Harmonic optimization method and system for renewable energy power generation

Abstract

The present invention provides a harmonic optimization method and system for renewable energy power generation, and relates to the field of electric power technology. The implementation scheme is: if an abnormal inverter is detected, the distance between the abnormal inverter and its adjacent inverter is calculated by using the functional relationship between the impedance characteristic mutual influence change curve between two adjacent inverters and the distance change, and the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter is obtained; based on the impedance characteristic mutual influence change curve, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are solved; based on the first target harmonic compensation parameter and the second target harmonic compensation parameter, the abnormal inverter and the adjacent inverter are harmonically optimized respectively.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a harmonic optimization method and system for renewable energy power generation. Background Art

[0002] In distributed photovoltaic systems, the difference in physical distance between inverters leads to different output impedance characteristics. When the DC bus voltage of an inverter fluctuates, the switching frequency drifts, or the inductor saturation changes, the output impedance of the inverter will change. This impedance change will affect the harmonic compensation control strategy of adjacent inverters. Moreover, since the harmonic compensation control strategies of multiple inverters affect each other, there is a complex coupling relationship between them. This coupling effect may cause system instability and even resonance, which will have an adverse effect on the overall harmonic suppression effect.

[0003] Therefore, how to coordinate the harmonic compensation control strategies of multiple inverters to achieve overall harmonic optimization while ensuring system stability is also a challenging technical problem. Summary of the invention

[0004] The present invention provides a harmonic optimization method, device, system and storage medium for renewable energy power generation, which can solve at least one of the above technical problems.

[0005] According to one aspect of the present invention, a harmonic optimization method for renewable energy power generation is provided, comprising:

[0006] In the case where an abnormal inverter is detected in the distributed photovoltaic system, the distance between the abnormal inverter and its adjacent inverter is calculated by using the pre-constructed functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters with the distance change, so as to obtain the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter;

[0007] Based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are solved;

[0008] Based on the first target harmonic compensation parameter and the second target harmonic compensation parameter, harmonic optimization is performed on the abnormal inverter and the adjacent inverter respectively.

[0009] According to another aspect of the present invention, there is provided a harmonic optimization device for renewable energy power generation, comprising:

[0010] A curve determination module is used to calculate the distance between the abnormal inverter and its adjacent inverter by using a pre-built functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters with the change of distance when an abnormal inverter is detected in the distributed photovoltaic system, so as to obtain the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter;

[0011] A compensation parameter solving module, used to solve the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter;

[0012] A harmonic optimization module is used to perform harmonic optimization on the abnormal inverter and the adjacent inverter based on the first target harmonic compensation parameter and the second target harmonic compensation parameter.

[0013] According to another aspect of the present invention, there is provided an electronic device, comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any harmonic optimization method for renewable energy power generation in the embodiments of the present invention.

[0017] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to execute any harmonic optimization method for renewable energy power generation according to any embodiment of the present invention.

[0018] By adopting the technical solution of the present invention, when an abnormal inverter is detected, the distance between the abnormal inverter and its adjacent inverter is calculated by using the pre-constructed functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters with the change of distance, and the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter is obtained; based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter obtained by pre-training and the output harmonic content of the inverter, the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are solved; based on the first target harmonic compensation parameter and the second target harmonic compensation parameter, the abnormal inverter and the adjacent inverter are harmonically optimized respectively. Thus, the harmonic compensation control strategy of multiple inverters between the abnormal inverter and its adjacent inverter can be coordinated to achieve overall harmonic optimization and avoid the occurrence of resonance.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings are used to better understand the present invention and do not constitute a limitation of the present invention.

[0021] Figure 1 is a flow chart of a harmonic optimization method for renewable energy power generation according to an embodiment of the present invention;

[0022] Figure 2 It is a structural block diagram of a harmonic optimization device for renewable energy power generation according to an embodiment of the present invention;

[0023] Figure 3 The block diagram is a block diagram of an electronic device for implementing the method according to the embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present invention. Similarly, for clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0025] Figure 1 It is a flow chart of a harmonic optimization method for renewable energy power generation according to an embodiment of the present invention.

