Harmonic suppression and power quality optimization method for photovoltaic inverter

Abstract

The application discloses a photovoltaic inverter harmonic suppression and power grid power quality collaborative optimization method and relates to the technical field of power systems, and comprises the following steps: acquiring a photovoltaic inverter output current wave and marking the wave as a real-time detection wave; acquiring a first historical data point based on a normal current wave in a power grid; acquiring a screening coordinate point based on the first historical data point; acquiring a reference periodic wave based on the screening coordinate point; constructing a real-time detection length based on the real-time detection wave and the reference wave; acquiring a historical detection length based on a normal wave without a harmonic; acquiring a detection length threshold value based on the historical detection length; and classifying the real-time detection wave based on the real-time detection length and the detection length threshold value, and performing different operations on different kinds of real-time detection waves; the application is used to solve the problem that, in the prior art, whether a harmonic appears is determined by frequency and amplitude data, resulting in complicated data comparison and low accuracy of a determination result.

Description

Technical Field

[0001] This invention relates to the field of power system technology, specifically to a method for the coordinated optimization of harmonic suppression in photovoltaic inverters and power quality in power grids. Background Technology

[0002] With the acceleration of the global energy transition, photovoltaic power generation, as a core component of renewable energy, can inject harmonic currents into the grid due to the rapid switching action, nonlinear control characteristics, and interaction with grid impedance of photovoltaic inverters. This can lead to voltage waveform distortion, seriously threatening the safe and stable operation of the grid and the normal operation of various electrical equipment. This interference not only reduces the power quality of the grid but may also negatively affect grid stability and equipment lifespan. Therefore, it is necessary to determine whether harmonics occur and to suppress them.

[0003] To prevent the injection of harmonic current into the power grid, it is necessary to determine whether the injected current is distorted. Existing methods analyze the AC output of the photovoltaic inverter to determine if harmonics are present. This typically involves comparing multiple data points, such as frequency and amplitude, of the AC output from the photovoltaic inverter. Because multiple data points are on the verge of being abnormal, even if the current appears normal on each individual data point, the overall current may be abnormal. Therefore, the data comparison is cumbersome and the accuracy of the judgment is low. For example, patent application CN119496136A discloses a multi-dimensional time-series photovoltaic grid-connected harmonic current prediction and control method. This scheme determines whether harmonics are present by using data such as frequency and amplitude, which makes the data comparison cumbersome and the judgment result in low accuracy. In other words, existing technologies that determine whether harmonics are present by using data such as frequency and amplitude result in cumbersome data comparison and low accuracy of the judgment result. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art by acquiring the output current wave of a photovoltaic inverter and marking it as a real-time detection wave; acquiring a first historical data point based on the normal current wave in the power grid; acquiring a filter coordinate point based on the first historical data point; acquiring a reference periodic wave based on the filter coordinate point; constructing a real-time detection length based on the real-time detection wave and the reference waveform; acquiring a historical detection length based on a normal wave without harmonics; acquiring a detection length threshold based on the historical detection length; classifying the real-time detection wave based on the real-time detection length and the detection length threshold, and performing different operations on different types of real-time detection waves. This solves the problem in the prior art where determining whether harmonics exist by using data such as frequency and amplitude leads to cumbersome data comparison and low accuracy of the judgment results.

[0005] To achieve the above objectives, this application provides a method for the coordinated optimization of photovoltaic inverter harmonic suppression and grid power quality, comprising the following steps:

[0006] Acquire the output current waveform of the photovoltaic inverter and mark it as the real-time detection waveform;

[0007] The first historical data point is obtained based on the normal current wave within the power grid.

[0008] Obtain the filter coordinate points based on the first historical data point;

[0009] Obtain the reference periodic wave based on the selected coordinate points;

[0010] The real-time detection length is constructed based on the real-time detected wave and the reference waveform;

[0011] Historical detection lengths are obtained based on normal waves without harmonics;

[0012] The detection length threshold is obtained based on historical detection lengths;

[0013] Based on the real-time detection length and the detection length threshold, real-time detection waves are classified, and different operations are performed on different types of real-time detection waves.

[0014] Furthermore, obtaining the first historical data point based on the normal current wave within the power grid includes the following sub-steps:

[0015] The first number of periodic waveforms in the normal current wave within the power grid are obtained and marked as historical periodic waveforms.

