Fault discrimination method for distributed photovoltaic active power distribution network and related equipment

Abstract

The invention relates to the technical field of power systems, and discloses a fault judgment method and related equipment for a distributed photovoltaic active power distribution network, and the method comprises the steps: constructing an electrical response characteristic model of the distributed photovoltaic active power distribution network when a single-phase grounding fault occurs, the electrical response characteristic model at least comprises current characteristics in a fault state; based on the electrical response characteristic model, fault characteristic information used for reflecting fault current waveform distortion characteristics is extracted, and the waveform distortion degree represented by the fault characteristic information is analyzed; comparing the waveform distortion degree with a preset reference to generate a comparison result; and determining the fault direction of the power distribution network based on the comparison result. The fault direction of the distributed photovoltaic active power distribution network can be accurately judged, the fault judgment time is shortened, the fault detection sensitivity and accuracy are improved, rapid fault isolation and operation recovery of the power distribution network are facilitated, and therefore safe and stable operation of the power distribution network is guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a fault discrimination method for a distributed photovoltaic active distribution network and related equipment. BACKGROUND

[0002] With the deep transformation of global energy structure and the rapid development of renewable energy technology, the penetration rate of distributed photovoltaic power generation in distribution networks is showing a sustained upward trend. Photovoltaic power generation system has become an indispensable part of modern power systems due to its significant advantages such as clean and environmentally friendly, renewable use, and distributed layout. However, the large-scale access of distributed photovoltaic power generation brings unprecedented challenges to the operation mechanism and protection system of traditional distribution networks. Traditional distribution networks mainly adopt single-source radial network structure, and the power flow distribution has obvious unidirectional characteristics. The corresponding protection configuration scheme is relatively simple and direct. However, with the widespread access of distributed photovoltaic power generation units, the topology structure and operation characteristics of distribution networks have undergone fundamental changes. Distribution networks have changed from traditional passive networks to active networks with multiple power sources, and the power flow distribution presents a significant bidirectional flow characteristic. This structural change has put the traditional protection principle based on unidirectional power flow under severe test, and higher technical requirements have been put forward for the sensitivity, selectivity and speed of the protection system.

[0003] In the common single-phase ground fault scenario of distribution networks, the problem caused by distributed photovoltaic power is particularly prominent. Unlike traditional synchronous generators, the fault current provided by photovoltaic power has a small amplitude and is mixed with a large number of harmonic components, which makes the sensitivity and selectivity of traditional protection devices decrease sharply, and the risk of misoperation or refusal to act increases significantly when a fault occurs, which seriously threatens the safe and stable operation of distribution networks. In addition, photovoltaic inverters, as the core equipment of distributed photovoltaic systems, have complex control strategies and unique dynamic response characteristics, which make the fault current waveform significantly different from that of traditional synchronous generators. These differences not only exist in the amplitude and phase of the current, but also exist in the distortion and transient characteristics of the waveform, further increasing the difficulty of fault detection and positioning. At present, the fault discrimination methods applied in distribution networks mostly rely on power frequency quantities or steady-state components for analysis, which is not sufficient when facing the rapidly changing fault transient process of distribution networks after the access of photovoltaic power. In the context of rapid development of new energy, the traditional fault discrimination method has become a bottleneck restricting the intelligent and efficient operation of distribution networks. Therefore, it is urgent to develop a new fault discrimination method that can adapt to the characteristics of active distribution networks containing distributed photovoltaic power, in order to improve the speed and accuracy of protection and ensure the safe and stable operation of distribution networks.

[0004] The preceding description is to provide general background information and does not necessarily constitute the prior art. SUMMARY

[0005] The embodiment of the present application provides a fault discrimination method for a distributed photovoltaic active power distribution network and related equipment, which can realize accurate discrimination of the fault direction of the distributed photovoltaic active power distribution network, shorten the fault discrimination time, improve the sensitivity and accuracy of fault detection, and is beneficial to fast isolation of faults and recovery of operation of the power distribution network, thereby guaranteeing the safe and stable operation of the power distribution network.

[0006] In a first aspect, the embodiment of the present application provides a fault discrimination method for a distributed photovoltaic active power distribution network, comprising: constructing an electrical response characteristic model of the power distribution network containing distributed photovoltaic when a single-phase ground fault occurs, wherein the electrical response characteristic model at least includes current characteristics in a fault state; extracting fault feature information reflecting waveform distortion characteristics of the fault current based on the electrical response characteristic model, and analyzing the degree of waveform distortion represented by the fault feature information; comparing the degree of waveform distortion with a preset reference to generate a comparison result; determining the fault direction of the power distribution network based on the comparison result.

[0007] Further, in some embodiments of the present application, the construction of the electrical response characteristic model of the power distribution network containing distributed photovoltaic when a single-phase ground fault occurs comprises: analyzing the influence mechanism of the access of distributed photovoltaic on the fault current characteristics of the power distribution network; establishing a basic electrical parameter model of the power distribution network in a normal operating state; generating the electrical response characteristic model based on the basic electrical parameter model and the single-phase ground fault condition.

[0008] Further, in some embodiments of the present application, the extraction of the fault feature information reflecting the waveform distortion characteristics of the fault current based on the electrical response characteristic model comprises: resolving the transient current component characteristics in the fault state from the electrical response characteristic model; performing mathematical transformation processing on the transient current component characteristics to generate intermediate characteristic quantities reflecting the waveform distortion characteristics; constructing and outputting the fault feature information based on the intermediate characteristic quantities.

[0009] Further, in some embodiments of the present application, the mathematical transformation processing on the transient current component characteristics to generate intermediate characteristic quantities reflecting the waveform distortion characteristics comprises: coupling the transient current component characteristics after mathematical transformation processing with a modulation function for operation; The coupling operation generates the intermediate characteristic quantity containing the characteristic harmonic component.

[0010] Further, in some embodiments of the present application, the analysis of the waveform distortion degree represented by the fault characteristic information comprises: According to the fault characteristic information, a fault current time domain signal to be analyzed is acquired; The fault current time domain signal is discretely sampled to form a current data sequence; The current data sequence is subjected to waveform distortion quantization analysis, and a quantization index value representing the distortion degree is output.