[0026] like Figure 1As shown, the harmonic optimization method for renewable energy power generation may include:

[0027] S110, when an abnormal inverter is detected in the distributed photovoltaic system, a pre-built functional relationship between the impedance characteristic mutual influence change curve between two adjacent inverters and the distance is used to calculate the distance between the abnormal inverter and its adjacent inverter, and obtain the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter;

[0028] S120, based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, solve the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter;

[0029] S130: Perform harmonic optimization on the abnormal inverter and the adjacent inverter respectively based on the first target harmonic compensation parameter and the second target harmonic compensation parameter.

[0030] Exemplarily, a distributed photovoltaic system may include a plurality of inverters.

[0031] Exemplarily, based on information such as the DC bus voltage, switching frequency, and inductor current information of each inverter in the distributed photovoltaic system, it can be detected whether each inverter is an abnormal inverter.

[0032] Exemplarily, a data acquisition card can be used to collect data from each inverter in a distributed photovoltaic system. For example, the DC bus voltage, switching frequency and inductor current time series data of the inverter are collected at a sampling frequency of 50 kHz using an NI-9215 data acquisition card. The voltage time series data is collected using a voltage collector with a range of 0 to 1000 volts, and the inductor current time series data is collected using a current sensor with a range of 0 to 100 amperes.

[0033] Exemplarily, by using the DC bus voltage, switching frequency, and inductor current information of each inverter within a specified time window, it is possible to detect whether each inverter is an abnormal inverter.

[0034] Exemplarily, each inverter may have multiple adjacent inverters. An abnormal inverter may also have one or more adjacent inverters. The adjacent inverter in the above example refers to an inverter adjacent to the abnormal inverter.

[0035] Exemplarily, the above functional relationship can be constructed using historical data of each inverter in the distributed photovoltaic system.

[0036] For example, for two inverters at different distances, the impedance characteristics of the two inverters have different mutual influence change curves.

[0037] Exemplarily, the impedance characteristic mutual influence change curve may be used to describe the impedance characteristic of the first inverter of the two inverters that changes due to the change of the impedance characteristic of the second inverter. The first inverter is adjacent to the second inverter.

[0038] For example, for any inverter, if its dead time, switching frequency and modulation ratio change, its impedance characteristics also change accordingly, and thus, its output harmonic content also changes accordingly. The dead time is the interlocking delay time of the switch tube in the inverter.

[0039] For example, the historical data of each inverter in the distributed photovoltaic system can be used to pre-train the nonlinear mapping relationship between the inverter's dead time, switching frequency and modulation ratio and the inverter's output harmonic content. A neural network model can be used for training, or a nonlinear function can be used for fitting and generation.

[0040] Exemplarily, a particle swarm optimization algorithm or a genetic algorithm may be used to solve the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter, and these two parameters are optimal parameters.

[0041] It can be understood that the first target harmonic compensation parameter may include the dead time, switching frequency and modulation ratio of the abnormal inverter.

[0042] It can be understood that the second target harmonic compensation parameter may include the dead time, switching frequency and modulation ratio of the adjacent inverter.

[0043] Exemplarily, based on the first target harmonic compensation parameter and the second target harmonic compensation parameter, the operating parameters of the abnormal inverter and the adjacent inverter are respectively set, thereby achieving harmonic optimization.

[0044] According to the above implementation, based on the DC bus voltage, switching frequency and inductor current information of each inverter in the distributed photovoltaic system, detect whether each inverter is an abnormal inverter; in the case of detecting an abnormal inverter, the distance between the abnormal inverter and its adjacent inverter is calculated by using the pre-constructed functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters with the distance change, and the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter is obtained; based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are solved; based on the first target harmonic compensation parameter and the second target harmonic compensation parameter, the abnormal inverter and the adjacent inverter are harmonically optimized respectively. Thus, the harmonic compensation control strategy of multiple inverters between the abnormal inverter and its adjacent inverter can be coordinated to achieve overall harmonic optimization and avoid the occurrence of resonance.

