then subtract the real-time power generation from the power generation demand value and mark the difference as the adjustment parameter. Then, a one-way communication connection is established between the integrated processing module and the automatic adjustment module, and the adjustment parameters are transmitted to the automatic adjustment module; The automatic adjustment module is used to receive adjustment parameters and adjust and control the parameters of the photovoltaic equipment according to the adjustment parameters, thereby minimizing the harmonic data generated during the photovoltaic power generation process.

[0019] The above merely describes preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art, according to the technical solution and inventive concept of the present application, makes equivalent replacement or change within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A photovoltaic power generation harmonic analysis and processing system, characterized in that, include: The harmonic analysis module is used to analyze historical data of the target area, extract harmonic data from the historical data, divide the harmonic data into multiple unit intervals, integrate the power generation data and power consumption data corresponding to the harmonic data at the same time point to obtain a data set, calculate the load ratio based on the power generation data and power consumption data at the same time point, calculate the overlap ratio of the load ratio in the smallest unit interval with the other unit intervals based on the load ratio, and select the benchmark operating ratio in the smallest unit interval based on the overlap ratio. The power consumption analysis module is used to analyze historical power consumption data. It divides the historical power consumption data into periods of time, sets local time within each period of time, sets the period of time as the cycle time, calculates the characteristic value of the power consumption data in each local time, and determines the inertial power consumption curve of the target area based on the characteristic value. The solar energy monitoring module is used to monitor the real-time power consumption intensity of the target area and determine the real-time power generation. The integrated processing module is used to determine the power consumption data of the target area at the current time based on the inertial power consumption curve, and to combine and analyze the power consumption data with the real-time power generation and the baseline operating ratio to determine the adjustment parameters. The automatic adjustment module is used to adjust and control the parameters of the photovoltaic equipment according to the adjustment parameters.

2. The photovoltaic power generation harmonic analysis and processing system according to claim 1, characterized in that, Methods for dividing unit intervals include: S1: Obtain historical data within the valid time period; Harmonic data is extracted from historical data, and the values ​​of the obtained harmonic data are arranged in ascending order to obtain a harmonic sequence. The minimum value XBmin and the maximum value XBmax of the harmonic data in the harmonic sequence are identified, and the minimum value and the maximum value are set as the interval endpoints in the data sequence, thereby obtaining the harmonic interval [XBmin, XBmax]. The minimum value of the harmonic data is the left endpoint of the harmonic interval, and the maximum value is the right endpoint of the harmonic interval. S2: Set the unit value d, and obtain the left endpoint of the harmonic interval. Starting from the left endpoint, divide the harmonic interval into several unit intervals according to the unit value, namely [XBmin, 1×b), [1×b, 2×b), ..., [(n-1)×b, n×b), [n×b, XBmax]. The unit value b is set to 1%, and n takes the value of a positive integer.

3. The photovoltaic power generation harmonic analysis and processing system according to claim 1, characterized in that, Methods for obtaining data sets include: In historical data, identify the time periods during which each photovoltaic device is in operation within the corresponding cycle time and mark them as operation period periods; In the historical data, the time point corresponding to each harmonic data is combined and marked. At the same time, the remaining historical data corresponding to this time point, including power generation data and power consumption data, are identified. The historical data at the same time point are combined and marked as combined data. All unit intervals are acquired, and harmonic data for the operating state period in each cycle time are identified. The harmonic data are organized and summarized according to the interval range of the unit interval. At the same time, according to the combined data, the combined data corresponding to the harmonic data in the same unit interval are integrated into the same data set. At this time, there is a corresponding data set for each unit interval.