[0011] Further, in some embodiments of the present application, the waveform distortion quantization analysis of the current data sequence and the output of the quantization index value representing the distortion degree comprise: The harmonic spectrum distribution of the current data sequence is calculated; The waveform distortion quantization index is calculated based on the harmonic spectrum distribution.

[0012] Further, in some embodiments of the present application, the determination of the fault direction of the power distribution network based on the comparison result comprises: When the comparison result is that the waveform distortion degree is greater than the preset reference, it is determined as a reverse direction fault, and the reverse direction fault represents that the fault point is located on the line side of the distributed photovoltaic power source relative to the protection installation; When the comparison result is that the waveform distortion degree is less than or equal to the preset reference, it is determined as a positive direction fault, and the positive direction fault represents that the fault point is located on the bus side of the distributed photovoltaic power source relative to the protection installation.

[0013] In a second aspect, the embodiments of the present application provide a fault discrimination device for a distributed photovoltaic active power distribution network, comprising: A construction module is configured to construct an electrical response characteristic model of a power distribution network containing a distributed photovoltaic when a single-phase ground fault occurs, and the electrical response characteristic model at least includes current characteristics in a fault state; An analysis module is configured to extract fault characteristic information reflecting waveform distortion characteristics of a fault current based on the electrical response characteristic model, and analyze a waveform distortion degree represented by the fault characteristic information; A comparison module is configured to compare the waveform distortion degree with a preset reference to generate a comparison result; A determination module is configured to determine a fault direction of the power distribution network based on the comparison result.

[0014] In a third aspect, an electronic device is provided, which comprises a memory, a processor, and a computer program stored in the memory and capable of being run on the processor, wherein the processor implements the steps of the fault identification method for a distributed photovoltaic active power distribution network according to the first aspect when running the computer program.

[0015] In a fourth aspect, a storage medium is provided, which stores a computer program capable of being loaded by a processor and implementing the fault identification method for a distributed photovoltaic active power distribution network according to the first aspect.

[0016] The present application provides a fault identification method for a distributed photovoltaic active power distribution network and related equipment. First, by constructing an electrical response characteristic model of the power distribution network containing distributed photovoltaic when a single-phase ground fault occurs, the current characteristics under the fault state can be accurately simulated, providing a basis for subsequent fault analysis. Then, based on the model, fault feature information reflecting the distortion characteristics of the fault current waveform is extracted, and the degree of waveform distortion represented thereby is analyzed, so that the acquisition of fault features is more accurate and targeted. Finally, by comparing the degree of waveform distortion with a preset reference, and determining the fault direction of the power distribution network according to the comparison result, the fault direction of the power distribution network is quickly and accurately determined. Thus, the present embodiment can effectively solve the problem of complex changes in fault features caused by the access of distributed photovoltaic, shorten the fault identification time, improve the sensitivity and accuracy of fault identification, and is conducive to quickly isolating faults and restoring operation of the power distribution network, thereby ensuring the safe and stable operation of the power distribution network. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Figure 1 is an application environment diagram of the fault identification method for a distributed photovoltaic active power distribution network provided by the present embodiment; Figure 2 is a flowchart of the fault identification method for a distributed photovoltaic active power distribution network provided by the present embodiment; Figure 3 is another flowchart of the fault identification method for a distributed photovoltaic active power distribution network provided by the present embodiment; Figure 4 is a topological diagram of a power distribution network containing photovoltaic provided by the present embodiment; Figure 5The U-phase ground fault three-phase current harmonic distortion rate diagram is provided by the embodiment of the present application; Figure 6 The structural schematic diagram of the fault discrimination device for the distributed photovoltaic active distribution network is provided by the embodiment of the present application. Figure 7 The structural schematic diagram of the electronic device is provided by the embodiment of the present application. DETAILED DESCRIPTION

[0019] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same or similar elements unless otherwise represented. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present application. Rather, they are merely examples of the systems and methods consistent with some aspects of the present application, as detailed in the appended claims.

[0020] It should be noted that, in this document, the terms "comprise", "comprising", or any other variant thereof are intended to encompass non-exclusive inclusion, such that processes, methods, articles, or devices that "comprise", "comprising", or "include" a list of elements do not only include those elements, but also include other elements not explicitly listed, or further include elements inherent in such processes, methods, articles, or devices. Without more limitations, the element defined by the statement "comprises one" does not exclude the presence of additional identical elements in the process, method, article, or device that includes the element, and in addition, components, features, elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and the specific meaning thereof should be determined in the interpretation of the specific embodiment or further combined with the context of the specific embodiment.

[0021] It should be understood that the specific embodiments described herein are merely used to explain the present application, and are not used to limit the present application.

[0022] In the subsequent description, the suffix such as "module", "component", or "unit" used to represent elements is only for the convenience of the description of the present application, and has no specific meaning. Therefore, "module", "component", or "unit" can be used mixedly.

[0023] In order to solve the above technical problems and break through the defects of the prior art, the embodiment of the present application provides a fault discrimination method for a distributed photovoltaic active distribution network and related equipment, which can accurately determine the fault direction of the distributed photovoltaic active distribution network, shorten the fault discrimination time, improve the sensitivity and accuracy of fault detection, and is beneficial to quickly isolate faults and restore operation of the distribution network, thereby ensuring the safe and stable operation of the distribution network.

[0024] Figure 1 Figure 1 is a diagram of an application environment of a fault identification method for a distributed photovoltaic active power distribution network according to an embodiment of the present application. Referring to Figure 1 The fault identification method for a distributed photovoltaic active power distribution network is applied to a fault identification system for a distributed photovoltaic active power distribution network. The fault identification system for a distributed photovoltaic active power distribution network includes a terminal 110 and a server 120. The terminal 110 and the server 120 are connected through a network. The terminal 110 can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, a tablet computer, a notebook computer, etc. The server 120 can be implemented by an independent server or a server cluster composed of multiple servers. The server 120 is configured to execute the fault identification method for a distributed photovoltaic active power distribution network, which includes: constructing an electrical response characteristic model of a power distribution network containing distributed photovoltaic when a single-phase ground fault occurs, the electrical response characteristic model including at least current characteristics in a fault state; extracting fault feature information reflecting fault current waveform distortion characteristics based on the electrical response characteristic model, and analyzing the degree of waveform distortion represented by the fault feature information; comparing the degree of waveform distortion with a preset benchmark to generate a comparison result; and determining the fault direction of the power distribution network based on the comparison result.