[0045] In one embodiment, based on the DC bus voltage, switching frequency and inductor current information of each inverter in the distributed photovoltaic system, detecting whether each inverter is an abnormal inverter includes: performing short-time Fourier transform on the DC bus voltage change curve, switching frequency change curve and inductor current change curve of the inverter in the first time window, respectively, to obtain DC bus voltage spectrum data, switching frequency spectrum data and inductor current spectrum data; determining the voltage fluctuation amplitude based on the ratio between the fundamental component and the DC component in the DC bus voltage spectrum data; determining the switching frequency deviation value based on the difference between the fundamental frequency in the switching frequency spectrum data and the preset switching frequency; determining the inductor current distortion based on the ratio between the harmonic component and the fundamental component in the inductor current spectrum data; and determining whether the inverter is an abnormal inverter based on the voltage fluctuation amplitude, the switching frequency deviation value and the inductor current distortion.

[0046] Exemplarily, the length of the first time window may be 1 minute or 10 minutes.

[0047] Exemplarily, the DC bus voltage variation curve is used to describe the DC bus voltage that varies with time. The switching frequency variation curve is used to describe the switching frequency that varies with time. The inductor current variation curve is used to describe the inductor current that varies with time.

[0048] Exemplarily, the DC bus voltage change curve, the switching frequency change curve and the inductor current change curve are normalized respectively, and then multiplied by the Hamming window function, and then the fundamental frequency, harmonic component and phase angle corresponding to each curve are calculated by short-time Fourier transform, that is, the spectrum data containing frequency components and phase information are obtained.

[0049] For example, a Kalman filter may be used to filter the voltage fluctuation amplitude, switching frequency deviation value, and inductor current distortion, and the filtered voltage fluctuation amplitude, switching frequency deviation value, and inductor current distortion value may be used to determine whether the inverter is an abnormal inverter, thereby improving the accuracy of detecting abnormal inverters.

[0050] According to the above implementation, the DC bus voltage, switching frequency and inductor current information of each inverter in the distributed photovoltaic system are short-time Fourier transformed to obtain DC bus voltage spectrum data, switching frequency spectrum data and inductor current spectrum data. Then, the DC bus voltage spectrum data, switching frequency spectrum data and inductor current spectrum data are used for calculation to obtain the voltage fluctuation amplitude, switching frequency deviation value and inductor current distortion. The voltage fluctuation amplitude, switching frequency deviation value and inductor current distortion are used to determine whether the inverter is an abnormal inverter. Thus, the accuracy of detecting abnormal inverters can be improved.

[0051] In one embodiment, based on the voltage fluctuation amplitude, the switching frequency deviation value and the inductor current distortion, determining whether the inverter is an abnormal inverter includes: when at least one of the voltage fluctuation amplitude, the switching frequency deviation value and the inductor current distortion does not meet the first preset condition, determining that the inverter is an abnormal inverter.

[0052] Exemplarily, the first preset condition may include that a voltage fluctuation amplitude is within a preset voltage fluctuation amplitude range, a switching frequency deviation value is within a preset switching frequency deviation range, and an inductor current distortion is within a preset inductor current distortion range.

[0053] Exemplarily, when the voltage fluctuation amplitude, the switching frequency deviation value, and the inductor current distortion all meet the first preset condition, it is determined that the inverter is a normal inverter.

[0054] Exemplarily, if the bus voltage fluctuation amplitude exceeds the rated value by 8%, it is determined that the inverter is an abnormal inverter.

[0055] Exemplarily, if the switching frequency deviation value exceeds the rated value by 5%, it is determined that the inverter is an abnormal inverter.

[0056] Exemplarily, if the third harmonic component in the inductor current exceeds 10% of the fundamental component or the fifth harmonic component exceeds 8% of the fundamental component, it is determined that the inverter is an abnormal inverter.

[0057] According to the above-described embodiment, it is possible to accurately determine whether an inverter is an abnormal inverter.

[0058] In one embodiment, it also includes: constructing a three-dimensional model of photovoltaic components based on the position information of each inverter in the distributed photovoltaic system; for any pair of components of two adjacent inverters in the three-dimensional model of photovoltaic components, performing the following simulation process: controlling the first inverter of the two adjacent inverters to work under the first simulation condition, and controlling the second inverter not to work, to obtain a first impedance characteristic curve of the first inverter; controlling the second inverter to work under the first simulation condition, and controlling the first inverter not to work, to obtain a second impedance characteristic curve of the second inverter; controlling the first inverter and the second inverter to work simultaneously under the simulation condition to obtain a third impedance characteristic curve of the first inverter and a fourth impedance characteristic curve of the second inverter; and determining the impedance characteristic mutual influence change curve of the two adjacent inverters at the distance between the first inverter and the second inverter based on the offset of the third impedance characteristic curve relative to the first impedance characteristic curve and the offset of the fourth impedance characteristic curve relative to the second impedance characteristic curve; after executing the above simulation process, based on the impedance characteristic mutual influence change curve of each component pair in the three-dimensional model of photovoltaic components at the corresponding component distance, determining the functional relationship between the impedance characteristic mutual influence change curve between the two adjacent inverters with the change of distance.

