Dynamic characteristic evaluation method for static var generator and related equipment

Abstract

The invention discloses a static var generator dynamic characteristic evaluation method and related equipment. In the method, firstly, according to power system parameters and wind turbine generator data, a distributed power supply bearing capacity model is constructed and then solved, and reactive power output data of a static var generator and power system voltage data are obtained; performing spectral analysis on the reactive power output data of the static var generator and the voltage data of the power system to obtain characteristic data; and finally, calculating the feature data by using multiple indexes to obtain a comprehensive score. In the process, reactive power output data of a static var generator and voltage data of a power system can be obtained in a new energy access scene by utilizing a distributed power supply bearing capacity model, deeper data analysis can be provided by utilizing a spectral analysis method, and finally, characteristic data is calculated through multiple indexes, so that a new energy access scene is obtained. Therefore, the actual performance of the static var generator in the new energy environment can be accurately reflected.

Description

Technical Field

[0001] The present application relates to the technical field of evaluating characteristics of a static VAR generator, and in particular to a method for evaluating dynamic characteristics of a static VAR generator and related equipment. Background Art

[0002] With the rapid development of renewable energy, distributed power sources (such as solar photovoltaic and wind power) are increasingly used in distribution networks. However, the randomness and intermittent characteristics of distributed power sources pose great challenges to the stable operation of the power grid. In order to meet these challenges, the power grid needs to have higher flexibility and regulation capabilities, among which reactive power compensation and voltage regulation are key factors. Static Var Generator (SVG) is an important dynamic reactive power compensation device that quickly responds to system voltage changes, adjusts reactive power, and improves power quality and grid stability.

[0003] In order to better utilize distributed power sources and improve the power quality and stability of the power grid, it is necessary to study how to comprehensively consider control measures such as SVG and OLTC, accurately evaluate the carrying capacity of distributed power sources, and analyze the dynamic characteristics of SVG on this basis, which has become a hot issue in power system research. However, there is currently no method to consider the dynamic characteristics of SVG in the carrying capacity of distributed power sources in combination with new energy. In this regard, this application proposes a comprehensive evaluation method combining new energy, SVG and distributed power carrying capacity. Summary of the invention

[0004] The present application provides a method and related equipment for evaluating the dynamic characteristics of a static VAR generator. In the scenario of wind turbines in renewable energy, combined with a model of distributed power supply carrying capacity, a comprehensive score of the dynamic characteristics of SVG can be obtained, so that the actual performance of SVG in a renewable energy environment can be accurately reflected based on the comprehensive score.

[0005] In a first aspect, the present application provides a method for evaluating dynamic characteristics of a static VAR generator, the method comprising:

[0006] Construct a distributed power generation capacity model based on power system parameters and wind turbine data;

[0007] Solve the distributed power generation capacity model to obtain the reactive power output data of the static VAR generator and the voltage data of the power system;

[0008] Perform spectrum analysis on the reactive output data of the static VAR generator and the voltage data of the power system to obtain characteristic data;

[0009] The characteristic data are calculated using multiple indicators to obtain a comprehensive score, which is used to evaluate the dynamic characteristics of the static VAR generator.

[0010] Optionally, a distributed power generation capacity model is constructed based on power system parameters and wind turbine data, including:

[0011] According to the reactive power parameters of the static VAR generator in the power system parameters, a reactive power regulation model is constructed;

[0012] According to the voltage parameters of the on-load tap-changing transformer in the power system parameters, a substation bus voltage model is constructed;

[0013] Construct a wind power output model based on wind turbine data;

[0014] According to the reactive power regulation model, substation bus voltage model, wind power output model and distribution network dynamic model, the distributed power generation carrying capacity model is obtained.

[0015] Optionally, a wind power output model is constructed based on the wind turbine data, including:

[0016] Combining the Weibull distribution function and wind turbine data, a wind power output model is constructed, where the formula of the wind power output model is as follows:

[0017]

[0018] Where f(v) represents the probability density function of the Weibull distribution of wind speed; v represents the actual wind speed, c represents the scale parameter; k represents the shape parameter; F(v) represents the cumulative distribution function of the Weibull distribution;

[0019] Optionally, the construction process of the distribution network dynamic model includes:

[0020] According to the distributed generation parameters and node parameters in the power system parameters, a distribution network dynamic model is established, which specifically includes the following formulas:

[0021]

[0022] Among them, P DG,i represents the active power of the distributed generation connected to node i; N represents the number of nodes; P loss Indicates network loss.

[0023] The above formula represents the maximum value of the active power of the distributed generation, where the constraints of the distribution network dynamic model include the following formulas:

[0024]

[0025] Among them, P j represents the active power injected into node j; B and E represent the node set and branch set; I ijrepresents the current of branch ij, the superscript and subscript represent the upper and lower limits of the current respectively; r ij Represents the resistance of branch ij; P ij represents the active power flowing from node i to node j; P jk represents the active power flowing from node j to node k; g j represents the conductance of node j; v j is the voltage at node j; Q j represents the reactive power injected by node j; x ij Represents the reactance of branch ij; Q ij represents the reactive power flowing from node i to node j; Q jk represents the reactive power flowing from node j to node k; b j is the susceptance of node j.

[0026] Optionally, the characteristic data includes standardized reactive power output data and voltage data, and the reactive power output data of the static VAR generator and the voltage data of the power system are subjected to spectrum analysis to obtain the characteristic data, including:

[0027] Use the moving average filtering method to denoise the reactive power output data and voltage data to obtain the denoised time domain signal;

[0028] The denoised time domain signal is converted into a frequency domain signal using fast Fourier transform;

[0029] According to the amplitude and phase of the frequency domain signal, the main frequency component and the amplitude of the main frequency component are extracted;

[0030] The main frequency component and the amplitude of the main frequency component are standardized to obtain standardized reactive power output data and voltage data.