[0025] Please refer to Figure 2 , Figure 2 Figure 1 is a diagram of an application environment of a fault identification method for a distributed photovoltaic active power distribution network according to an embodiment of the present application. Referring to S1. Constructing an electrical response characteristic model of a power distribution network containing distributed photovoltaic when a single-phase ground fault occurs, the electrical response characteristic model including at least current characteristics in a fault state; Specifically, for step S1, in a distribution network with distributed photovoltaic access, a single-phase ground fault can trigger a complex electrical response. The model needs to accurately simulate the current characteristics under fault conditions, including the amplitude, phase, frequency, and harmonic content of the current. For example, the fault current amplitude provided by the photovoltaic power supply is small, and it is mixed with a large number of harmonic components, which is significantly different from the fault current characteristics of traditional synchronous generators. When building the model, the dynamic response characteristics of the photovoltaic inverter need to be considered, such as its control strategy which affects the transient characteristics of the fault current. In addition, the model should also cover the topology and operating state of the distribution network, such as line parameters, load characteristics, etc., to accurately simulate the current changes under different fault locations and operating conditions. By establishing such a model, it can provide basic data support for subsequent fault analysis. Further, the model can be dynamically updated based on real-time data to adapt to the intermittency and volatility of distributed photovoltaic output power, ensuring the accuracy and timeliness of the model.

[0026] S2. Based on the electrical response characteristic model, extract fault feature information reflecting the waveform distortion characteristics of the fault current, and analyze the waveform distortion degree represented by the fault feature information; Specifically, for step S2, fault feature information extraction is a key step for fault discrimination. First, the transient current component characteristics under fault conditions are analyzed from the electrical response characteristic model, including the amplitude and decay characteristics of the transient current. Then, mathematical transformation processing is performed on the transient current component characteristics, such as using wavelet transform or Fourier transform methods, to generate intermediate feature quantities reflecting waveform distortion characteristics. These intermediate feature quantities can highlight the components of waveform distortion, providing a basis for further analysis. Based on the intermediate feature quantities, fault feature information is constructed and output, such as harmonic amplitude ratio, waveform distortion degree, etc. Finally, the waveform distortion degree is quantified through harmonic analysis and waveform reconstruction methods. For example, the ratio of the amplitude of each harmonic to the fundamental wave amplitude is calculated to obtain a quantitative index of waveform distortion. This step can accurately capture the waveform distortion characteristics of the fault current, providing a key basis for subsequent fault direction determination. In order to improve the accuracy of feature extraction, a multi-resolution analysis method can be used, combining the advantages of wavelet transform and Fourier transform to capture high-frequency harmonic components and low-frequency transient components in the fault current.

[0027] S3. Compare the waveform distortion degree with the preset reference, and generate a comparison result; Specifically, for step S3, the preset reference is a threshold value preset for judging whether the waveform distortion degree exceeds the normal range. The setting of the threshold value needs to be based on a large amount of experimental and simulation data, and the waveform distortion characteristics under normal operation and fault conditions of the distribution network are comprehensively considered. For example, the threshold value of the waveform distortion degree is set to 10%, and when the actual waveform distortion degree exceeds 10%, it is determined that the fault is in the reverse direction, and vice versa. By comparing the waveform distortion degree obtained by analysis with the preset reference, a comparison result can be generated, which provides a direct basis for the final fault direction determination. The preset reference can be dynamically adjusted according to different operating conditions and fault types to improve the adaptability and accuracy of fault discrimination.

[0028] S4. Determine the fault direction of the distribution network based on the comparison result; Specifically, for step S4, different fault directions will result in different fault current waveform distortion degrees. When the fault direction is the reverse direction, that is, the fault point is located on the line side of the protection installation relative to the distributed photovoltaic power supply, due to the output characteristics of the distributed photovoltaic power supply, the fault current waveform distortion degree will be larger and exceed the preset reference. When the fault direction is the positive direction, that is, the fault point is located on the bus side of the protection installation relative to the distributed photovoltaic power supply, the fault current waveform distortion degree is relatively small and does not exceed the preset reference. Therefore, the fault direction can be quickly and accurately determined by the comparison result, so as to realize the rapid positioning and processing of the fault. In order to further improve the reliability of fault direction determination, the relationship between the waveform distortion degree and the fault location in the fault feature information can be used in combination with the fault distance estimation method to realize more accurate fault positioning.

[0029] In specific embodiments, the fault discrimination method for a distributed photovoltaic active distribution network provided by the present embodiment is as shown in Figure 3 As shown in the figure, first, the influence of photovoltaic access on the distribution network is analyzed, and the single-phase current expression of the distribution network under normal conditions is used to derive the single-phase current expression of the distribution network under single-phase ground fault. Then, the instantaneous power of the distribution network is calculated using the single-phase current expression of the distribution network under fault, and the non-periodic component in the instantaneous power expression of the distribution network fault is processed to obtain an alternating current expression containing high-order harmonic components. Finally, the alternating current expression containing high-order harmonic components is used to construct a photovoltaic fault sending current expression, the photovoltaic fault sending current is sampled, and the total harmonic distortion rate of the current is calculated using the sampling result. When the total harmonic distortion rate of the current is greater than a given threshold value, the fault is in the reverse direction, and vice versa, the fault is in the positive direction.