[0059] For example, in the simulation software, the location information of each inverter in the distributed photovoltaic system and the connection relationship between each inverter are used to perform three-dimensional modeling to obtain a three-dimensional model of the photovoltaic module, wherein two adjacent inverters are a module pair.

[0060] For example, an electromagnetic field simulation environment is constructed in ANSYS Maxwell software, and the above simulation process is performed on any pair of components of two adjacent inverters in the simulation environment.

[0061] Exemplarily, the impedance characteristic curve may include a phase-frequency impedance characteristic curve and an amplitude-frequency impedance characteristic curve. For example, the impedance characteristic curve may include impedance changes within a frequency range of 100 Hz to 100 kHz.

[0062] Exemplarily, by averaging the offset values ​​of each position point in the offset of the third impedance characteristic curve relative to the first impedance characteristic curve, and the offset of the fourth impedance characteristic curve relative to the second impedance characteristic curve, the offset value at each position point can be obtained, and the offset value of each position point is fitted to obtain the impedance characteristic mutual influence change curve.

[0063] Exemplarily, by finding the maximum value of the two offset values ​​of each position point in the offset of the third impedance characteristic curve relative to the first impedance characteristic curve, and the offset of the fourth impedance characteristic curve relative to the second impedance characteristic curve, the offset value at each position point can be obtained, and the offset value of each position point is fitted to obtain the impedance characteristic mutual influence change curve.

[0064] Exemplarily, a polynomial function is used to fit the impedance characteristic mutual influence change curve of each component pair at its corresponding component distance, and a functional relationship between the impedance characteristic mutual influence change curve between two adjacent inverters and the distance change is obtained.

[0065] According to the above implementation, the functional relationship between the impedance characteristic mutual influence change curve between two adjacent inverters and the distance change can be accurately fitted.

[0066] In one embodiment, based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are solved, including: determining an initial population, wherein the initial population includes multiple individuals, each individual corresponds to the first harmonic compensation parameter of the abnormal inverter and the second harmonic compensation parameter of the adjacent inverter, wherein the harmonic compensation parameters include dead time, switching frequency and modulation ratio; based on the nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, determining a first mapping function between the dead time, switching frequency and modulation ratio of the inverter and the impedance characteristic of the inverter, and a second mapping function between the impedance characteristic of the inverter and the output harmonic content of the inverter; starting from the initial population, performing the following iterative operations: using the first mapping function, respectively calculating the first harmonic compensation parameter and the second harmonic compensation parameter corresponding to each individual in the current iteration population, and obtaining each individual; The method comprises the steps of: calculating a first impedance characteristic of an abnormal inverter corresponding to each individual and a second impedance characteristic of an adjacent inverter; adjusting the first impedance characteristic and the second impedance characteristic corresponding to each individual based on a change curve of impedance characteristics mutual influence between the abnormal inverter and the adjacent inverter, and obtaining a third impedance characteristic of the abnormal inverter corresponding to each individual and a fourth impedance characteristic of the adjacent inverter; using a second mapping function, calculating the third impedance characteristic of the abnormal inverter corresponding to each individual and the fourth impedance characteristic of the adjacent inverter, and obtaining a first output harmonic content of the abnormal inverter corresponding to each individual and a second output harmonic content of the adjacent inverter; when the first output harmonic content of the abnormal inverter corresponding to each individual and the second output harmonic content of the adjacent inverter meet the second preset condition, determining a target individual from each individual based on the first output harmonic content of the abnormal inverter corresponding to each individual and the second output harmonic content of the adjacent inverter, and determining a first target harmonic compensation parameter of the abnormal inverter and a second target harmonic compensation parameter of the adjacent inverter based on the first harmonic compensation parameter of the abnormal inverter corresponding to the target individual and the second harmonic compensation parameter of the adjacent inverter.

