A photovoltaic power generation harmonic analysis processing system

Abstract

The present application relates to photovoltaic power generation technical field, especially a kind of photovoltaic power generation harmonic analysis processing system, including harmonic data and power generation, power consumption data integration analysis, division harmonic data unit interval, according to load ratio selection reference operation proportion, then based on the period division and local time characteristic value calculation of historical power consumption data, accurately determine inertia power consumption curve, then through real-time monitoring power consumption intensity and determine real-time power generation, make system can obtain power generation dynamic information in time, inertia power consumption curve, real-time power generation and reference operation proportion are combined, accurately determine adjustment parameter, finally automatic adjustment module is accurately adjusted and controlled according to adjustment parameter to photovoltaic equipment parameter;The present application can be self-adaptive adjustment according to the actual power consumption and power generation of region, significantly improves the operation efficiency and stability of photovoltaic power generation system.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation technology, and in particular to a photovoltaic power generation harmonic analysis and processing system. Background Technology

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

[0003] The prior art CN119010092A discloses a method for intelligent frequency regulation of a photovoltaic power generation system. This method includes calculating the deviation between the current output frequency of the photovoltaic power generation system and the grid frequency; generating a preliminary regulation strategy based on the frequency deviation using an adaptive PID control algorithm; performing preliminary frequency adjustment; evaluating the stability of the frequency regulation during real-time monitoring and feedback; classifying the frequency regulation into stable and unstable types based on the evaluation results; and, when the system is in unstable frequency regulation, monitoring harmonic data under load changes in real-time using harmonic monitoring equipment, analyzing and identifying abnormal harmonic frequencies, and dynamically adjusting the regulation strategy based on the stability of the frequency regulation and the harmonic frequency anomaly index using fuzzy logic.

[0004] However, photovoltaic power generation systems generate harmonics during operation. These harmonics not only affect the power quality of the power system, but may also damage electrical equipment, reduce equipment lifespan, and even cause safety accidents. At the same time, when regulating and controlling photovoltaic power generation systems, it is impossible to accurately consider the regional power consumption characteristics and power generation situation, resulting in poor regulation effect and difficulty in effectively suppressing harmonics and ensuring stable system operation. Summary of the Invention

[0005] The purpose of this invention is to solve the problems in the background art by proposing a photovoltaic power generation harmonic analysis and processing system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A photovoltaic power generation harmonic analysis and processing system, comprising:

[0008] 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.

[0009] The power consumption analysis module is used to analyze historical power consumption data. It divides the historical power consumption data into periodic time, sets local time within the periodic time, sets the periodic time as the loop time, calculates the representative value of power consumption data in each local time, and determines the inertial power consumption curve of the target area based on the representative value.

[0010] 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.

[0011] 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.

[0012] The automatic adjustment module is used to adjust and control the parameters of the photovoltaic equipment according to the adjustment parameters.

[0013] As a further aspect of the present invention, the method for dividing the unit interval includes:

[0014] S1: Obtain historical data within the valid time period;

[0015] 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.

[0016] S2: Set the unit value b, 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.

[0017] As a further aspect of the present invention, the method for acquiring the data set includes:

[0018] 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;

[0019] 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.

[0020] 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.

[0021] As a further aspect of the present invention, the method for determining the baseline operating ratio includes:

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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;

[0026] Obtain the overlap ratio Hi of all load ratios FZi in the target set, identify the minimum value of the overlap ratio Hi, and mark the load ratio FZi corresponding to the minimum value of Hi as the baseline operating ratio.

[0027] As a further aspect of the present invention, the method for determining the inertial power consumption curve includes:

[0028] Obtain historical power consumption data from historical data, and divide the effective time into several periodic time periods according to fixed time intervals;

[0029] 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.

[0030] 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. At the same time, the historical power consumption data corresponding to each local time is marked as time period data.

[0031] Each cycle time is used as a single loop to obtain all cycle times, and the cycle times are aligned according to local time.