[0030] The embodiment can accurately simulate the current characteristics of the distributed photovoltaic active distribution network in single-phase ground fault, including the amplitude, phase, frequency and harmonic content of the current, by constructing an electrical response characteristic model. By extracting the transient current component characteristics and performing mathematical transformation processing, intermediate characteristic quantities are generated, fault characteristic information is constructed, and the waveform distortion degree is quantified. After comparison with the preset reference, the fault direction can be quickly determined, the change of fault characteristics caused by photovoltaic access can be effectively dealt with, the sensitivity and accuracy of fault discrimination are improved, the adaptability and reliability of the distribution network protection system are enhanced, and the safe and stable operation of the power grid is ensured.

[0031] Further, in some embodiments, the step S1 "constructing an electrical response characteristic model of the distribution network containing distributed photovoltaic in single-phase ground fault" can specifically include: S11. Analyze the influence mechanism of distributed photovoltaic access on the fault current characteristics of the distribution network; Specifically, after the distributed photovoltaic is accessed into the distribution network, due to the intermittency and volatility of its output power, as well as the dynamic response characteristics of the photovoltaic inverter, it will significantly affect the fault current characteristics of the distribution network. The fault current amplitude provided by the photovoltaic power supply is usually small and contains a large amount of harmonic components, which is significantly different from the fault current characteristics of traditional synchronous generators. In addition, the control strategy of the photovoltaic inverter (such as maximum power point tracking, reactive power regulation, etc.) will also affect the transient and steady-state characteristics of the fault current. Analyzing these influence mechanisms is the basis for constructing an accurate electrical response characteristic model. Further, the influence of different types of photovoltaic inverters (such as voltage source inverters and current source inverters) on the fault current, as well as the influence of different fault types (such as single-phase ground fault, phase-to-phase short circuit fault, etc.) on the fault current characteristics can be studied to improve the generality and accuracy of the model.

[0032] S12. Establish a basic electrical parameter model of the distribution network in normal operation state; Specifically, for step S12, in the normal operation state, the electrical parameters of the distribution network include voltage, current, power, etc., which are affected by factors such as the topology of the distribution network, line parameters (resistance, inductance, capacitance), load characteristics, etc. Establishing a basic electrical parameter model requires detailed analysis of the structure and operating conditions of the distribution network. For example, the topology of the distribution network determines the distribution path of the current, the line parameters determine the voltage drop and power loss, and the load characteristics determine the change of power demand. By establishing a basic electrical parameter model, reference data can be provided for subsequent fault analysis. Further, a method combining real-time monitoring data and historical data can be used to improve the dynamic adaptability of the model. For example, real-time acquisition of the operating parameters of the distribution network through an online monitoring system, combined with historical data for model correction.

[0033] S13. Derive the electrical response characteristic model based on the basic electrical parameter model and the single-phase ground fault condition. Specifically, for step S13, under the single-phase ground fault condition, the electrical response characteristics of the distribution network will change significantly. The amplitude, phase, frequency and harmonic content of the fault current will change. By combining the basic electrical parameter model and the fault condition, the electrical response characteristic model under the fault condition can be derived. This model needs to accurately reflect the characteristics of the fault current, including the changes of the transient current component and the steady-state current component. For example, the transient process of the fault current can be described by a differential equation, and the expression of the fault current can be obtained by solving the differential equation, and then the waveform distortion characteristics of the fault current can be analyzed. Further, the multi-physical field coupling analysis method can be used to consider the influence of electrical, thermal, mechanical and other multi-physical fields, so as to improve the comprehensiveness and accuracy of the model. In addition, cloud computing and big data technology can be used to analyze and process a large amount of fault data, optimize model parameters, and improve the prediction ability of the model.

[0034] The embodiment analyzes the influence mechanism of distributed photovoltaic access on the fault current characteristics of the distribution network, establishes a basic electrical parameter model, and derives an electrical response characteristic model based on the basic electrical parameter model, which helps to deeply understand the change of the fault current characteristics of the distribution network after the photovoltaic access, and provides accurate model support for subsequent fault feature extraction and direction determination.

[0035] In a specific embodiment, for step S1, the influence of photovoltaic access on the distribution network is analyzed, and the expression of single-phase current of the distribution network under single-phase ground fault is derived by using the expression of single-phase voltage and single-phase current of the distribution network under normal conditions. Photovoltaic power generation can reduce the dependence of the distribution network on traditional energy, reduce carbon emissions, and also provide voltage support and improve power quality. However, its intermittency and volatility can cause voltage problems, and the inverter may also introduce harmonics. Therefore, photovoltaic access is the key to low-carbon transformation of the distribution network, and needs technical upgrading and fine management to ensure safe and efficient grid connection.

[0036] The expression of single-phase current of the distribution network under single-phase ground fault is derived by using the expression of single-phase voltage and single-phase current of the distribution network under normal conditions. Among them, the expression of single-phase voltage and single-phase current of the distribution network under normal conditions is respectively: In the formula, is the single-phase voltage of the distribution network, is the voltage amplitude, is the angular frequency, is the initial phase angle of the voltage, is the single-phase current of the distribution network, is the current amplitude, is the initial impedance angle.

[0037] When the power grid fails, for example, in a single-phase ground fault, the power grid current satisfies: wherein, is the resistance after short circuit, is the instantaneous value of the short circuit current, is the inductance after short circuit.

[0038] Solving the above differential equation, the expression of the single-phase current of the distribution network in a single-phase ground fault is: wherein, is the amplitude of the periodic component of the short circuit current, is the impedance angle of the short circuit circuit, is the integral constant, is the time constant.

[0039] Further, in some embodiments, the "extracting fault feature information reflecting the distortion characteristics of the fault current waveform based on the electrical response characteristic model" in step S2 can specifically include: S21. Analyzing the transient current component characteristics in the fault state from the electrical response characteristic model; Specifically, for step S21, in an active distribution network with distributed photovoltaic, the transient component characteristics of the fault current are the key information for fault discrimination. The transient current component includes the amplitude, decay characteristics, etc. of the transient current. By analyzing the electrical response characteristic model, these transient current component characteristics can be extracted, providing a basis for subsequent fault analysis. Further, high-precision numerical methods such as the Runge-Kutta method can be used to solve the differential equations in the electrical response characteristic model to more accurately analyze the transient current component characteristics.