[0067] For example, for the nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, the training process may be as follows:

[0068] A training sample set is obtained, wherein each training sample in the training sample set includes input features and output features, the input features include the dead time, switching frequency and modulation ratio of the inverter, and the output features include the output harmonic content of the inverter; according to the training sample set, a fuzzy neural network with a three-layer network structure is trained to obtain a nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter. The input layer of the fuzzy neural network includes three input nodes: dead time, switching frequency and modulation ratio, the fuzzy layer includes multiple rules, each rule corresponds to a Gaussian membership function, and the output layer uses a weighted average method to generate the output harmonic content of the inverter and output the output harmonic content.

[0069] Exemplarily, the nonlinear mapping relationship may be converted into a mathematical function, and the mathematical function may be decomposed into the first mapping function and the second mapping function mentioned above.

[0070] Exemplarily, the second preset condition may be that the difference between the first output harmonic content of each individual abnormal inverter and the second output harmonic content of the adjacent inverter falls within a preset harmonic difference range. Alternatively, the second preset condition may be that the maximum and minimum values ​​of the difference between the first output harmonic content of each individual abnormal inverter and the second output harmonic content of the adjacent inverter fall within a preset harmonic difference range.

[0071] In one embodiment, it also includes: when the first output harmonic content of the abnormal inverter corresponding to each individual and the second output harmonic content of the adjacent inverter do not meet the second preset condition, cross-mutation is performed on each individual in the current iterative population to obtain the next iterative population, and based on the next iterative population, return to continue to execute the above iterative operation.

[0072] According to the above implementation, the genetic algorithm may be modified and applied to the above harmonic compensation parameter solution process, so as to optimize the harmonic compensation parameters quickly and accurately and obtain the optimal value.

[0073] Figure 2 It is a structural block diagram of a harmonic optimization device for renewable energy power generation according to an embodiment of the present invention.

[0074] like Figure 2 As shown, the harmonic optimization device for renewable energy power generation may include:

[0075] The curve determination module 210 is used to calculate the distance between the abnormal inverter and its adjacent inverter by using the pre-built functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters with the change of distance when an abnormal inverter is detected in the distributed photovoltaic system, so as to obtain the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter;

[0076] A compensation parameter solving module 220 is used to solve the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter;

[0077] The harmonic optimization module 230 is used to perform harmonic optimization on the abnormal inverter and the adjacent inverter based on the first target harmonic compensation parameter and the second target harmonic compensation parameter.

[0078] In one embodiment, the above device further comprises:

[0079] The abnormal object determination module is used to detect whether each inverter is an abnormal inverter based on the DC bus voltage, switching frequency and inductor current information of each inverter in the distributed photovoltaic system.

[0080] In one implementation, the abnormal object determination module includes:

[0081] A Fourier transform unit, used for performing short-time Fourier transform on the DC bus voltage variation curve, the switching frequency variation curve and the inductor current variation curve of the inverter in the first time window, respectively, to obtain DC bus voltage spectrum data, switching frequency spectrum data and inductor current spectrum data;

[0082] a voltage fluctuation determination unit, configured to determine a voltage fluctuation amplitude based on a ratio between a fundamental component and a DC component in the DC bus voltage spectrum data;

[0083] A frequency deviation determining unit, configured to determine a switching frequency deviation value based on a difference between a fundamental frequency in the switching frequency spectrum data and a preset switching frequency;

[0084] A distortion determination unit, configured to determine the distortion of the inductor current based on a ratio between a harmonic component and a fundamental component in the inductor current spectrum data;

[0085] The abnormal object determination unit is used to determine whether the inverter is an abnormal inverter based on the voltage fluctuation amplitude, the switching frequency deviation value and the inductor current distortion.

[0086] In one implementation, the abnormal object determination unit is specifically configured to:

[0087] When at least one of the voltage fluctuation amplitude, the switching frequency deviation value and the inductor current distortion does not meet a first preset condition, it is determined that the inverter is an abnormal inverter.