[0016] A Cartesian coordinate system was established with the horizontal axis representing time and the vertical axis representing current, and this system was marked as the historical current coordinate system.

[0017] This ensures that the starting point of the historical periodic wave coincides with the origin of the historical current coordinate system.

[0018] A second number of evenly spaced data points are plotted on each historical periodic wave and marked as the first historical data point.

[0019] Furthermore, obtaining the filter coordinate points based on the first historical data point includes the following sub-steps:

[0020] Obtain the ordinate of the first historical data point with the same x-coordinate and mark it as the historical ordinate.

[0021] Obtain the range of the historical vertical coordinates and mark it as the first overall range;

[0022] The first overall area is evenly divided into a third number of areas, which are marked as the first division area;

[0023] Obtain the frequency of the historical ordinate within each first division range and mark it as the first division frequency;

[0024] Sort the frequencies of the first division from left to right according to the minimum value of the corresponding first division range, from smallest to largest.

[0025] Furthermore, obtaining the filter coordinates based on the first historical data point also includes the following sub-steps:

[0026] Obtain the sum of the first frequencies and mark it as the first total frequency;

[0027] Divide the first total frequency by the third quantity to obtain the value, and mark it as the first average frequency.

[0028] Set a first abnormality percentage; multiply the first average frequency and the first abnormality percentage to obtain a value, and mark it as the first abnormality threshold;

[0029] The first segment frequency that is less than the first anomaly threshold is marked as the first anomaly frequency;

[0030] If the leftmost first division frequency is the first abnormal frequency, then delete the leftmost first abnormal frequency and continue deleting the first abnormal frequency to the right until the leftmost is no longer the first abnormal frequency and then stop; after stopping, obtain the minimum value of the first division range corresponding to the leftmost first abnormal frequency and mark it as the first ordinate value.

[0031] If the first division frequency on the far right is the first abnormal frequency, then delete the first abnormal frequency on the far right and continue to delete the first abnormal frequency to the left until the far right is no longer the first abnormal frequency. After stopping, obtain the maximum value of the first division range corresponding to the first abnormal frequency on the far side of Yangpu and mark it as the second vertical coordinate value.

[0032] Obtain the first historical data point with a ordinate between the first and second ordinate values, and mark it as the filter coordinate point.

[0033] Furthermore, obtaining the reference period waveform based on the selected coordinate points includes the following sub-steps:

[0034] Get the filter coordinates of each point with the same x-coordinate;

[0035] The function is obtained by fitting all the selected coordinate points to a function and marked as the reference periodic wave.

[0036] Furthermore, constructing the real-time detection length based on the real-time detected waveform and the reference waveform includes the following sub-steps:

[0037] Acquire the waveform of each period in the real-time detection wave and mark it as the real-time detection periodic wave;

[0038] Make the starting point of the real-time detected periodic wave coincide with that of the reference periodic wave;

[0039] The sixth number of coordinate points with equal intervals are plotted on the real-time detected periodic wave and marked as the first detection coordinate point;

[0040] The first detection coordinate points are marked with serial numbers according to their x-coordinates from smallest to largest, and these serial numbers are designated as the first serial numbers; the first serial numbers are positive integers starting from 1.

[0041] Plot the sixth number of coordinate points with equal intervals on the reference periodic wave and mark them as the second detection coordinate points;

[0042] The second detection coordinate points are labeled with serial numbers according to their x-coordinates from smallest to largest, and these serial numbers are positive integers starting from 1.

[0043] Connect the first detection coordinate point with the second detection coordinate point that has the same first and second serial numbers to obtain a line segment, which is marked as an interval line segment;

[0044] Obtain the length between the interval line segments and mark it as the real-time detection length.

[0045] Furthermore, obtaining the historical detection length based on a normal wave without harmonics includes the following sub-steps:

[0046] Obtain the fourth number of normal waves without harmonics and mark them as the second historical periodic waves;

[0047] The second historical periodic wave is regarded as a real-time detection periodic wave to obtain the real-time detection length, which is then marked as the historical detection length.

[0048] Furthermore, obtaining the detection length threshold based on historical detection lengths includes the following sub-steps:

[0049] Obtain the range of historical detection lengths and mark it as the second overall range;

[0050] The second overall range is evenly divided into a fifth number of ranges, which are marked as the second division range;

[0051] Obtain the frequency of historical detection lengths within each second division range and mark it as the second division frequency;

[0052] Sort the frequencies of the second division from left to right according to the minimum value of the corresponding second division range, from smallest to largest.