[0040] S22. Mathematically transforming the transient current component characteristics to generate intermediate characteristic quantities reflecting the waveform distortion characteristics; Specifically, for step S22, the mathematical transformation of the transient current component characteristics is a key step in extracting fault features. Mathematical methods such as wavelet transform or Fourier transform can be used to convert the transient current component from the time domain to the frequency domain or other characteristic spaces. For example, wavelet transform can capture high-frequency harmonic components in the fault current, while Fourier transform can analyze steady-state harmonic components. These transformed data generate intermediate characteristic quantities that can highlight the characteristics of waveform distortion. Further, a combination of multiple mathematical transformation methods can be used, such as combining wavelet transform with Hilbert transform, to more comprehensively capture the characteristics of the transient current component.

[0041] S23. Constructing and outputting fault feature information based on the intermediate characteristic quantities; Specifically, for step S23, the intermediate feature quantity contains rich fault information, and further fault feature information needs to be constructed. Feature extraction algorithms such as principal component analysis (PCA) or independent component analysis (ICA) can be used to extract parameters that best represent fault features from the intermediate feature quantity. For example, the amplitude and phase of each harmonic can be calculated, or statistical features such as kurtosis and skewness of the waveform can be extracted. The constructed fault feature information is used for subsequent fault discrimination. Further, deep learning techniques such as convolutional neural networks (CNN) can be used to learn the intermediate feature quantity and automatically extract fault feature information, improving the intelligent level of fault discrimination.

[0042] The embodiment generates intermediate feature quantities by analyzing transient current component features and performing mathematical transformation processing, and constructs fault feature information, which not only improves the accuracy and reliability of fault feature extraction, but also enhances the intelligent level and adaptability of fault discrimination by combining multiple mathematical transformation methods and deep learning techniques, effectively improving the efficiency and accuracy of distribution network fault discrimination.

[0043] Further, in some embodiments, step S22 "performing mathematical transformation processing on the transient current component features to generate intermediate feature quantities reflecting waveform distortion characteristics" can specifically include: S221. Coupling operation of the transient current component features after mathematical transformation processing and modulation function; S222. Generating intermediate feature quantities containing characteristic harmonic components through coupling operation; Specifically, after extracting the transient current component features, mathematical transformation processing is needed to generate intermediate feature quantities reflecting waveform distortion characteristics. Common mathematical transformation methods include wavelet transform and Fourier transform. Wavelet transform is suitable for capturing high-frequency harmonic components in fault current, while Fourier transform is suitable for analyzing steady-state harmonic components. Through these transformations, the transient current component can be converted from time domain to frequency domain or other feature space, thus more clearly showing the waveform distortion characteristics. The data after mathematical transformation can generate intermediate feature quantities, which can highlight the characteristics of waveform distortion. For example, wavelet transform can obtain high-frequency harmonic component amplitudes at different scales, and Fourier transform can obtain the amplitudes and phases of each harmonic. These intermediate feature quantities provide a basis for subsequent construction of fault feature information.

[0044] To further enhance the representation ability of the intermediate feature quantity, the transient current component feature processed by mathematical transformation can be coupled with a modulation function. The modulation function can be a pre-designed function, such as a sine modulation function or a square wave modulation function. Through coupling operation, the waveform distortion characteristics in a specific frequency range can be highlighted, thereby improving the distinguishability of fault feature information. The result of coupling operation generates an intermediate feature quantity containing characteristic harmonic components. These intermediate feature quantities not only contain the characteristics of the original transient current component, but also highlight specific harmonic components through the processing of the modulation function. For example, high-frequency harmonic components can be highlighted by designing the modulation function, thereby more effectively capturing waveform distortion caused by faults.

[0045] The embodiment generates intermediate feature quantities containing characteristic harmonic components by performing mathematical transformation processing on the transient current component feature and coupling operation with the modulation function, not only improves the accuracy and reliability of fault feature extraction, but also enhances the distinguishability and adaptability of fault feature information by combining multiple mathematical transformation methods and adaptive modulation functions.

[0046] Further, in some embodiments, the "analyzing the degree of waveform distortion represented by the fault feature information" in step S2 includes: S24. According to the fault feature information, obtaining the fault current time domain signal to be analyzed; Specifically, for step S24, the fault feature information contains key data reflecting the waveform distortion of the fault current. According to these information, the current time domain signal at the fault occurrence time can be accurately extracted from the recorded current data, providing original data support for subsequent analysis. Among them, for the method of obtaining time domain signal: one method is to collect the current signal in the distribution network in real time through a high-precision current transformer (CT), and then convert the collected analog signal into a digital signal through an analog-to-digital converter (ADC). After these digital signals are preprocessed (such as filtering and denoising), they can be used as the fault current time domain signal to be analyzed. The preprocessing process can use Kalman filtering algorithm, effectively removing high-frequency noise and interference components in the signal, and improving the signal quality.

[0047] S25. Discrete sampling processing is performed on the fault current time domain signal to form a current data sequence; Specifically, for step S25, the discrete sampling process is to convert the continuous time-domain signal into a sequence of discrete data points. The key of this step is to select a suitable sampling frequency and sampling strategy to ensure that the waveform characteristics of the fault current can be accurately restored while meeting the real-time and efficiency requirements of data processing. In order to ensure that the sampled data can accurately reflect the characteristics of the fault current, it is necessary to follow the Nyquist sampling theorem, and the sampling frequency should be at least twice the highest frequency component of the signal. In practical applications, considering that the fault current may contain rich high-frequency harmonic components, the sampling frequency is usually selected to be higher, such as several kilohertz or even higher. During the sampling process, uniform sampling or non-uniform sampling strategies can be used. For periodic fault current signals, uniform sampling can better utilize frequency domain analysis tools such as Fourier transform; while for non-periodic or burst fault signals, non-uniform sampling can more flexibly capture the key characteristics of the signal. In order to improve sampling efficiency and reduce data volume, data compression techniques can be used to compress and store the sampled data without losing signal characteristics S26. Waveform distortion quantification analysis is performed on the current data sequence, and a quantitative index value representing the distortion degree is outputted; Specifically, for step S26, the purpose of waveform distortion quantification analysis is to convert the waveform distortion degree of the fault current into specific numerical indicators. This can be achieved by calculating harmonic content, waveform distortion, and other parameters. Among them, a commonly used method for waveform distortion quantification analysis is to calculate the total harmonic distortion (THD). THD is defined as the ratio of the effective value of each harmonic to the effective value of the fundamental wave. By Fourier transform, the current data sequence is converted from time domain to frequency domain, and the amplitude of each harmonic can be obtained, and then THD is calculated. In addition, the method of waveform reconstruction can be used to compare the sampled current data with the ideal sinusoidal wave, and the difference between the two is calculated as the quantitative indicator of waveform distortion. Waveform reconstruction can be realized by least square method and other algorithms, to find the ideal sinusoidal wave closest to the actual waveform, and calculate the root mean square error (RMS Error) between the two as the measure of distortion degree.