[0088] In one embodiment, the above device further comprises:

[0089] A three-dimensional model building module, used to build a three-dimensional model of photovoltaic components based on the location information of each inverter in the distributed photovoltaic system;

[0090] The inverter operation simulation module is used to perform the following simulation process for any pair of adjacent inverter components in the three-dimensional model of the photovoltaic component;

[0091] Controlling a first inverter of the two adjacent inverters to operate under a first simulation condition, and controlling the second inverter not to operate, to obtain a first impedance characteristic curve of the first inverter;

[0092] controlling the second inverter to operate under the first simulation condition, and controlling the first inverter not to operate, to obtain a second impedance characteristic curve of the second inverter;

[0093] controlling the first inverter and the second inverter to operate simultaneously under the simulation condition to obtain a third impedance characteristic curve of the first inverter and a fourth impedance characteristic curve of the second inverter; and

[0094] Determine a mutual influence change curve of impedance characteristics of the two adjacent inverters at a distance between the first inverter and the second inverter based on an offset of the third impedance characteristic curve relative to the first impedance characteristic curve and an offset of the fourth impedance characteristic curve relative to the second impedance characteristic curve;

[0095] The function relationship determination module is used to determine the function relationship of the impedance characteristic mutual influence change curve between two adjacent inverters as the distance changes based on the impedance characteristic mutual influence change curve of each pair of components at their corresponding component distances in the three-dimensional model of the photovoltaic component.

[0096] In one implementation, the compensation parameter solving module 220 includes:

[0097] A population initialization unit, used for determining an initial population, wherein the initial population includes a plurality of individuals, each of the individuals corresponds to a first harmonic compensation parameter of the abnormal inverter and a second harmonic compensation parameter of the adjacent inverter, wherein the harmonic compensation parameters include a dead time, a switching frequency and a modulation ratio;

[0098] A function decomposition unit, configured to determine a first mapping function between the dead time, switching frequency and modulation ratio of the inverter and the impedance characteristic of the inverter, and a second mapping function between the impedance characteristic of the inverter and the output harmonic content of the inverter based on a nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter;

[0099] The population iteration unit is used to perform the following iterative operations starting from the initial population:

[0100] The first mapping function is used to calculate the first harmonic compensation parameter and the second harmonic compensation parameter corresponding to each of the individuals in the current iteration population, respectively, to obtain the first impedance characteristic of the abnormal inverter and the second impedance characteristic of the adjacent inverter corresponding to each of the individuals;

[0101] Based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, the first impedance characteristic and the second impedance characteristic corresponding to each of the individuals are adjusted respectively to obtain the third impedance characteristic of the abnormal inverter and the fourth impedance characteristic of the adjacent inverter corresponding to each of the individuals;

[0102] The second mapping function is used to calculate the third impedance characteristic of the abnormal inverter and the fourth impedance characteristic of the adjacent inverter corresponding to each of the individuals, respectively, to obtain the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals;

[0103] When the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals meet the second preset condition, a target individual is determined among the individuals based on the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals, and the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are respectively determined based on the first harmonic compensation parameter of the abnormal inverter and the second harmonic compensation parameter of the adjacent inverter corresponding to the target individual.

[0104] In one embodiment, the population iteration unit is further used for:

[0105] When the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals do not meet the second preset condition, cross-mutation is performed on each of the individuals in the current iterative population to obtain the next iterative population, and based on the next iterative population, the iterative operation is returned to be executed.

[0106] For the description of specific functions and examples of each module and submodule of the device in the embodiment of the present invention, reference can be made to the relevant description of the corresponding steps in the above method embodiment, which will not be repeated here.

[0107] In the technical solution of the present invention, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0108] According to an embodiment of the present invention, the present invention also provides a system and a readable storage medium.

[0109] Figure 3 A schematic block diagram of an example electronic device 800 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0110] like Figure 3 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0111] A number of components in the device 800 are connected to the I / O interface 805, including: an input unit 806, such as a keyboard, a mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a disk, an optical disk, etc.; and a communication unit 809, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0112] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as a harmonic optimization method for renewable energy power generation. For example, in some embodiments, the harmonic optimization method for renewable energy power generation may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the harmonic optimization method for renewable energy power generation described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the harmonic optimization method for renewable energy power generation in any other appropriate manner (for example, by means of firmware).

[0113] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0115] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0118] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0119] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document does not limit this.