[0031] Optionally, multiple indicators are used to calculate the characteristic data to obtain a comprehensive score, including:

[0032] According to the opinions of experts, determine the weight of reactive power compensation capability among multiple indicators and the weight of system voltage regulation capability among multiple indicators;

[0033] The characteristic data is weighted and summed according to the weight of the reactive power compensation capability and the weight of the system voltage regulation capability to obtain a comprehensive score.

[0034] Optionally, the method further comprises:

[0035] Draw a curve of the comprehensive score changing over time;

[0036] The trend of the curve is analyzed to evaluate the dynamic characteristics of the static VAR generator under the new energy environment and distributed power generation capacity.

[0037] In a second aspect, the present application provides a static VAR generator dynamic characteristics evaluation device 400, the device comprising:

[0038] A construction unit, used for constructing a distributed power source carrying capacity model according to power system parameters and wind turbine data;

[0039] A solving unit, used for solving the distributed power source carrying capacity model to obtain reactive power output data of the static VAR generator and voltage data of the power system;

[0040] An analysis unit, used for performing spectrum analysis on reactive output data of the static VAR generator and voltage data of the power system to obtain characteristic data;

[0041] The calculation unit is used to calculate the characteristic data using multiple indicators to obtain a comprehensive score, and the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator.

[0042] Optionally, the building block is specifically used for:

[0043] According to the reactive power parameters of the static VAR generator in the power system parameters, a reactive power regulation model is constructed;

[0044] According to the voltage parameters of the on-load tap-changing transformer in the power system parameters, a substation bus voltage model is constructed;

[0045] Construct a wind power output model based on wind turbine data;

[0046] According to the reactive power regulation model, substation bus voltage model, wind power output model and distribution network dynamic model, the distributed power generation carrying capacity model is obtained.

[0047] Optionally, when the construction unit constructs the wind power output model according to the wind turbine generator set data, it is specifically used to:

[0048] The wind power output model is constructed by combining the Weibull distribution function and the wind turbine data, wherein the formula of the wind power output model is as follows:

[0049]

[0050] Where f(v) represents the probability density function of the Weibull distribution of wind speed; v represents the actual wind speed, c represents the scale parameter; k represents the shape parameter; and F(v) represents the cumulative distribution function of the Weibull distribution.

[0051] Optionally, the construction unit 401 is further used for:

[0052] According to the distributed power generation parameters and node parameters in the power system parameters, a distribution network dynamic model is established, which specifically includes the following formulas:

[0053]

[0054] Among them, P DG,i represents the active power of the distributed generation connected to node i; N represents the number of nodes; P loss Indicates network loss;

[0055] The above formula represents the maximum value of the active power of the distributed power source, wherein the constraint conditions of the distribution network dynamic model include the following formula:

[0056]

[0057] Among them, P j represents the active power injected into node j; B and E represent the node set and branch set; I ij represents the current of branch ij, the superscript and subscript represent the upper and lower limits of the current respectively; r ij Represents the resistance of branch ij; P ij represents the active power flowing from node i to node j; P jk represents the active power flowing from node j to node k; g j represents the conductance of node j; v j is the voltage at node j; Q j represents the reactive power injected by node j; x ij Represents the reactance of branch ij; Q ij represents the reactive power flowing from node i to node j; Q jk represents the reactive power flowing from node j to node k; b j is the susceptance of node j.

[0058] Optionally, the characteristic data includes standardized reactive power output data and voltage data, and the analysis unit is specifically used for:

[0059] Use the moving average filtering method to denoise the reactive power output data and voltage data to obtain the denoised time domain signal;

[0060] The denoised time domain signal is converted into a frequency domain signal using fast Fourier transform;

[0061] According to the amplitude and phase of the frequency domain signal, the main frequency component and the amplitude of the main frequency component are extracted;

[0062] The main frequency component and the amplitude of the main frequency component are standardized to obtain standardized reactive power output data and voltage data.

[0063] Optionally, the computing unit is specifically used for:

[0064] According to the opinions of experts, determine the weight of reactive power compensation capability among multiple indicators and the weight of system voltage regulation capability among multiple indicators;

[0065] The characteristic data is weighted and summed according to the weight of the reactive power compensation capability and the weight of the system voltage regulation capability to obtain a comprehensive score.

[0066] Optionally, the device further comprises:

[0067] A drawing unit, used for drawing a curve showing the comprehensive score changing over time;

[0068] The evaluation unit is used to analyze the trend of the curve to evaluate the dynamic characteristics of the static VAR generator under the new energy environment and the distributed power supply capacity.

[0069] In a third aspect, the present application provides an electronic device, the electronic device comprising a memory and a processor:

[0070] The memory is used to store computer programs;

[0071] The processor is used to execute the method provided by the first aspect according to the computer program.

[0072] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program for executing the method provided in the first aspect.

[0073] It can be seen that this application has the following beneficial effects:

[0074] The present application provides a method for evaluating the dynamic characteristics of a static VAR generator. First, after constructing a distributed power supply carrying capacity model according to power system parameters and wind turbine data, the distributed power supply carrying capacity model is solved to obtain the reactive output data and power system voltage data of the static VAR generator; then the reactive output data and power system voltage data of the static VAR generator are subjected to spectrum analysis to obtain characteristic data; finally, the characteristic data are calculated using multiple indicators to obtain a comprehensive score, which is used to evaluate the dynamic characteristics of the static VAR generator. In this process, the distributed power supply carrying capacity model can be used to obtain the reactive output data and power system voltage data of the static VAR generator under the new energy access scenario (wind turbine), and then the spectrum analysis method is used to convert the reactive output data and power system voltage data into frequency domain signals and then extract the characteristic data, so as to provide a more in-depth data analysis, and finally the characteristic data is calculated by multiple indicators to obtain a comprehensive score, wherein the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0076] Figure 1 A schematic flow chart of an embodiment of a method for evaluating dynamic characteristics of a static VAR generator according to an embodiment of the present application;

[0077] Figure 2 A schematic diagram of converting a time domain signal into a frequency domain signal in an embodiment of the present application;

[0078] Figure 3 A schematic flow chart of another embodiment of a method for evaluating dynamic characteristics of a static VAR generator in an embodiment of the present application;

[0079] Figure 4 This is a schematic structural diagram of a static VAR generator dynamic characteristics evaluation device in an embodiment of the present application;

[0080] Figure 5 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application. DETAILED DESCRIPTION

[0081] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0082] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0083] First, the technical terms involved in the embodiments of the present application are explained:

[0084] (1) Static Var Generator (SVG): It is a device that adjusts reactive power by quickly responding to system voltage changes and can effectively suppress voltage fluctuations. SVG dynamically adjusts reactive power to prevent voltage flicker and short-term voltage changes from affecting equipment. Installing SVG in a substation can provide reactive power compensation, especially when the load changes greatly, significantly improving the stability and power supply quality of the power system.