[0053] Furthermore, obtaining the detection length threshold based on historical detection lengths also includes the following sub-steps:

[0054] Obtain the sum of the second frequencies and mark it as the second sum frequency;

[0055] Divide the second total frequency by the fifth quantity to obtain the value, and mark it as the second average frequency.

[0056] Set a second abnormality percentage; multiply the second average frequency and the second abnormality percentage to obtain a value, and mark it as the second abnormality threshold;

[0057] The second division frequency that is less than the second anomaly threshold is marked as the second anomaly frequency;

[0058] If the rightmost second division frequency is the second abnormal frequency, then delete the rightmost second abnormal frequency and continue deleting the second abnormal frequency to the left until the rightmost is no longer the second abnormal frequency and then stop; after stopping, obtain the maximum value of the second division range corresponding to the rightmost second abnormal frequency and mark it as the detection length threshold.

[0059] Furthermore, based on the real-time detection length and the detection length threshold, the real-time detected waves are classified, and different operations are performed on different types of real-time detected waves, including the following sub-steps:

[0060] If the real-time detection length exceeds the detection length threshold, the real-time detection wave is marked as a first-type real-time wave; if the real-time detection length does not exceed the detection length threshold, the real-time detection wave is marked as a second-type real-time wave; if it is a first-type real-time wave, the current output by the photovoltaic inverter is de-harmonicized before being input into the grid; if it is a second-type real-time wave, the current output by the photovoltaic inverter is directly input into the grid.

[0061] The beneficial effects of this invention are as follows: This invention acquires the output current wave of a photovoltaic inverter and marks it as a real-time detection wave; acquires a first historical data point based on the normal current wave in the power grid; acquires a filter coordinate point based on the first historical data point; acquires a reference periodic wave based on the filter coordinate point; constructs a real-time detection length based on the real-time detection wave and the reference waveform; acquires a historical detection length based on a normal wave without harmonics; acquires a detection length threshold based on the historical detection length; and classifies the real-time detection wave based on the real-time detection length and the detection length threshold, performing different operations on different types of real-time detection waves. The advantage is that it reduces the amount of comparison data and improves the accuracy of the judgment results.

[0062] This invention constructs a real-time detection length based on a real-time detected wave and a reference waveform. Its advantage lies in determining whether harmonics are present based on the real-time detection length, reducing the amount of comparison data and improving the accuracy of the judgment results. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;

[0064] Figure 2 This is a schematic diagram of the reference periodic wave of the present invention;

[0065] Figure 3This is a schematic diagram of an interval line segment according to the present invention. Detailed Implementation

[0066] 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.

[0067] Example 1, please refer to Figure 1 As shown, this application provides a method for the coordinated optimization of photovoltaic inverter harmonic suppression and power grid power quality, including the following steps:

[0068] Step S1: Obtain the output current waveform of the photovoltaic inverter and mark it as the real-time detection waveform.

[0069] Step S2: Obtain the first historical data point based on the normal current wave within the power grid; Step S2 includes the following sub-steps:

[0070] Step S201: Obtain the first number of periodic waveforms in the normal current wave in the power grid and mark them as historical periodic waveforms; the first number of historical periodic waveforms are used to obtain more accurate waveforms, for example, the first number is set to 100.

[0071] Step S202: Establish a Cartesian coordinate system with the horizontal axis representing time and the vertical axis representing current value, and mark it as the historical current coordinate system;

[0072] Step S203: Make the starting point of the historical periodic wave coincide with the origin of the historical current coordinate system.

[0073] Step S204: Draw a second number of evenly spaced data points on each historical periodic wave and mark them as first historical data points; obtain the second number of first historical data points to facilitate obtaining the waveform of each historical periodic wave, for example, the second number is 20.

[0074] Step S3: Obtain the filter coordinate points based on the first historical data points; Step S3 includes the following sub-steps:

[0075] Step S301: Obtain the ordinate of the first historical data point with the same x-coordinate and mark it as the historical ordinate.

[0076] Step S302: Obtain the range of the historical vertical coordinates and mark it as the first overall range;

[0077] Step S303: The first overall range is evenly divided into a third number of ranges, which are marked as the first division ranges; the third number of the first division ranges are obtained in order to observe the historical vertical coordinate distribution, for example, the third number is 10;

[0078] Step S304: Obtain the frequency of the historical ordinate within each first division range and mark it as the first division frequency;

[0079] Step S305: Sort the frequency of the first division from left to right according to the minimum value of the corresponding first division range in ascending order.