[0120] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A harmonic optimization method for renewable energy power generation, characterized in that: include: Based on the location information of each inverter in the distributed photovoltaic system, a three-dimensional model of photovoltaic components is constructed; For any pair of adjacent inverters in the three-dimensional model of the photovoltaic component, the following simulation process is performed: the first inverter of the two adjacent inverters is controlled to work under the first simulation condition, and the second inverter is controlled not to work, so as to obtain a first impedance characteristic curve of the first inverter; the second inverter is controlled to work under the first simulation condition, and the first inverter is controlled not to work, so as to obtain a second impedance characteristic curve of the second inverter; the first inverter and the second inverter are controlled to work simultaneously under the simulation condition, so as to obtain a third impedance characteristic curve of the first inverter and a fourth impedance characteristic curve of the second inverter; and based on the offset of the third impedance characteristic curve relative to the first impedance characteristic curve, and the offset of the fourth impedance characteristic curve relative to the second impedance characteristic curve, the impedance characteristic mutual influence change curve of the two adjacent inverters at the distance between the first inverter and the second inverter is determined; Based on the impedance characteristic mutual influence change curve of each pair of the photovoltaic components at the corresponding component distance in the three-dimensional model of the photovoltaic components, determining the functional relationship between the impedance characteristic mutual influence change curve between two adjacent inverters as the distance changes; In the case where an abnormal inverter is detected in the distributed photovoltaic system, the distance between the abnormal inverter and its adjacent inverter is calculated by using a pre-constructed functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters as a function of the distance, so as to obtain the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter; Based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are solved; Based on the first target harmonic compensation parameter and the second target harmonic compensation parameter, harmonic optimization is performed on the abnormal inverter and the adjacent inverter respectively.

2. The method according to claim 1, characterized in that Also includes: Based on the DC bus voltage, switching frequency and inductor current information of each inverter in the distributed photovoltaic system, it is detected whether each inverter is an abnormal inverter.

3. The method according to claim 2, characterized in that The detecting whether each inverter is an abnormal inverter based on the DC bus voltage, switching frequency and inductor current information of each inverter in the distributed photovoltaic system includes: Performing short-time Fourier transform on the DC bus voltage variation curve, the switching frequency variation curve and the inductor current variation curve of the inverter in the first time window respectively to obtain DC bus voltage spectrum data, switching frequency spectrum data and inductor current spectrum data; Determining a voltage fluctuation amplitude based on a ratio between a fundamental component and a DC component in the DC bus voltage spectrum data; Determining a switching frequency deviation value based on a difference between a fundamental frequency in the switching frequency spectrum data and a preset switching frequency; Determining the degree of inductor current distortion based on a ratio between a harmonic component and a fundamental component in the inductor current spectrum data; Based on the voltage fluctuation amplitude, the switching frequency deviation value and the inductor current distortion, it is determined whether the inverter is an abnormal inverter.

4. The method according to claim 1, characterized in that: The method of solving the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, includes: Determine an initial population, wherein the initial population includes a plurality of individuals, each of the individuals corresponds to a first harmonic compensation parameter of the abnormal inverter and a second harmonic compensation parameter of the adjacent inverter, wherein the harmonic compensation parameters include dead time, switching frequency and modulation ratio; Based on the nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter, determine a first mapping function between the dead time, switching frequency and modulation ratio of the inverter and the impedance characteristics of the inverter, and a second mapping function between the impedance characteristics of the inverter and the output harmonic content of the inverter; Starting from the initial population, the following iterative operations are performed: The first mapping function is used to calculate the first harmonic compensation parameter and the second harmonic compensation parameter corresponding to each of the individuals in the current iteration population, respectively, to obtain the first impedance characteristic of the abnormal inverter and the second impedance characteristic of the adjacent inverter corresponding to each of the individuals; Based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, the first impedance characteristic and the second impedance characteristic corresponding to each of the individuals are adjusted respectively to obtain the third impedance characteristic of the abnormal inverter and the fourth impedance characteristic of the adjacent inverter corresponding to each of the individuals; The second mapping function is used to calculate the third impedance characteristic of the abnormal inverter and the fourth impedance characteristic of the adjacent inverter corresponding to each of the individuals, respectively, to obtain the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals; When the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals meet the second preset condition, a target individual is determined among the individuals based on the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals, and the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are determined respectively based on the first harmonic compensation parameter of the abnormal inverter and the second harmonic compensation parameter of the adjacent inverter corresponding to the target individual.