[0085] (2) On-Load Tap Changer (OLTC): As a traditional voltage regulation device, it maintains the system voltage within the set range by adjusting the tap position of the transformer. The tap adjustment of OLTC flexibly adjusts the tap position of the on-load tap changing transformer, which can quickly adjust the system voltage level and effectively prevent the voltage over-limit problem caused by the access of distributed power sources.

[0086] At present, under the current new energy background, the access of distributed power sources in the power system has increased significantly, which has brought new challenges to the stable operation of the power grid. The access capacity and location of distributed power sources as well as their intermittent and random characteristics make it difficult for traditional power grid control methods to cope with these changes. Among them, the static VAR generator, as a key dynamic reactive power compensation device, can quickly respond to system voltage changes and improve power quality and grid stability by adjusting reactive power. However, existing methods mostly focus on SVG dynamic characteristics test platforms or SVG controller parameter identification, and fail to conduct a comprehensive evaluation in combination with the carrying capacity of distributed power sources.

[0087] Specifically, existing research mainly focuses on the dynamic characteristics and control parameters of SVG itself, while ignoring the impact of distributed power sources on the power grid under different access conditions, and also fails to fully consider the maximum carrying capacity of distributed power sources and their interaction with SVG, so the applicability and accuracy of the evaluation results are limited. In addition, the complexity of power grid operation in the new energy environment increases. The evaluation of the carrying capacity of distributed power sources not only needs to consider the topological structure, load characteristics and access of distributed power sources of the power grid, but also needs to combine active control measures such as on-load tap-changing transformers and SVG. Existing technologies have failed to provide a unified comprehensive evaluation method to solve this complex problem, resulting in a significant reduction in the applicability and accuracy of the evaluation results. Therefore, how to comprehensively consider the carrying capacity of distributed power sources and the dynamic characteristics of SVG in the new energy environment and establish a unified evaluation system is an urgent problem to be solved in current research.

[0088] In addition, the current evaluation of SVG dynamic characteristics is usually based on a single dimension, lacking a comprehensive analysis method of multiple indicators. This single-dimensional evaluation method mainly focuses on a specific aspect, such as SVG's heat dissipation performance, control parameter identification, or other single characteristics. For example, some studies evaluate the heat dissipation capacity of SVG heat pipe radiators by testing their dynamic characteristics, while other studies identify SVG controller parameters based on real-time digital simulation system (RTDS) data.

[0089] However, this single-dimensional evaluation method has significant limitations. First, it cannot fully reflect the comprehensive performance of SVG in different application scenarios. For example, only evaluating the heat dissipation performance of SVG, although it can understand its heat dissipation effect under high load conditions, it cannot draw conclusions about its impact on reactive power compensation capability and voltage regulation capability in actual power grid operation. Similarly, focusing only on controller parameter identification cannot fully understand the overall performance of SVG in dynamic operation. Second, the lack of multi-index decision analysis means that the evaluation process cannot comprehensively consider the relationship between multiple key performance indicators. In the power system, the reactive power compensation capability, system voltage regulation capability and power quality level of SVG are all crucial performance indicators. These indicators are interrelated and jointly affect the operation effect of the power grid. Therefore, the single-dimensional evaluation method cannot provide a comprehensive understanding of the dynamic characteristics of SVG, nor can it provide strong support for the formulation of power grid regulation strategies. However, the application of this method in existing research is relatively limited, and most studies have failed to conduct a comprehensive analysis from multiple dimensions such as reactive power compensation capability, system voltage regulation capability and power quality level. Therefore, the existing technology lacking multi-index decision analysis cannot fully reflect the comprehensive performance of SVG, nor can it effectively evaluate its overall impact on power grid operation.

[0090] In the embodiment of the present application, by combining new energy scenarios and distributed power supply carrying capacity to perform SVG dynamic characteristics analysis and multi-index decision analysis, a more comprehensive comprehensive evaluation system is established, which can accurately reflect the dynamic characteristics and comprehensive performance of SVG in a new energy environment.

[0091] In specific implementation, the method may include, for example: first, constructing a distributed power supply carrying capacity model based on power system parameters and wind turbine data, then solving the distributed power supply carrying capacity model to obtain reactive output data of the static VAR generator and power system voltage data; then performing spectrum analysis on the reactive output data of the static VAR generator and the power system voltage data to obtain characteristic data; finally, calculating the characteristic data using multiple indicators to obtain a comprehensive score, which is used to evaluate the dynamic characteristics of the static VAR generator.

[0092] It can be seen that the method provided by the implementation of this application can obtain the reactive output data and power system voltage data of the static VAR generator under the new energy access scenario (wind turbine) by using the distributed power supply carrying capacity model, and then use the spectrum analysis method to convert the reactive output data and the power system voltage data into frequency domain signals and then extract the feature data, so as to provide a more in-depth data analysis, and finally calculate the feature data through multiple indicators to obtain a comprehensive score, wherein the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator.

[0093] To facilitate understanding of the specific implementation of a static VAR generator dynamic characteristics evaluation method provided in an embodiment of the present application, it will be described below with reference to the accompanying drawings.