[0080] Step S306: Obtain the sum of the first frequencies and mark it as the first total frequency;

[0081] Step S307: Divide the first total frequency by the third quantity to obtain a value, and mark it as the first average frequency.

[0082] Step S308: Set the first abnormality percentage; multiply the first average frequency and the first abnormality percentage to obtain a value, and mark it as the first abnormality threshold; in order to obtain a smaller first average frequency, the first abnormality percentage is set to be small, for example, the first abnormality percentage is 0.2.

[0083] Step S309: Mark the first division frequency that is less than the first abnormal threshold as the first abnormal frequency;

[0084] In practical applications, the first total frequency is obtained as 100; the first average frequency is calculated as 100 ÷ 10 = 10; the first anomaly threshold is calculated as 10 × 0.2 = 2; the first anomaly threshold is 2; the first sub-frequency that is less than 2 is marked as the first anomaly frequency.

[0085] Step S310: If the leftmost first division frequency is the first abnormal frequency, delete the leftmost first abnormal frequency and continue deleting the first abnormal frequency to the right until the leftmost is no longer the first abnormal frequency and then stop; after stopping, obtain the minimum value of the first division range corresponding to the leftmost first abnormal frequency and mark it as the first ordinate value; the first ordinate value is used to filter out historical ordinates that are too small, so that the historical ordinate range obtained later is more accurate, and thus the reference period wave obtained later is more accurate;

[0086] Step S311: If the rightmost first division frequency is the first abnormal frequency, delete the rightmost first abnormal frequency and continue deleting the first abnormal frequency to the left until the rightmost is no longer the first abnormal frequency. After stopping, obtain the maximum value of the first division range corresponding to the first abnormal frequency on the Yangpu side and mark it as the second ordinate value. The second ordinate value is used to filter out excessively large historical ordinates, so that the range of historical ordinates obtained later is more accurate, and thus the reference period wave obtained later is more accurate.

[0087] Step S312: Obtain the first historical data point with ordinate between the first ordinate value and the second ordinate value, and mark it as the filter coordinate point;

[0088] For practical applications, please refer to Figure 2 As shown, the obtained filter coordinate points.

[0089] Step S4: Obtain the reference periodic wave based on the selected coordinate points; Step S4 includes the following sub-steps:

[0090] Step S401: Obtain the filter coordinate points with the same x-coordinate;

[0091] Step S402: Perform function fitting on all the selected coordinate points to obtain a function, which is marked as the reference periodic wave; the reference periodic wave is closer to the fluctuation of normal current.

[0092] For practical applications, please refer to Figure 3 As shown, the obtained reference periodic wave.

[0093] Step S5: Construct the real-time detection length based on the real-time detected waveform and the reference waveform; Step S5 includes the following sub-steps:

[0094] Step S501: Obtain the waveform of each period in the real-time detection wave and mark it as the real-time detection periodic wave;

[0095] Step S502: Align the starting points of the real-time detected periodic wave with the reference periodic wave for easy comparison.

[0096] Step S503: Draw a sixth number of coordinate points with equal intervals on the real-time detection periodic wave and mark them as the first detection coordinate points; the larger the sixth number is, the better, for example, the sixth number is 20.

[0097] Step S504: Mark the first detection coordinate points with serial numbers according to their x-coordinates from smallest to largest, and mark them as the first serial number; the first serial number is a positive integer starting from 1;

[0098] Step S505: Draw a sixth number of coordinate points with equal spacing paths on the reference periodic wave and mark them as the second detection coordinate points; set the sixth number to be consistent to facilitate the first detection coordinate point and the second detection coordinate point;

[0099] Step S506: Mark the second detection coordinate points with serial numbers according to their x-coordinates from smallest to largest, and mark them as the second serial number; the second serial number is a positive integer starting from 1;

[0100] Step S507: Connect the first detection coordinate point with the second detection coordinate point that has the same first serial number and the second serial number to obtain a line segment, which is marked as an interval line segment;

[0101] Step S508: Obtain the length between the interval line segments and mark it as the real-time detection length; the real-time detection length is the same as the distance at the same position. If the real-time detection length is larger, it means that it is less approximate to the reference periodic wave; then the real-time detection periodic wave can be judged based on the real-time detection length, reducing the comparison data of frequency and assignment, etc.; the real-time detection length is the actual distance of the drawing size.