[0032] 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.

[0033] 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.

[0034] As a further aspect of the present invention, the fixed time is set to 1 day, one cycle time is one natural day, and the unit time is set to 1 hour.

[0035] As a further aspect of the present invention, before performing averaging, it is necessary to identify and delete outlier data in each time period, and then perform averaging on the remaining normal data to obtain the representative value for the corresponding local time. Furthermore, the outlier identification method employs the standard deviation method, including:

[0036] 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.

[0037] As a further aspect of the present invention, the method for determining the adjustment parameter includes:

[0038] 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.

[0039] As a further embodiment of the present invention, a data storage module is also included, which is used to store basic data and historical data of the target area. The basic data includes photovoltaic equipment data and regional distribution data of the target area, and the historical data includes historical power generation data, harmonic data and historical power consumption data of the target area. Then, the data storage module establishes a one-way communication connection with the harmonic analysis module and the power consumption analysis module respectively.

[0040] Compared with existing technologies, the advantages of this invention are:

[0041] This invention integrates and analyzes harmonic data with power generation and consumption data, divides harmonic data unit intervals, selects a benchmark operating ratio based on the load ratio, and then accurately determines the inertial power consumption curve based on the periodic division of historical power consumption data and the calculation of local time representative values. By real-time monitoring of power consumption intensity and determination of real-time power generation, the system can acquire dynamic power generation information in a timely manner. Combining the inertial power consumption curve, real-time power generation, and benchmark operating ratio, the system accurately determines adjustment parameters. Finally, the automatic adjustment module precisely adjusts and controls the photovoltaic equipment parameters based on these parameters. Through the collaborative work of its modules, this system achieves comprehensive and accurate analysis and processing of photovoltaic power generation harmonics, effectively improving the power quality of the power system, ensuring the safe and stable operation of electrical equipment, and enabling adaptive adjustment based on the actual power consumption and generation conditions of the target area, significantly improving the operating efficiency and stability of the photovoltaic power generation system. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0043] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0044] Reference Figure 1 A photovoltaic power generation harmonic analysis and processing system includes 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.

[0045] The data storage module is used to store basic data and historical data of the target area. The basic data includes photovoltaic equipment data and regional distribution data of the target area, and the historical data includes historical power generation data, harmonic data and historical power consumption data of the target area. Then, the data storage module establishes a one-way communication connection with the harmonic analysis module and the power consumption analysis module respectively.

[0046] The harmonic analysis module is used to acquire historical data and combine historical power generation and consumption data with harmonic data for comprehensive analysis to determine the baseline operating ratio corresponding to the minimum harmonic value. The specific methods for determining the baseline operating ratio include:

[0047] S1: Obtain historical data within the valid time period. The specific value of the valid time period is set by those skilled in the art based on big data experience. In this embodiment, the valid time period is set to 6 months.

[0048] 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.

[0049] Furthermore, in this embodiment, the harmonic data refers to the total harmonic distortion rate (THD), and the smaller the THD, the less harmonic pollution and the better the performance of the photovoltaic power generation equipment.

[0050] S2: Set the unit value b, and simultaneously 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 specific value of the unit value b is set by those skilled in the art based on big data experience. In this embodiment, the unit value b is set to 1%, and n takes the value of a positive integer.

[0051] S3: 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;

[0052] It should be further explained that photovoltaic equipment generates electricity through the photovoltaic effect, that is, when photons irradiate semiconductor materials, they excite electrons to generate electromotive force, thereby converting light energy into electrical energy. When the photovoltaic equipment is at night without light, it is in standby mode. At this time, the correlation between the harmonic generation state and photovoltaic power generation is weakened. Therefore, in this embodiment, the harmonics of the photovoltaic equipment in standby mode will not be analyzed.

[0053] Then, in the historical data, the time point corresponding to each harmonic data is combined and marked, and 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.