[0094] It should be noted that the subject implementing the static VAR generator dynamic characteristics evaluation method may be a static VAR generator dynamic characteristics evaluation device provided in the embodiment of the present application, and the static VAR generator dynamic characteristics evaluation device may be carried in an electronic device or a functional module of an electronic device. The electronic device in the embodiment of the present application may be any device capable of implementing the static VAR generator dynamic characteristics evaluation method in the embodiment of the present application, for example, an Internet of Things (IoT) device.

[0095] Figure 1 A flow chart of a static VAR generator dynamic characteristics evaluation method provided in an embodiment of the present application. The method can also be applied to a static VAR generator dynamic characteristics evaluation device, which can be, for example, Figure 4 The static VAR generator dynamic characteristic evaluation device 400 shown in FIG. 1 may also be integrated in the static VAR generator dynamic characteristic evaluation device 400. Figure 5 Functional modules in the electronic device 500 are shown.

[0096] like Figure 1 As shown, the method includes the following S101 to S104:

[0097] S101: Construct a distributed power generation capacity model based on power system parameters and wind turbine data.

[0098] In order to evaluate the dynamic characteristics of the static VAR generator under the background of new energy, firstly, after constructing the distributed power supply carrying capacity model according to the power system parameters and wind turbine data, the distributed power supply carrying capacity model is solved to obtain the reactive output data of the static VAR generator and the power system voltage data; then the reactive output data of the static VAR generator and the power system voltage data are subjected to spectrum analysis to obtain characteristic data; finally, the characteristic data is calculated using multiple indicators to obtain a comprehensive score, which is used to evaluate the dynamic characteristics of the static VAR generator. Therefore, the embodiment of the present application can construct a distributed power supply carrying capacity model through S101, and make preliminary preparations for the subsequent acquisition of the reactive output data of the static VAR generator and the power system voltage data.

[0099] As an example, S101 may include: S1011, constructing a reactive power regulation model based on the reactive power parameters of the static VAR generator in the power system parameters; S1012, constructing a substation bus voltage model based on the voltage parameters of the on-load tap-changing transformer in the power system parameters; S1013, constructing a wind power output model based on wind turbine data; S1014, obtaining a distributed power source carrying capacity model based on the reactive power regulation model, the substation bus voltage model, the wind power output model and the distribution network dynamic model.

[0100] In the above S1011, since the static VAR generator SVG is the best solution in the field of reactive power control. Compared with traditional phase shifters, capacitor reactors, and traditional SVC represented by thyristor controlled reactors TCR, SVG has incomparable advantages. The reactive power compensation of the static VAR generator can improve the voltage rise and reactive devices can be put into use at nodes with obvious voltage rise. Therefore, this application combines the reactive power parameters of the static VAR generator to construct a reactive power regulation model, wherein the reactive power regulation model can be shown as the following formula (1):

[0101]

[0102] in, and Indicates the lower and upper limits of SVG compensation power; Indicates the actual compensation power of SVG; B SVG Represents a node collection containing SVG.

[0103] Since the capacity of distributed power generation in the distribution network is large, problems such as power flow backflow and overvoltage may occur. Can be negative.

[0104] In the above S1012, after the on-load tap-changing transformer is added to the power system, the substation bus node voltage can be adjusted by adjusting the low-voltage side voltage of the on-load tap-changing transformer, that is, a substation bus voltage model can be established as shown in the following formula (2):

[0105]

[0106] in, Indicates the voltage value of the high-voltage side of the transformer, V j,min 、V j,max Indicates the upper and lower limits of the bus voltage on the low-voltage side of the substation, B OLTC Represents a set of substations containing OLTC; Indicates the upper and lower limits of the adjustable ratio, r j,t It is the ratio of the actual voltage on the secondary side to the actual voltage on the primary side.

[0107] In the above S1013, in terms of new energy, Weibull distribution is suitable for describing wind power output. In the research of wind and solar scene generation, random optimization scheduling, etc., it is often assumed that the wind speed obeys Weibull distribution. In this regard, in the embodiment of the present application, the Weibull distribution function and wind turbine data are combined to construct the wind power output model, wherein the formula of the wind power output model is shown in the following formula (3) and formula (4):

[0108]

[0109] Where f(v) represents the probability density function of the Weibull distribution of wind speed; v represents the actual wind speed, c represents the scale parameter; k represents the shape parameter; and F(v) represents the cumulative distribution function of the Weibull distribution.

[0110] In the above S1014, a distributed power supply carrying capacity model is obtained according to the reactive power regulation model, the substation bus voltage model, the wind power output model and the distribution network dynamic model, that is, the distributed power supply carrying capacity model includes the reactive power regulation model, the substation bus voltage model, the wind power output model and the distribution network dynamic model, wherein the reactive power regulation model, the substation bus voltage model and the wind power output model are as described above, and the construction process of the distribution network dynamic model includes:

[0111] According to the distributed power generation parameters and node parameters in the power system parameters, a distribution network dynamic model is established, which specifically includes the following formula (5) as the objective function:

[0112]

[0113] Among them, P DG,i represents the active power of the distributed generation connected to node i; N represents the number of nodes; P loss Indicates network loss.

[0114] The above formula represents the maximum value of the active power of the distributed power source, wherein the constraints of the distribution network dynamic model include the following formulas (6) to (11):

[0115]

[0116] Among them, P j represents the active power injected into node j; B and E represent the node set and branch set; I ij represents the current of branch ij, the superscript and subscript represent the upper and lower limits of the current respectively; r ij Represents the resistance of branch ij; P ij represents the active power flowing from node i to node j; P jk represents the active power flowing from node j to node k; g j represents the conductance of node j; v j is the voltage at node j; Q j represents the reactive power injected by node j; x ij Represents the reactance of branch ij; Q ij represents the reactive power flowing from node i to node j; Q jk represents the reactive power flowing from node j to node k; b j is the susceptance of node j.