[0102] For practical applications, please refer to Figure 3 As shown, an interval line segment is obtained; the obtained real-time detection length is 5cm.

[0103] Step S6: Obtain the historical detection length based on the normal wave without harmonics; Step S6 includes the following sub-steps:

[0104] Step S601: Obtain the fourth number of normal waves without harmonics and mark them as the second historical periodic waves; the fourth number of second historical periodic waves is to obtain the maximum value of the real-time detection length when there are no harmonics; therefore, the fourth number should not be set too low, for example, the fourth number is 100.

[0105] Step S602: Treat the second historical periodic wave as a real-time detection periodic wave to obtain the real-time detection length, and mark it as the historical detection length.

[0106] Step S7: Obtain the detection length threshold based on historical detection lengths; Step S7 includes the following sub-steps:

[0107] Step S701: Obtain the range of historical detection lengths and mark it as the second overall range;

[0108] Step S702: The second overall range is evenly divided into a fifth number of ranges, which are marked as the second division ranges; the fifth number of second division ranges are for observing the historical detection length distribution, for example, the fifth number is 10;

[0109] Step S703: Obtain the frequency of historical detection lengths within each second division range and mark it as the second division frequency;

[0110] Step S704: Sort the second division frequency from left to right according to the minimum value of the corresponding second division range in ascending order.

[0111] Step S705: Obtain the sum of the second frequencies and mark it as the second sum frequency;

[0112] Step S706: Divide the second total frequency by the fifth quantity to obtain a value, and mark it as the second average frequency.

[0113] Step S707: Set the second anomaly percentage; multiply the second average frequency and the second anomaly percentage to obtain a value, and mark it as the second anomaly threshold; in order to obtain a smaller second average frequency, the second anomaly percentage should not be set too large, for example, the second anomaly percentage is 0.2;

[0114] Step S708: Mark the second division frequency that is less than the second abnormal threshold as the second abnormal frequency;

[0115] In practical applications, the second total frequency is obtained as 100; the second average frequency is calculated as 100 ÷ 10 = 10; the second anomaly threshold is calculated as 10 × 0.2 = 2; the second anomaly threshold is 2; the second average frequency less than 2 is marked as the second anomaly frequency.

[0116] Step S709: If the rightmost second division frequency is the second abnormal frequency, delete the rightmost second abnormal frequency and continue deleting the second abnormal frequency to the left until the rightmost is no longer the second abnormal frequency; after stopping, obtain the maximum value of the second division range corresponding to the rightmost second abnormal frequency and mark it as the detection length threshold; the detection length threshold is used to filter out excessively large historical detection lengths; that is, to delete abnormal points and make the obtained maximum value of historical detection length more accurate;

[0117] In practical applications, for example, the detection length threshold is set to 0.62cm.

[0118] Step S8 involves classifying the real-time detected waves based on the real-time detection length and the detection length threshold, and performing different operations on different types of real-time detected waves. Step S8 includes the following sub-steps:

[0119] Step S801: If the real-time detection length is greater than the detection length threshold, the real-time detection wave is marked as a first type of real-time wave; if the real-time detection length is not greater than the detection length threshold, the real-time detection wave is marked as a second type of real-time wave; if it is a first type of real-time wave, the current output by the photovoltaic inverter is de-harmonicized before being input into the grid; if it is a second type of real-time wave, the current output by the photovoltaic inverter is directly input into the grid; if no harmonics occur, the real-time detection wave is similar to the reference periodic wave, and the real-time detection length is small; if harmonics occur, the real-time detection wave is distorted, making the real-time detection wave different from the reference periodic wave, that is, the real-time detection length is large and will be greater than the detection length threshold, so it is necessary to de-harmonicize before inputting it into the grid.

[0120] In practical applications, there may be more than one real-time detection length. For example, if the real-time detection length is 5cm, which is greater than the detection length threshold of 0.62cm, then the real-time detection wave is marked as the first type of real-time wave. The current output by the photovoltaic inverter is then fed into the grid after the harmonics are eliminated, and the existing harmonic elimination methods can be used.