[0054] All unit intervals are acquired, harmonic data of the running state period in each cycle time are identified, harmonic data are organized and summarized according to the interval range of the unit interval, and combined data corresponding to 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.

[0055] S4: 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.

[0056] Then, the remaining unit intervals and corresponding data sets are obtained, and the obtained data sets are re-integrated to obtain an overall set. Then, the combined data in the overall set are processed 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.

[0057] S5: Randomly 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 that exist at the benchmark value. 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.

[0058] Then, all load ratios FZi in the target set are set as baseline values ​​and processed according to the above method to obtain the overlap ratio Hi of each load ratio FZi in the overall set;

[0059] S6: Obtain the overlap ratio Hi of all load ratios FZi in the target set, identify the minimum value of overlap ratio Hi, mark the load ratio FZi corresponding to the minimum value of Hi as the reference operating ratio, and then the harmonic analysis module transmits the reference operating ratio to the integrated processing module.

[0060] The power consumption analysis module is used to acquire historical power consumption data from historical data, and based on this historical power consumption data, to determine the inertial power consumption curve for the target area. The specific methods for determining the inertial power consumption curve include:

[0061] Historical power consumption data is obtained from historical data, and the effective time is divided into several periodic time periods according to fixed time. The specific value of the fixed time period is set by those skilled in the art based on big data experience. In this embodiment, the fixed time period is set to 1 day. Furthermore, one periodic time period is one natural day.

[0062] 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 times. In this embodiment, the unit time is set to 1 hour. Furthermore, the local times include 0:00~1:00, 1:00~2:00, 2:00~3:00, ...;

[0063] 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. At the same time, the historical power consumption data corresponding to each local time is marked as time period data.

[0064] Each cycle time is used as a single loop to obtain all cycle times, and the cycle times are aligned according to local time.

[0065] 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.

[0066] 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.

[0067] Then, a one-way communication connection is established between the power consumption analysis module and the integrated processing module;

[0068] 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.

[0069] 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:

[0070] 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.

[0071] 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;

[0072] 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.

[0073] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A photovoltaic power generation harmonic analysis processing system characterized by comprising: The method comprises the following steps: The harmonic analysis module is used for analyzing the historical data of the target area, extracting the harmonic data in the historical data, and dividing the harmonic data into multiple unit intervals; the power generation data and the power consumption data corresponding to the harmonic data at the same time point are integrated to obtain a data set; the load ratio is calculated according to the power generation data and the power consumption data at the same time point; the overlapping proportion of the load ratio in the minimum unit interval in the remaining unit intervals is calculated according to the load ratio; and the reference operation proportion is selected in the minimum unit interval according to the overlapping proportion. The determination method of the reference operation proportion comprises the following steps: The minimum unit interval [XBmin, 1×b) and the corresponding data set are obtained, and the data set is marked as a target set; the combined data in the target set is extracted; the load ratio FZi is obtained by dividing the power generation data by the power consumption data in the same combined data, wherein i represents the label of the different load ratios in the target set, and i∈[1, I] represents that there are I load ratios in the target set; The remaining unit intervals and the corresponding data sets are obtained, and the data sets are re-integrated to obtain a whole set; then the combined data in the whole set is processed 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 the different load ratios in the whole set, and j∈[1, J] represents that there are J load ratios in the whole set; An arbitrary load ratio FZi in the target set is selected, and the load ratio FZi is set as a reference value; then the reference value is identified in the load ratios corresponding to the whole set, and the number Nu of the reference value is counted; then the number Nu is divided by the total number J of the load ratios in the whole set, and the obtained value is marked as the overlapping proportion Hi of the load ratio FZi, that is, Hi=Nu÷J; All the load ratios FZi in the target set are set as reference values, and the above method is used for processing to obtain the overlapping proportion Hi of each load ratio FZi in the whole set; The overlapping proportions Hi of all the load ratios FZi in the target set are obtained, and the minimum value of the overlapping proportions Hi is identified; the load ratio FZi corresponding to the minimum value of the overlapping proportions Hi is marked as the reference operation proportion; The power consumption analysis module is used for analyzing the historical power consumption data, dividing the historical power consumption data according to the cycle time, setting a local time in the cycle time, setting the cycle time as a cycle time, calculating the representative value of the power consumption data in each local time, and determining the inertia power consumption curve of the target area based on the representative value; The light energy monitoring module is used for monitoring the light intensity of the target area in real time, and converting the light intensity into real-time power generation according to the equipment parameters of the photovoltaic equipment; The integrated processing module is used for determining the power consumption data of the target area at the current time according to the inertia power consumption curve, combining and analyzing the power consumption data, the real-time power generation and the reference operation proportion, and determining the adjustment parameter; The automatic adjustment module is used for adjusting and controlling the photovoltaic equipment parameters according to the adjustment parameter.