[0117] In this process, by combining the load capacity analysis of distributed power sources, the dynamic characteristics of SVG can be accurately evaluated under the premise of considering the access of new energy. Among them, the load capacity analysis of distributed power sources can provide the operating status of the power grid under different working conditions, providing more comprehensive data support for the dynamic characteristics evaluation of SVG. In addition, combined with the evaluation method of distributed power supply load capacity, the performance of SVG in the actual operating environment can be better reflected.

[0118] S102: Solve the distributed power generation capacity model to obtain reactive power output data of the static VAR generator and power system voltage data.

[0119] The decision variables of the distributed power generation capacity model in the embodiment of the present application include reactive compensation power, OLTC tap position, etc. Therefore, in mathematical essence, the solution of the distributed power generation capacity model is a mixed integer nonlinear programming problem that includes both continuous variables and discrete variables.

[0120] In the embodiment of the present application, the distributed power supply carrying capacity model composed of the above-mentioned reactive power regulation model, substation bus voltage model, wind power output model and distribution network dynamic model is solved to obtain the output of the static VAR generator, that is, the reactive output data Q of the static VAR generator SVG(t)and power system voltage data V(t). The reactive output data of the static VAR generator is the reactive power generated by the static VAR generator.

[0121] S103: Performing spectrum analysis on the reactive output data of the static VAR generator and the voltage data of the power system to obtain characteristic data.

[0122] As an example, S103 may include: S1031, using a moving average filter method to denoise reactive output data and voltage data to obtain a denoised time domain signal; S1032, using a fast Fourier transform to convert the denoised time domain signal into a frequency domain signal; S1033, extracting the main frequency component and the amplitude of the main frequency component based on the amplitude and phase of the frequency domain signal; S1034, standardizing the main frequency component and the amplitude of the main frequency component to obtain standardized reactive output data and voltage data.

[0123] In the above S1031, the moving average filtering method is used to perform denoising on the reactive power output data and the voltage data. Specifically, the following formulas (12) and (13) can be used:

[0124]

[0125] Among them, Q f (t) and V f (t) represents the filtered value of the reactive power output data and system voltage data of SVG at time point t; M represents the size of the moving window; Q(ti) and V(ti) represent the reactive power output data and system voltage data of SVG at time point ti.

[0126] In the above S1032, the denoised time domain signal is converted into a frequency domain signal using fast Fourier transform, which can be specifically achieved using the following formulas (14) and (15):

[0127]

[0128]

[0129] Among them, Q f (f) represents the frequency domain signal of the reactive power output data filtering value of SVG; V f (f) represents the frequency domain signal of the filtered value of the system voltage data; T represents the total number of sampling points of the signal, that is, N points are sampled in one cycle; Q f (t) and V f (t) represents the filtered value of the reactive power output data and system voltage data of SVG at time point t; n represents the index of the discrete sampling point in time, from 0 to T-1; f represents the discrete value of frequency; j represents an arbitrary scale.

[0130] In order to perform in-depth processing on data processing and data analysis, the embodiment of the present application intends to make full use of the frequency domain characteristics to perform in-depth analysis on the data, that is, Figure 2 As shown, the time domain signal is converted into the frequency domain signal using Fast Fourier Transform (FFT), revealing the frequency characteristics of the signal and providing more in-depth data analysis.

[0131] In the above S1033, the amplitude and phase of the above frequency domain signal can be calculated first, and the characteristic frequency and the amplitude of the characteristic frequency can be extracted. Specifically, the following formula (16) and formula (17) can be used:

[0132]

[0133] Among them, |Q f (f) represents the amplitude of the frequency domain signal of the reactive power output data filtering value of SVG; Q f (f) Frequency domain signal representing the reactive power output data filtering value of SVG; ∠Q f (f) represents the phase of the frequency domain signal of the reactive power output data filtering value of SVG; |V f (f) | The amplitude of the frequency domain signal representing the filtered value of the system voltage data; V f (f) Frequency domain signal representing the filtered value of system voltage data; ∠V f (f) Phase of the frequency domain signal representing the filtered value of the system voltage data.

[0134] Then, the key features are extracted from the extracted characteristic frequencies and the amplitudes of the characteristic frequencies, including the main frequency components and the amplitudes of the main frequency components. Specifically, the following formulas (18) and (19) can be used:

[0135]

[0136]

[0137] Among them, F Q The main frequency component in the frequency domain signal representing the filtered value of reactive power output data of SVG; |Q f (f) | The amplitude of the frequency domain signal representing the reactive power output data filtering value of SVG; A Q The amplitude corresponding to the main frequency component in the frequency domain signal representing the reactive power output data filtering value of SVG; F V The main frequency component in the frequency domain signal representing the filtered value of the system voltage data; |V f (f) | The amplitude of the frequency domain signal representing the filtered value of the system voltage data; A V The amplitude corresponding to the main frequency component in the frequency domain signal representing the filtered value of the system voltage data.

[0138] In the above S1034, the extracted key features are normalized so that the data of the key features are at the same level. Specifically, the following formulas (20) and (21) can be used:

[0139]

[0140] Among them, Q norm Indicates the characteristic data in the reactive power output data of SVG; Q f The key features in the frequency domain signal representing the filtered value of reactive power output data of SVG; Q fmax The maximum value of the key feature in the frequency domain signal representing the filtered value of the reactive output data of the SVG; Q fmin The minimum value of the key feature in the frequency domain signal representing the filtered value of the reactive power output data of SVG; V norm Represents the characteristic data in the system voltage data; V f The key features in the frequency domain signal representing the filtered value of the system voltage data of SVG; V fmax The maximum value of the key feature in the frequency domain signal representing the filtered value of the system voltage data; V fmin The minimum value of the key feature in the frequency domain signal representing the filtered value of the system voltage data.

[0141] In this process, the spectrum analysis method is used to convert the reactive power output and system voltage data of SVG from the time domain to the frequency domain, so that the key characteristics of the frequency can be extracted, the frequency characteristics of the signal can be revealed, and more in-depth data analysis can be provided.