5. The method according to claim 4, characterized in that Also includes: When the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals do not meet the second preset condition, cross-mutation is performed on each of the individuals in the current iterative population to obtain the next iterative population, and based on the next iterative population, the iterative operation is returned to be executed.

6. A harmonic optimization device for renewable energy power generation, characterized in that: include: A three-dimensional model building module is used to build a three-dimensional model of photovoltaic components based on the location information of each inverter in the distributed photovoltaic system; The inverter operation simulation module is used to perform the following simulation process for any pair of adjacent inverters in the three-dimensional model of the photovoltaic assembly: control the first inverter of the two adjacent inverters to operate under the first simulation condition, and control the second inverter not to operate, to obtain a first impedance characteristic curve of the first inverter; control the second inverter to operate under the first simulation condition, and control the first inverter not to operate, to obtain a second impedance characteristic curve of the second inverter; control the first inverter and the second inverter to operate simultaneously under the simulation condition, to obtain a third impedance characteristic curve of the first inverter and a fourth impedance characteristic curve of the second inverter; and determine the impedance characteristic mutual influence change curve of the two adjacent inverters at the distance between the first inverter and the second inverter based on the offset of the third impedance characteristic curve relative to the first impedance characteristic curve, and the offset of the fourth impedance characteristic curve relative to the second impedance characteristic curve; A functional relationship determination module, for determining a functional relationship between the impedance characteristic mutual influence change curves of two adjacent inverters as the distance changes based on the impedance characteristic mutual influence change curves of each pair of components at their corresponding component distances in the three-dimensional model of the photovoltaic components; A curve determination module is used to calculate the distance between the abnormal inverter and its adjacent inverter by using a pre-built functional relationship of the impedance characteristic mutual influence change curve between two adjacent inverters with the change of distance when an abnormal inverter is detected in the distributed photovoltaic system, so as to obtain the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter; A compensation parameter solving module, used to solve the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, and the pre-trained nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter; A harmonic optimization module is used to perform harmonic optimization on the abnormal inverter and the adjacent inverter based on the first target harmonic compensation parameter and the second target harmonic compensation parameter.

7. The device according to claim 6, characterized in that The compensation parameter solving module comprises: A population initialization unit, used for determining an initial population, wherein the initial population includes a plurality of individuals, each of the individuals corresponds to a first harmonic compensation parameter of the abnormal inverter and a second harmonic compensation parameter of the adjacent inverter, wherein the harmonic compensation parameters include a dead time, a switching frequency and a modulation ratio; A function decomposition unit, configured to determine a first mapping function between the dead time, switching frequency and modulation ratio of the inverter and the impedance characteristic of the inverter, and a second mapping function between the impedance characteristic of the inverter and the output harmonic content of the inverter based on a nonlinear mapping relationship between the dead time, switching frequency and modulation ratio of the inverter and the output harmonic content of the inverter; The population iteration unit is used to perform the following iterative operations starting from the initial population: The first mapping function is used to calculate the first harmonic compensation parameter and the second harmonic compensation parameter corresponding to each of the individuals in the current iteration population, respectively, to obtain the first impedance characteristic of the abnormal inverter and the second impedance characteristic of the adjacent inverter corresponding to each of the individuals; Based on the impedance characteristic mutual influence change curve between the abnormal inverter and the adjacent inverter, the first impedance characteristic and the second impedance characteristic corresponding to each of the individuals are adjusted respectively to obtain the third impedance characteristic of the abnormal inverter and the fourth impedance characteristic of the adjacent inverter corresponding to each of the individuals; The second mapping function is used to calculate the third impedance characteristic of the abnormal inverter and the fourth impedance characteristic of the adjacent inverter corresponding to each of the individuals, respectively, to obtain the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals; When the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals meet the second preset condition, a target individual is determined among the individuals based on the first output harmonic content of the abnormal inverter and the second output harmonic content of the adjacent inverter corresponding to each of the individuals, and the first target harmonic compensation parameter of the abnormal inverter and the second target harmonic compensation parameter of the adjacent inverter are determined respectively based on the first harmonic compensation parameter of the abnormal inverter and the second harmonic compensation parameter of the adjacent inverter corresponding to the target individual.

8. A harmonic optimization system for renewable energy power generation, comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

9. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-5.

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