[0048] The embodiment obtains the time-domain signal of the fault current and performs discrete sampling processing to form a current data sequence, and then performs waveform distortion quantification analysis on the current data sequence to output a quantitative index value representing the distortion degree. This not only realizes accurate quantification of the waveform distortion of the fault current and improves the reliability of fault discrimination, but also ensures accurate extraction of fault characteristics by using high-precision signal acquisition and flexible sampling strategies.

[0049] Further, in some embodiments, step S26 "waveform distortion quantification analysis is performed on the current data sequence, and a quantitative index value representing the distortion degree is outputted", specifically can include: S261. Calculate the harmonic spectral distribution of the current data sequence. S262. Calculate the waveform distortion quantification index based on the harmonic spectral distribution.

[0050] Specifically, the harmonic spectral distribution analysis is an important step in the waveform distortion quantification analysis. By calculating the harmonic spectral distribution of the current data sequence, the amplitude and phase information of each harmonic can be obtained. The key of this step is to use an efficient frequency domain analysis method, such as Fast Fourier Transform (FFT), to convert the time domain signal to the frequency domain signal. The FFT algorithm can quickly calculate the frequency spectrum of the signal, thereby improving the analysis efficiency. In order to improve the accuracy of harmonic analysis, the current data sequence can be processed with a window function to reduce spectral leakage. For example, using a Hanning window or a Hamming window for data preprocessing can effectively reduce the influence of side lobes and improve the resolution of the main lobe. In addition, interpolation FFT algorithm can be used to further improve the measurement accuracy of harmonic frequency and amplitude. Interpolation FFT can more accurately determine the frequency and amplitude of the harmonics by interpolating between the FFT results.

[0051] The waveform distortion quantification index is used to quantitatively describe the degree of waveform distortion of the fault current. Common waveform distortion quantification indexes include total harmonic distortion (THD), harmonic amplitude ratio, waveform distortion degree, etc. By analyzing the harmonic spectral distribution, these quantification indexes can be calculated to provide specific basis for fault discrimination. The total harmonic distortion (THD) is a commonly used index to measure the degree of waveform distortion. For example, the amplitude ratio and phase difference of each harmonic can be calculated to further analyze the characteristics of waveform distortion. In addition, statistical analysis methods can be introduced to statistically analyze the waveform distortion quantification indexes of multiple measurements to evaluate the severity and occurrence probability of the fault.

[0052] This embodiment realizes accurate quantification of the waveform distortion of the fault current by calculating the harmonic spectral distribution of the current data sequence and calculating the waveform distortion quantification index based on it, improves the accuracy and efficiency of harmonic analysis, and enhances the comprehensiveness and intelligent level of fault feature extraction.

[0053] In specific embodiments, for step S2, the instantaneous power of the distribution network is calculated using the single-phase current expression of the distribution network during fault, and the non-periodic component in the instantaneous power expression of the distribution network fault is processed to obtain an alternating current expression containing high-order harmonic components. The instantaneous power of the distribution network during fault can be calculated as follows: In the formula, is the periodic component of the instantaneous power of the distribution network, is the non-periodic component of the instantaneous power of the distribution network.

[0054] The non-periodic component in the power expression of the distribution network fault is processed, that is, the voltage differential of the non-periodic component is taken, and the following equation is obtained: The AC current expression containing high harmonic components is obtained by multiplying the voltage differential expression of the non-periodic component and the switching function, that is, In the formula, is the AC current instantaneous value converted from the non-periodic component, is an equivalent factor, is a switching function modulation ratio, is a converted equivalent impedance angle, is a constant quantity equivalent value.

[0055] In the specific embodiment, for step S2, the AC current expression containing high harmonic components is used to construct the photovoltaic fault sending current expression, the photovoltaic fault sending current is sampled, and the total harmonic distortion rate of the current is calculated by using the sampling result.

[0056] The AC current expression containing high harmonic components is used to construct the photovoltaic fault sending current expression, which can be expressed as: In the formula, is the amplitude of the periodic component of the photovoltaic fault current, is the impedance angle of the periodic component, is the amplitude of the non-periodic component of the photovoltaic fault current, is the impedance angle of the non-periodic component.

[0057] The photovoltaic fault sending current is sampled, which can be expressed as: In the formula, is the first derivative of the current at time m, is the sampling time, is the sampling time interval, is the sampling value obtained at time m.

[0058] The total harmonic distortion rate of the current is calculated by using the sampling result, that is, In the formula, is the total harmonic distortion rate of the current, is the amplitude of the first harmonic of the current, is the amplitude of the hth harmonic of the current.

[0059] Therefore, for step S3, the relationship between the current total harmonic distortion and the given domain value is determined, and if the current total harmonic distortion is greater than the given threshold, the fault is in the opposite direction, otherwise, the fault is in the positive direction.

[0060] Further, in some embodiments, step S4 "determining the fault direction of the power distribution network based on the comparison result" can specifically include: S41. When the comparison result is that the waveform distortion degree is greater than the preset reference, it is determined as a reverse direction fault, and the reverse direction fault is characterized by the fault point being located in the line side direction of the protection installation relative to the distributed photovoltaic power supply; S42. When the comparison result is that the waveform distortion degree is less than or equal to the preset reference, it is determined as a positive direction fault, and the positive direction fault is characterized by the fault point being located in the bus side direction of the protection installation relative to the distributed photovoltaic power supply.