[0142] S104: Calculate the characteristic data using multiple indicators to obtain a comprehensive score, where the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator.

[0143] As an example, S104 may include: determining the weight of reactive power compensation capability among multiple indicators and the weight of system voltage regulation capability among multiple indicators according to expert opinions; performing weighted sum calculation on the characteristic data according to the weight of reactive power compensation capability and the weight of system voltage regulation capability to obtain a comprehensive score, that is, the comprehensive score may be obtained as shown in the following formula (22):

[0144] S=ω Q ×Q norm +ω V ×V norm Formula (22)

[0145] Among them, S represents the comprehensive score; ω Q Represents the weight of reactive power compensation capability; ω V Indicates the weight of the ability to regulate system voltage; Q norm Represents the characteristic data in the reactive power output data of SVG; Vnorm Indicates characteristic data in system voltage data.

[0146] In this process, the multi-index decision analysis method is combined to calculate the comprehensive score by determining the weights of reactive power compensation capability, system voltage regulation capability and power quality level. This comprehensive application method can improve the depth and accuracy of the evaluation results.

[0147] In addition, after obtaining the comprehensive score, the embodiment of the present application can also draw a curve showing the change of the comprehensive score over time to intuitively display the dynamic characteristics of the SVG. The trend of the curve can also be analyzed to evaluate the dynamic characteristics of the static VAR generator under the new energy environment and the distributed power supply carrying capacity.

[0148] For example, the peak and valley of the curve can be analyzed based on the trend of the curve. In other words, the peak area indicates that the SVG has strong reactive power compensation capability, good system voltage regulation effect, and high power quality during this period. In the low peak area, the SVG has weak reactive power compensation capability, poor system voltage regulation effect, and low power quality during this period, which means that the SVG performance is poor.

[0149] It can be seen that the embodiment of the present application can obtain the reactive output data and power system voltage data of the static VAR generator under the new energy access scenario (wind turbine) by utilizing the distributed power supply carrying capacity model, and then use the spectrum analysis method to convert the reactive output data and the power system voltage data into frequency domain signals and extract the feature data, thereby providing a more in-depth data analysis. Finally, the feature data is calculated through multiple indicators to obtain a comprehensive score, wherein the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator.

[0150] In order to make the method provided in the embodiment of the present application clearer and easier to understand, Figure 3 A specific example of this method is described.

[0151] like Figure 3 As shown, the implementation may include:

[0152] S301: Obtain power system parameters and wind turbine data.

[0153] In order to combine the scenario of new energy in the embodiment of the present application, wind power output can be considered, so it is necessary to use wind turbine data. For this, the embodiment of the present application first needs to obtain power system parameters and wind turbine data, where the power system parameters may include line transmission, voltage, current, power balance, OLTC parameters, SVG parameters and power generation equipment parameters, etc.

[0154] S302: Constructing a reactive power regulation model according to reactive power parameters of the static VAR generator in the power system parameters.

[0155] Since the static VAR generator is the best solution in the field of reactive power control at present, the reactive power regulation model is constructed according to the reactive power parameters of the static VAR generator. The specific construction equation formula is shown in the above embodiment and will not be repeated here.

[0156] S303: Constructing a substation bus voltage model according to the voltage parameters of the on-load tap-changing transformer in the power system parameters.

[0157] After the on-load tap-changing transformer is added in the embodiment of the present application, the bus node voltage of the substation can be adjusted by adjusting the low-voltage side voltage of the on-load tap-changing transformer. Therefore, the bus voltage model of the substation can be constructed according to the voltage parameters of the on-load tap-changing transformer. The specific construction equation formula is shown in the above embodiment and will not be repeated here.

[0158] S304: Construct a wind power output model based on the wind turbine data.

[0159] The embodiment of the present application needs to consider the new energy aspect, because the Weibull distribution is suitable for describing wind power output. In the research of wind and solar scene generation, random optimization scheduling, etc., it is often assumed that the wind speed obeys the Weibull distribution. Therefore, the embodiment of the present application can combine the Weibull distribution function and the wind turbine data to construct a wind power output model. The specific construction equation formula is shown in the above embodiment and will not be repeated here.

[0160] S305: Obtain a distributed power generation capacity model according to the reactive power regulation model, the substation bus voltage model, the wind power output model and the distribution network dynamic model.

[0161] The distributed power supply carrying capacity model in the embodiment of the present application may include a reactive power regulation model, a substation bus voltage model, a wind power output model and a distribution network dynamic model. That is, when the distributed power supply carrying capacity model is subsequently solved, the above-mentioned reactive power regulation model, substation bus voltage model, wind power output model and distribution network dynamic model are solved.

[0162] The dynamic model of the distribution network takes the maximum value of the active power of the distributed power source as the objective function, and the specific objective function is as shown in the above embodiment, which will not be elaborated here; other node parameters in the power system are used as constraints, and the specific constraints are as shown in the above embodiment, which will not be elaborated here.

[0163] S306: Solve the distributed power generation capacity model to obtain reactive power output data of the static VAR generator and power system voltage data.

[0164] S307: Perform spectrum analysis on the reactive output data of the static VAR generator and the voltage data of the power system to obtain characteristic data.

[0165] As an example, S307 may include: using a moving average filter method to perform denoising on reactive power output data and voltage data to obtain a denoised time domain signal; using a fast Fourier transform to convert the denoised time domain signal into a frequency domain signal; extracting the main frequency component and the amplitude of the main frequency component according to the amplitude and phase of the frequency domain signal; and performing standardization on the main frequency component and the amplitude of the main frequency component to obtain standardized reactive power output data and voltage data. That is, the time domain signal can be converted to the frequency domain to reveal the frequency characteristics of the signal and provide a more in-depth data analysis.

[0166] S308: Perform weighted sum calculation on the characteristic data according to the weight of the reactive power compensation capability in the multiple indicators and the weight of the system voltage regulation capability in the multiple indicators to obtain a comprehensive score.