4. The photovoltaic power generation harmonic analysis and processing system according to claim 3, characterized in that, The methods for determining the baseline operating ratio include: Obtain the smallest unit interval [XBmin, 1×b) and the corresponding data set, and mark this data set as the target set. Extract the combined data in the target set, and divide the power generation data in the same combined data by the power consumption data to obtain the load ratio FZi, where i represents the label of different load ratios in the target set, and i∈[1, I], indicating that there are I load ratios in the target set. Obtain the remaining unit intervals and the corresponding data sets, and then re-integrate the obtained data sets to obtain an overall set. Then, process the combined data in the overall set according to the above method. At this time, each combined data in the overall set has a corresponding load ratio FCj, where j represents the label of different load ratios in the overall set, and j∈[1,J], indicating that there are J load ratios in the overall set. Arbitrarily select a load ratio FZi in the target set and set this load ratio FZi as the benchmark value. Then identify the benchmark value in the load ratios corresponding to the overall set and count the number of individuals Nu where the benchmark value exists. Then divide the number of individuals Nu by the total number of load ratios J in the overall set and mark the resulting value as the overlap ratio Hi of this load ratio FZi, i.e. Hi=Nu÷J. Set all load ratios FZi in the target set as baseline values ​​and process them according to the above method to obtain the overlap ratio Hi of each load ratio FZi in the overall set; Obtain the overlap ratio Hi of all load ratios FZi in the target set, identify the minimum value of overlap ratio Hi, and mark the load ratio FZi corresponding to the minimum value of Hi as the baseline operating ratio.

5. The photovoltaic power generation harmonic analysis and processing system according to claim 1, characterized in that, Methods for determining the inertial power consumption curve include: Obtain historical power consumption data from historical data, and divide the effective time into several periodic time periods according to fixed time intervals; At the same time, a unit time is set, and each cycle time is further divided according to the unit time to obtain several local time periods; Then, the collection time of historical power consumption data is identified, and the historical power consumption data is summarized and organized according to local time periods. At the same time, the historical power consumption data corresponding to each local time period is marked as time period data. Each cycle time is used as a single time interval to obtain all cycle times, and the cycle times are aligned according to local time intervals. Using local time as the unit of analysis, the time period data corresponding to the same local time in all periodic times are obtained and averaged. The obtained average is marked as the representative value of this local time. Based on the above method, the representative values ​​of all local times are calculated. Set up a two-dimensional plane coordinate system, set the horizontal axis to local time and the vertical axis to power consumption. Plot a curve based on the representative value of local time and mark this curve as the inertial power consumption curve of the target area.

6. The photovoltaic power generation harmonic analysis and processing system according to claim 5, characterized in that, The fixed time is set to 1 day, and one cycle is one calendar day. The unit of time is set to 1 hour.

7. The photovoltaic power generation harmonic analysis and processing system according to claim 5, characterized in that, Before averaging, outliers in the data for each time period need to be identified and removed. The remaining normal data is then averaged to obtain the representative value for the corresponding local time. Furthermore, the outlier identification method uses the standard deviation method, including: Based on formula The standard deviation Z is obtained, and De represents the power consumption data. This represents the average of all time periods within the local time corresponding to the power consumption data De. This represents the standard deviation of the time period data corresponding to the power consumption data De, and then... The power consumption data De corresponding to ≥k is marked as abnormal data, and will be... The power consumption data De corresponding to <k is marked as normal data.

8. The photovoltaic power generation harmonic analysis and processing system according to claim 1, characterized in that, Methods for determining adjustment parameters include: Identify the current time point and determine the power consumption data Ph of the target area at the current time point in the inertial power consumption curve. Multiply the power consumption data Ph by the baseline operating ratio to obtain the power generation demand value. Subtract the real-time power generation from the power generation demand value and mark the resulting difference as the adjustment parameter.

9. The photovoltaic power generation harmonic analysis and processing system according to claim 1, characterized in that, It also includes a data storage module for storing basic and historical data of the target area. The basic data includes photovoltaic equipment data and regional distribution data of the target area, while the historical data includes historical power generation data, harmonic data, and historical power consumption data of the target area. After that, the data storage module establishes a one-way communication connection with the harmonic analysis module and the power consumption analysis module, respectively.

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