[0061] Specifically, the reverse direction fault refers to the fault point being located in the line side direction of the protection installation relative to the distributed photovoltaic power supply. In this case, the fault current is mainly provided by the distributed photovoltaic power supply, and due to the output characteristics of the photovoltaic power supply, the waveform distortion degree of the fault current will be relatively large, usually exceeding the preset reference. By comparing the waveform distortion degree with the preset reference, when the waveform distortion degree is greater than the preset reference, it can be determined as a reverse direction fault. This determination logic is based on the current output characteristics of the distributed photovoltaic power supply in the reverse direction fault, and can accurately identify the fault direction.

[0062] The positive direction fault refers to the fault point being located in the bus side direction of the protection installation relative to the distributed photovoltaic power supply. In this case, the fault current is mainly provided by the main power grid, and the waveform distortion degree is relatively small, usually not exceeding the preset reference. By comparing the waveform distortion degree with the preset reference, when the waveform distortion degree is less than or equal to the preset reference, it can be determined as a positive direction fault. This determination logic is based on the current output characteristics of the main power grid in the positive direction fault, and can accurately identify the fault direction.

[0063] In determining the fault direction, the relationship between the waveform distortion degree and the fault location in the fault feature information can be used in combination with the fault distance estimation method to achieve more accurate fault location. For example, by establishing a mapping relationship between the fault distance and the waveform distortion degree, the distance from the fault point to the protection installation can be estimated, thereby achieving rapid fault location and isolation. In addition, machine learning algorithms can also be introduced to improve the accuracy and adaptability of fault direction determination by training a large amount of fault data. For example, using support vector machines (SVM) or neural networks (NN) to classify fault data can further improve the reliability of fault direction determination.

[0064] The embodiment can quickly and accurately determine the fault direction of the power distribution network by comparing the waveform distortion degree with the preset reference, effectively solve the problem of complex change of fault characteristics caused by distributed photovoltaic access, and improve the sensitivity and accuracy of fault discrimination.

[0065] In order to more conveniently understand the fault discrimination method for the distributed photovoltaic active power distribution network provided by the embodiment, the following is described in combination with specific embodiments, so that Figure 4 The power distribution network topology diagram containing distributed photovoltaic in a certain place is taken as an example, Figure 4 In the embodiment, the voltage rated voltage is 10 kV, the line L AB The length of the line L BC is 6 km, the unit length impedance is 0.223+j0.348 Ω, the distributed photovoltaic capacity is 12 MW, the transformer change is 10 kV / 0.4 kV, the load capacity is 10 MVA, k1 and k2 are short-circuit points that can occur, and the current harmonic distortion rate threshold is set to 10%. When a single-phase ground fault occurs at the k1 point, the fault time is 1.012 s, and the three-phase current harmonic distortion rate diagram is obtained by taking the U-phase ground as an example, as shown in Figure 5 It can be known from Figure 5 that at the moment when the fault occurs, the current harmonic distortion rate of the fault phase suddenly increases and far exceeds the set threshold, and the current harmonic distortion rate of the non-fault phase is also above the threshold, so it can be determined that the fault is located in the opposite direction, that is, there is a reverse current from the line side to the bus side.

[0066] In summary, the fault discrimination method for the distributed photovoltaic active power distribution network provided by the embodiment effectively solves the problem of fault feature extraction caused by photovoltaic access by establishing a fault current model of the power distribution network containing distributed photovoltaic and introducing the instantaneous power analysis technology; the special waveform characteristics of the photovoltaic fault current are accurately captured by using the non-periodic component processing technology and high-order harmonic component analysis, and the sensitivity of fault detection is significantly improved; a reliable fault direction identification mechanism is established, and the problem of insufficient adaptability of the traditional directional protection in the photovoltaic access scene is overcome, thereby providing a strong guarantee for the safe and efficient operation of the power distribution network under the background of large-scale access of distributed photovoltaic.

[0067] It should be understood that, although Figure 2 the steps in the flowchart are shown in order according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly described herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, Figure 2At least one of the steps in the above method can include a plurality of sub-steps or a plurality of stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least one part of other steps or sub-steps or stages of other steps.

[0068] To better implement the fault identification method for the distributed photovoltaic active power distribution network, the embodiment of the application further provides a fault identification device for the distributed photovoltaic active power distribution network based on the above-mentioned fault identification method for the distributed photovoltaic active power distribution network. The meanings of the terms are the same as in the above-mentioned fault identification method for the distributed photovoltaic active power distribution network, and specific implementation details can be referred to the description in the method embodiment.

[0069] Please refer to Figure 6 , Figure 6 The structure diagram of the fault identification device for the distributed photovoltaic active power distribution network provided by the embodiment of the application is shown in the figure, which can specifically include a construction module 201, an analysis module 202, a comparison module 203, and a determination module 204, and can be specifically as follows: The construction module 201 is configured to construct an electrical response characteristic model of the power distribution network containing distributed photovoltaic when a single-phase ground fault occurs, and the electrical response characteristic model at least includes current characteristics in a fault state; The analysis module 202 is configured to extract fault feature information reflecting the distortion characteristics of the fault current waveform based on the electrical response characteristic model, and analyze the waveform distortion degree represented by the fault feature information; The comparison module 203 is configured to compare the waveform distortion degree with a preset reference to generate a comparison result; The determination module 204 is configured to determine the fault direction of the power distribution network based on the comparison result.

[0070] Further, in some embodiments, the construction module 201 can specifically include: A mechanism analysis unit is configured to analyze the influence mechanism of the distributed photovoltaic access on the fault current characteristics of the power distribution network; A model establishment unit is configured to establish a basic electrical parameter model of the power distribution network in a normal operating state; A model derivation unit is configured to derive and generate the electrical response characteristic model based on the basic electrical parameter model and the single-phase ground fault condition.