[0167] The embodiment of the present application can use multi-index decision analysis, that is, combine multiple indicators such as reactive compensation capability, system voltage regulation capability and power quality level to perform comprehensive scoring, that is, the embodiment of the present application uses the weight of reactive compensation capability in multiple indicators and the weight of system voltage regulation capability in multiple indicators to perform weighted sum calculation on the characteristic data to obtain a comprehensive score. The specific calculation formula is shown in the above embodiment and will not be repeated here.

[0168] The embodiment of the present application combines spectrum analysis with multi-index decision analysis to evaluate the dynamic characteristics of SVG at a higher dimension.

[0169] S309: Draw a curve of the comprehensive score changing over time, and analyze the trend of the curve to evaluate the dynamic characteristics of the static VAR generator under the new energy environment and the distributed power supply carrying capacity.

[0170] First, we can draw a curve showing the changes of the comprehensive score over time to intuitively display the dynamic characteristics of SVG, observe the changing trend of the comprehensive score curve over time, identify the peaks and troughs of the score, and understand the dynamic characteristics of SVG in different time periods.

[0171] This embodiment provides a method for evaluating the dynamic characteristics of a static VAR generator. By combining the carrying capacity analysis of distributed power sources, the dynamic characteristics of SVG can be accurately evaluated under the premise of considering the access of new energy sources (wind turbines). The carrying capacity analysis of distributed power sources can provide the operating status of the power grid under different working conditions, providing more comprehensive data support for the evaluation of the dynamic characteristics of SVG. In addition, combined with the evaluation method of the carrying capacity of distributed power sources, the performance of SVG in the actual operating environment can be better reflected. In addition, the embodiment of the present application also uses spectrum analysis to convert time domain signals to frequency domains, revealing the frequency characteristics of signals and providing more in-depth data analysis; at the same time, multi-index decision analysis can combine multiple indicators such as reactive compensation capability, system voltage regulation capability and power quality level to perform comprehensive scoring; finally, by combining spectrum analysis and multi-index decision analysis, the dynamic characteristics of SVG can be evaluated at a higher dimension.

[0172] See also Figure 4 The embodiment of the present application provides a static VAR generator dynamic characteristic evaluation device 400, the device comprising:

[0173] A construction unit 401 is used to construct a distributed power generation capacity model according to power system parameters and wind turbine data;

[0174] A solving unit 402 is used to solve the distributed power generation capacity model to obtain reactive power output data of the static VAR generator and voltage data of the power system;

[0175] The analysis unit 403 is used to perform spectrum analysis on the reactive output data of the static VAR generator and the voltage data of the power system to obtain characteristic data;

[0176] The calculation unit 404 is used to calculate the characteristic data using multiple indicators to obtain a comprehensive score, and the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator.

[0177] Optionally, the construction unit 401 is specifically used for:

[0178] According to the reactive power parameters of the static VAR generator in the power system parameters, a reactive power regulation model is constructed;

[0179] According to the voltage parameters of the on-load tap-changing transformer in the power system parameters, a substation bus voltage model is constructed;

[0180] Construct a wind power output model based on wind turbine data;

[0181] According to the reactive power regulation model, substation bus voltage model, wind power output model and distribution network dynamic model, the distributed power generation carrying capacity model is obtained.

[0182] Optionally, when the construction unit 401 constructs the wind power output model according to the wind turbine group data, it is specifically used to:

[0183] The wind power output model is constructed by combining the Weibull distribution function and the wind turbine data, wherein the formula of the wind power output model is as follows:

[0184]

[0185] Where f(v) represents the probability density function of the Weibull distribution of wind speed; v represents the actual wind speed, c represents the scale parameter; k represents the shape parameter; and F(v) represents the cumulative distribution function of the Weibull distribution.

[0186] Optionally, the construction unit 401 is further used for:

[0187] According to the distributed power generation parameters and node parameters in the power system parameters, a distribution network dynamic model is established, which specifically includes the following formulas:

[0188]

[0189] Among them, P DG,i represents the active power of the distributed generation connected to node i; N represents the number of nodes; P loss Indicates network loss;

[0190] The above formula represents the maximum value of the active power of the distributed power source, wherein the constraint conditions of the distribution network dynamic model include the following formula:

[0191]

[0192] Among them, P j represents the active power injected into node j; B and E represent the node set and branch set; I ij represents the current of branch ij, the superscript and subscript represent the upper and lower limits of the current respectively; r ij Represents the resistance of branch ij; P ij represents the active power flowing from node i to node j; P jk represents the active power flowing from node j to node k; g j represents the conductance of node j; v j is the voltage at node j; Q j represents the reactive power injected by node j; x ij Represents the reactance of branch ij; Q ij represents the reactive power flowing from node i to node j; Q jk represents the reactive power flowing from node j to node k; b j is the susceptance of node j.

[0193] Optionally, the characteristic data includes standardized reactive power output data and voltage data, and the analysis unit 403 is specifically used for:

[0194] Use the moving average filtering method to denoise the reactive power output data and voltage data to obtain the denoised time domain signal;

[0195] The denoised time domain signal is converted into a frequency domain signal using fast Fourier transform;

[0196] According to the amplitude and phase of the frequency domain signal, the main frequency component and the amplitude of the main frequency component are extracted;

[0197] The main frequency component and the amplitude of the main frequency component are standardized to obtain standardized reactive power output data and voltage data.

[0198] Optionally, the calculation unit 404 is specifically configured to:

[0199] According to the opinions of experts, determine the weight of reactive power compensation capability among multiple indicators and the weight of system voltage regulation capability among multiple indicators;

[0200] The characteristic data is weighted and summed according to the weight of the reactive power compensation capability and the weight of the system voltage regulation capability to obtain a comprehensive score.