[0121] Example 2: This application also provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, steps such as those in the photovoltaic inverter harmonic suppression and grid power quality co-optimization method are performed to achieve the following functions: acquiring the output current wave of the photovoltaic inverter and marking it as a real-time detection wave; acquiring a first historical data point based on the normal current wave in the grid; acquiring a filter coordinate point based on the first historical data point; acquiring a reference periodic wave based on the filter coordinate point; constructing a real-time detection length based on the real-time detection wave and the reference waveform; acquiring a historical detection length based on a normal wave without harmonics; acquiring a detection length threshold based on the historical detection length; classifying the real-time detection wave based on the real-time detection length and the detection length threshold, and performing different operations on different types of real-time detection waves.

[0122] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the photovoltaic inverter harmonic suppression and grid power quality collaborative optimization method provided by the above methods. This method includes: acquiring the output current wave of the photovoltaic inverter and marking it as a real-time detection wave; acquiring a first historical data point based on the normal current wave in the grid; acquiring a filter coordinate point based on the first historical data point; acquiring a reference periodic wave based on the filter coordinate point; constructing a real-time detection length based on the real-time detection wave and the reference waveform; acquiring a historical detection length based on a normal wave without harmonics; acquiring a detection length threshold based on the historical detection length; classifying the real-time detection wave based on the real-time detection length and the detection length threshold, and performing different operations on different types of real-time detection waves.

[0124] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps in the above-mentioned method for co-optimizing photovoltaic inverter harmonic suppression and power grid power quality to achieve the following functions: acquiring the output current wave of the photovoltaic inverter and marking it as a real-time detection wave; acquiring a first historical data point based on the normal current wave in the power grid; acquiring a filter coordinate point based on the first historical data point; acquiring a reference periodic wave based on the filter coordinate point; constructing a real-time detection length based on the real-time detection wave and the reference waveform; acquiring a historical detection length based on a normal wave without harmonics; acquiring a detection length threshold based on the historical detection length; classifying the real-time detection wave based on the real-time detection length and the detection length threshold, and performing different operations on different types of real-time detection waves.

[0125] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0126] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for synergistic optimization of photovoltaic inverter harmonic suppression and power grid power quality, characterized in that, Includes the following steps: Acquire the output current waveform of the photovoltaic inverter and mark it as the real-time detection waveform; The first historical data point is obtained based on the normal current wave within the power grid. Obtain the filter coordinate points based on the first historical data point; Obtain the reference periodic wave based on the selected coordinate points; The real-time detection length is constructed based on the real-time detected wave and the reference waveform; Historical detection lengths are obtained based on normal waves without harmonics; The detection length threshold is obtained based on historical detection lengths; Based on the real-time detection length and the detection length threshold, the real-time detection waves are classified and different operations are performed on different types of real-time detection waves. Obtaining the filter coordinate points based on the first historical data point includes the following sub-steps: Obtain the ordinate of the first historical data point with the same x-coordinate and mark it as the historical ordinate. Obtain the range of the historical vertical coordinates and mark it as the first overall range; The first overall area is evenly divided into a third number of areas, which are marked as the first division area; Obtain the frequency of the historical ordinate within each first division range and mark it as the first division frequency; Sort the frequencies of the first division from left to right according to the minimum value of the corresponding first division range in ascending order; Obtain the sum of the first frequencies and mark it as the first total frequency; Divide the first total frequency by the third quantity to obtain the value, and mark it as the first average frequency. Set a first abnormality percentage; multiply the first average frequency and the first abnormality percentage to obtain a value, and mark it as the first abnormality threshold; The first segment frequency that is less than the first anomaly threshold is marked as the first anomaly frequency; If the leftmost first division frequency is the first abnormal frequency, then delete the leftmost first abnormal frequency and continue deleting the first abnormal frequency to the right until the leftmost is no longer the first abnormal frequency and then stop; after stopping, obtain the minimum value of the first division range corresponding to the leftmost first abnormal frequency and mark it as the first ordinate value. If the first division frequency on the far right is the first abnormal frequency, then delete the first abnormal frequency on the far right and continue to delete the first abnormal frequency to the left until the far right is no longer the first abnormal frequency. After stopping, obtain the maximum value of the first division range corresponding to the first abnormal frequency on the far side of Yangpu and mark it as the second vertical coordinate value. Obtain the first historical data point with a ordinate between the first and second ordinate values, and mark it as the filter coordinate point; Obtaining the reference period waveform based on the selected coordinate points includes the following sub-steps: Get the filter coordinates of each point with the same x-coordinate; The function is obtained by fitting all the selected coordinate points to a function and marked as the reference periodic wave; Constructing the real-time detection length based on the real-time detected waveform and the reference waveform includes the following sub-steps: Acquire the waveform of each period in the real-time detection wave and mark it as the real-time detection periodic wave; Make the starting point of the real-time detected periodic wave coincide with that of the reference periodic wave; The sixth number of coordinate points with equal intervals are plotted on the real-time detected periodic wave and marked as the first detection coordinate point; The first detection coordinate points are marked with serial numbers according to their x-coordinates from smallest to largest, and these serial numbers are designated as the first serial numbers; the first serial numbers are positive integers starting from 1. Plot the sixth number of coordinate points with equal intervals on the reference periodic wave and mark them as the second detection coordinate points; The second detection coordinate points are marked with serial numbers according to their abscissas from smallest to largest, and these serial numbers are designated as the second serial numbers. The second serial number is a positive integer starting from 1; Connect the first detection coordinate point with the second detection coordinate point that has the same first and second serial numbers to obtain a line segment, which is marked as an interval line segment; Obtain the length between the interval line segments and mark it as the real-time detection length.