2. The photovoltaic power generation harmonic analysis processing system according to claim 1, characterized by, The division method of the unit interval comprises the following steps: S1: obtaining historical data in an effective time; 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 interval end point values in the data sequence, thereby obtaining a harmonic interval [XBmin, XBmax], 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; S2: setting a unit value b, and obtaining the left end point of the harmonic interval. The harmonic interval is divided into a plurality of unit intervals, i.e., [XBmin, 1×b], [1×b, 2×b], …, [(n-1)×b, n×b], and [n×b, XBmax], according to the unit value b, wherein the unit value b is set to 1%, and n is a positive integer.

3. The photovoltaic power generation harmonic analysis processing system according to claim 1, characterized by, The method for obtaining the data set comprises: In the historical data, the time period in which each photovoltaic device is in an operating state in the corresponding cycle time is identified and marked as an operating state time period; 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 points, 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 obtained, the harmonic data in the operating state time period in each cycle time is identified, the harmonic data is sorted and summarized according to the interval range of the unit interval, and the combined data corresponding to 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.

4. The photovoltaic power generation harmonic analysis processing system according to claim 1, characterized by, 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 a fixed time to obtain a plurality of cycle times; Meanwhile, the unit time is set, and each cycle time is further divided according to the unit time to obtain a plurality of local times; Then, 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, and the historical power consumption data corresponding to each local time is marked as time period data; Taking a single cycle time as a cycle, all cycle times are obtained, and the cycle times are aligned according to the local time; Taking the local time as an analysis unit, the time period data corresponding to the same local time in all cycle times is obtained, and the mean value is processed. The obtained mean value is marked as the representative value of the local time. The representative values of all local times are calculated based on the above method; A two-dimensional plane coordinate system is set, the horizontal coordinate is set as the local time, and the vertical coordinate is set as the power consumption. According to the representative value of the local time, a curve graph is drawn, and the curve graph is marked as the inertia power consumption curve of the target area.

5. A photovoltaic power generation harmonic analysis processing system according to claim 4, characterized by, The fixed time is set to 1 day, and a cycle time is a natural day. The unit time is set to 1 hour.

6. The photovoltaic power generation harmonic analysis processing system according to claim 4, characterized by, Before the mean value processing, the abnormal data in each period data needs to be identified and deleted, and the remaining normal data is processed by mean value to obtain the representative value corresponding to the local time, the identification method of abnormal data adopts standard deviation method, including: Based on the formula The standard deviation Z, De represents the power consumption data, The standard deviation Z, De represents the power consumption data, The standard deviation Z, De represents the power consumption data, The standard deviation Z, De represents the power consumption data, The standard deviation Z, De represents the power consumption data, 7. The photovoltaic power generation harmonic analysis processing system according to claim 1, characterized by, 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 capacity from the power generation demand value, and mark the difference value as the adjustment parameter.

8. The photovoltaic power generation harmonic analysis processing system according to claim 1, characterized by, It also includes a data storage module for storing the basic data and historical data of the target area, the basic data including photovoltaic equipment data and regional distribution data of the target area, and the historical data including 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.

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