[0201] Optionally, the device 400 further includes:

[0202] A drawing unit, used for drawing a curve showing the comprehensive score changing over time;

[0203] The evaluation unit is used to analyze the trend of the curve to evaluate the dynamic characteristics of the static VAR generator under the new energy environment and the distributed power supply capacity.

[0204] It should be noted that the specific implementation method and effect of the static VAR generator dynamic characteristic evaluation device 400 can be found in the above Figure 1 or Figure 3 The relevant descriptions in the provided methods will not be repeated here.

[0205] The present application embodiment also provides an electronic device 500, such as Figure 5 As shown, the device 500 includes a memory 501 and a processor 502:

[0206] The memory 501 is used to store computer programs;

[0207] The processor 502 is used to execute the above Figure 1 or Figure 3 The method provided.

[0208] In addition, the present application also provides a computer-readable storage medium, the computer-readable storage medium is used to store a computer program, and the computer program is used to execute Figure 1 or Figure 3 The method provided.

[0209] Through the description of the above implementation methods, it can be known that those skilled in the art can clearly understand that all or part of the steps in the above-mentioned embodiment method can be implemented by means of software plus a general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a read-only memory (ROM) / RAM, a magnetic disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network communication device such as a router) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0210] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is only schematic, in which the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the goals of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0211] The above description is merely an exemplary embodiment of the present application and is not intended to limit the protection scope of the present application.

Claims

1. A method for evaluating dynamic characteristics of a static VAR generator, characterized in that: The method comprises: Construct a distributed power generation capacity model based on power system parameters and wind turbine data; Solving the distributed power generation capacity model to obtain reactive power output data of the static VAR generator and voltage data of the power system; Performing spectrum analysis on the reactive output data of the static VAR generator and the voltage data of the power system to obtain characteristic data; The characteristic data is calculated using multiple indicators to obtain a comprehensive score, which is used to evaluate the dynamic characteristics of the static VAR generator.

2. The method according to claim 1, characterized in that: The step of constructing a distributed power source carrying capacity model according to the power system parameters and wind turbine data includes: Constructing a reactive power regulation model according to reactive power parameters of the static VAR generator in the power system parameters; Constructing a substation bus voltage model according to the voltage parameters of the on-load tap-changing transformer in the power system parameters; Constructing a wind power output model according to the wind turbine data; The distributed power source carrying capacity model is obtained according to the reactive power regulation model, the substation bus voltage model, the wind power output model and the distribution network dynamic model.

3. The method according to claim 2, characterized in that The step of constructing a wind power output model according to the wind turbine generator set data includes: The wind power output model is constructed by combining the Weibull distribution function and the wind turbine data, wherein the formula of the wind power output model is as follows: Where f(v) represents the probability density function of the Weibull distribution of wind speed; v represents the actual wind speed; c represents the scale parameter; k represents the shape parameter; and F(v) represents the cumulative distribution function of the Weibull distribution.

4. The method according to claim 2, characterized in that: The construction process of the distribution network dynamic model includes: According to the distributed power generation parameters and node parameters in the power system parameters, a distribution network dynamic model is established, which specifically includes the following formulas: Among them, P DG,i represents the active power of the distributed generation connected to node i; N represents the number of nodes; P loss Indicates network loss; The above formula represents the maximum value of the active power of the distributed power source, wherein the constraint conditions of the distribution network dynamic model include the following formula: Among them, P j represents the active power injected into node j; B and E represent the node set and branch set; I ij represents the current of branch ij, the superscript and subscript represent the upper and lower limits of the current respectively; r ij Represents the resistance of branch ij; P ij represents the active power flowing from node i to node j; P jk represents the active power flowing from node j to node k; g j represents the conductance of node j; v j is the voltage at node j; Q j represents the reactive power injected by node j; x ij Represents the reactance of branch ij; Q ij represents the reactive power flowing from node i to node j; Q jk represents the reactive power flowing from node j to node k; b j is the susceptance of node j.

5. The method according to claim 1, characterized in that: The characteristic data includes standardized reactive power output data and voltage data. The reactive power output data of the static VAR generator and the voltage data of the power system are subjected to spectrum analysis to obtain the characteristic data, including: Using a moving average filtering method to perform denoising on the reactive power output data and the voltage data to obtain a denoised time domain signal; Convert the denoised time domain signal into a frequency domain signal using a fast Fourier transform; Extracting a main frequency component and the amplitude of the main frequency component according to the amplitude and phase of the frequency domain signal; The main frequency component and the amplitude of the main frequency component are standardized to obtain the standardized reactive power output data and voltage data.

6. The method according to claim 1, characterized in that The method of calculating the characteristic data using multiple indicators to obtain a comprehensive score includes: Determine, based on expert opinions, the weight of reactive power compensation capability among the multiple indicators and the weight of system voltage regulation capability among the multiple indicators; The characteristic data is weighted and summed according to the weight of the reactive power compensation capability and the weight of the voltage regulation system capability to obtain the comprehensive score.

7. The method according to claim 1, characterized in that The method further comprises: Draw a curve showing the comprehensive score changing over time; The trend of the curve is analyzed to evaluate the dynamic characteristics of the static VAR generator under the new energy environment and the distributed power supply carrying capacity.

8. A static VAR generator dynamic characteristics evaluation device, characterized in that: The device comprises: A construction unit, used for constructing a distributed power source carrying capacity model according to power system parameters and wind turbine data; A solving unit, used for solving the distributed power supply carrying capacity model to obtain reactive power output data of the static VAR generator and voltage data of the power system; An analysis unit, used for performing spectrum analysis on the reactive output data of the static VAR generator and the voltage data of the power system to obtain characteristic data; A calculation unit is used to calculate the characteristic data using multiple indicators to obtain a comprehensive score, and the comprehensive score is used to evaluate the dynamic characteristics of the static VAR generator.

9. An electronic device, characterized in that: The device includes a memory and a processor, and the processor is used to execute a program stored in the memory to run the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium is used to store a computer program, and the computer program is used to execute the method according to any one of claims 1 to 7.

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