2. The method for coordinated optimization of photovoltaic inverter harmonic suppression and power grid power quality according to claim 1, characterized in that, Obtaining the first historical data point based on normal current waves within the power grid includes the following sub-steps: The first number of periodic waveforms in the normal current wave within the power grid are obtained and marked as historical periodic waveforms. A Cartesian coordinate system was established with the horizontal axis representing time and the vertical axis representing current, and this system was marked as the historical current coordinate system. This ensures that the starting point of the historical periodic wave coincides with the origin of the historical current coordinate system. A second number of evenly spaced data points are plotted on each historical periodic wave and marked as the first historical data point.

3. The method for coordinated optimization of photovoltaic inverter harmonic suppression and power grid power quality according to claim 2, characterized in that, Obtaining the historical detection length based on a normal wave without harmonics includes the following sub-steps: Obtain the fourth number of normal waves without harmonics and mark them as the second historical periodic waves; The second historical periodic wave is regarded as a real-time detection periodic wave to obtain the real-time detection length, which is then marked as the historical detection length.

4. The method for coordinated optimization of photovoltaic inverter harmonic suppression and power grid power quality according to claim 3, characterized in that, Obtaining the detection length threshold based on historical detection lengths includes the following sub-steps: Obtain the range of historical detection lengths and mark it as the second overall range; The second overall range is evenly divided into a fifth number of ranges, which are marked as the second division range; Obtain the frequency of historical detection lengths within each second division range and mark it as the second division frequency; Sort the frequencies of the second division from left to right according to the minimum value of the corresponding second division range, from smallest to largest.

5. The method for coordinated optimization of photovoltaic inverter harmonic suppression and power grid power quality according to claim 4, characterized in that, Obtaining the detection length threshold based on historical detection lengths also includes the following sub-steps: Obtain the sum of the second frequencies and mark it as the second sum frequency; Divide the second total frequency by the fifth quantity to obtain the value, and mark it as the second average frequency. Set a second abnormality percentage; multiply the second average frequency and the second abnormality percentage to obtain a value, and mark it as the second abnormality threshold; The second division frequency that is less than the second anomaly threshold is marked as the second anomaly frequency; If the rightmost second division frequency is the second abnormal frequency, then delete the rightmost second abnormal frequency and continue deleting the second abnormal frequency to the left until the rightmost is no longer the second abnormal frequency and then stop; after stopping, obtain the maximum value of the second division range corresponding to the rightmost second abnormal frequency and mark it as the detection length threshold.

6. The method for coordinated optimization of photovoltaic inverter harmonic suppression and power grid power quality according to claim 5, characterized in that, Based on the real-time detection length and detection length threshold, real-time detected waves are classified, and different operations are performed on different types of real-time detected waves, including the following sub-steps: If the real-time detection length exceeds the detection length threshold, the real-time detection wave is marked as a first-type real-time wave; if the real-time detection length does not exceed the detection length threshold, the real-time detection wave is marked as a second-type real-time wave; if it is a first-type real-time wave, the current output by the photovoltaic inverter is de-harmonicized before being input into the grid; if it is a second-type real-time wave, the current output by the photovoltaic inverter is directly input into the grid.

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