[0071] Further, in some embodiments, the analysis module 202 can specifically include: A feature analysis unit is configured to analyze the transient current component feature in the fault state from the electrical response characteristic model; a mathematical transformation unit, configured to perform mathematical transformation on the transient current component feature to generate an intermediate feature quantity reflecting waveform distortion characteristics; a fault feature unit, configured to construct and output fault feature information based on the intermediate feature quantity.

[0072] Further, in some embodiments, the mathematical transformation unit can be specifically configured to: perform coupling operation on the transient current component feature after mathematical transformation and a modulation function; generate the intermediate feature quantity containing characteristic harmonic components through the coupling operation.

[0073] Further, in some embodiments, the analysis module 202 can further include: a signal acquisition unit, configured to acquire a fault current time domain signal to be analyzed according to the fault feature information; a discrete sampling unit, configured to perform discrete sampling on the fault current time domain signal to form a current data sequence; a quantitative analysis unit, configured to perform waveform distortion quantitative analysis on the current data sequence to output a quantitative index value representing the distortion degree.

[0074] Further, in some embodiments, the quantitative analysis unit can be specifically configured to: calculate a harmonic spectrum distribution of the current data sequence; calculate the waveform distortion quantitative index based on the harmonic spectrum distribution.

[0075] Further, in some embodiments, the determination module 204 can include: a first determination unit, configured to determine a reverse direction fault when the comparison result is that the waveform distortion degree is greater than the preset reference, the reverse direction fault being represented by that the fault point is located on the line side of the protection installation relative to the distributed photovoltaic power supply; a second determination unit, configured to determine a positive direction fault when the comparison result is that the waveform distortion degree is less than or equal to the preset reference, the positive direction fault being represented by that the fault point is located on the bus side of the protection installation relative to the distributed photovoltaic power supply.

[0076] The specific limitations of the fault discrimination device for the distributed photovoltaic active distribution network can be referred to the limitations of the fault discrimination method for the distributed photovoltaic active distribution network as described above, which will not be repeated here. Each module in the fault discrimination device for the distributed photovoltaic active distribution network described above can be realized by software, hardware and combinations thereof, in whole or in part. Each module described above can be embedded in or independent of the processor in the computer device in hardware form, or can be stored in the memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to each module.

[0077] The fault identification device for distributed photovoltaic (PV) active distribution networks provided in this embodiment constructs an electrical response characteristic model of the distribution network including distributed PV when a single-phase ground fault occurs through a construction module 201. The electrical response characteristic model includes at least the current characteristics under the fault state. Based on the electrical response characteristic model, an analysis module 202 extracts fault feature information reflecting the waveform distortion characteristics of the fault current and analyzes the degree of waveform distortion represented by the fault feature information. A comparison module 203 compares the waveform distortion degree with a preset benchmark to generate a comparison result. A judgment module 204 determines the fault direction of the distribution network based on the comparison result. This embodiment effectively solves the problem of complex and changing fault characteristics caused by distributed PV access, shortens fault identification time, improves the sensitivity and accuracy of fault identification, and facilitates rapid fault isolation and restoration of the distribution network, thereby ensuring the safe and stable operation of the distribution network.

[0078] Furthermore, embodiments of this application also provide an electronic device, such as... Figure 7 As shown, it illustrates a structural schematic diagram of the electronic device involved in the embodiments of this application, specifically: The electronic device may include components such as a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, a power supply 303, and an input unit 304. Those skilled in the art will understand that... Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 301 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 302, and by calling data stored in the memory 302, thereby providing overall monitoring of the electronic device. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 301.

[0079] The memory 302 can be used to store software programs and modules, and the processor 301 executes various function applications and the fault identification method for the distributed photovoltaic active power distribution network by running the software programs and modules stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; and the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device. Accordingly, the memory 302 can also include a memory controller to provide the processor 301 with access to the memory 302.

[0080] The electronic device also includes a power supply 303 for powering various components. Preferably, the power supply 303 can be logically connected to the processor 301 through a power management system, so as to realize functions such as management of charging, discharging, and power consumption management through the power management system. The power supply 303 can also include one or more direct current or alternating current power supplies, a recharging system, a power supply fault detection circuit, a power supply converter or inverter, a power supply state indicator, and the like.

[0081] The electronic device can also include an input unit 304, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0082] Although not shown, the electronic device can also include a display unit and the like, which will not be described here. Specifically, in the present embodiment, the processor 301 in the electronic device loads the executable file corresponding to the process of one or more application programs into the memory 302 according to the following instructions, and runs the application programs stored in the memory 302 by the processor 301, so as to realize various functions, as follows: A model of electrical response characteristics of a power distribution network containing distributed photovoltaics when a single-phase ground fault occurs is constructed, the model of electrical response characteristics at least including current characteristics in a fault state; based on the model of electrical response characteristics, fault feature information reflecting distortion characteristics of a fault current waveform is extracted, and a distortion degree of the waveform represented by the fault feature information is analyzed; the distortion degree of the waveform is compared with a preset reference, and a comparison result is generated; and a fault direction of the power distribution network is determined based on the comparison result.

[0083] The specific implementation of each operation can refer to the previous embodiments, which will not be described here.

[0084] The embodiment of the present application can effectively solve the problem of complex change of fault characteristics caused by distributed photovoltaic access, shorten the fault discrimination time, improve the sensitivity and accuracy of fault discrimination, and is beneficial to fast isolation of fault and recovery of operation of the distribution network, thereby guaranteeing the safe and stable operation of the distribution network.

[0085] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions or by controlling relevant hardware by the instructions, which can be stored in a computer readable storage medium and loaded and executed by a processor.

[0086] To this end, the embodiment of the present application provides a storage medium, which stores a plurality of instructions capable of being loaded by a processor to execute the steps in any one of the fault discrimination methods for the distributed photovoltaic active distribution network provided by the embodiment of the present application. For example, the instructions can execute the following steps: The electrical response characteristic model of the distribution network containing distributed photovoltaic when a single-phase ground fault occurs is constructed, and the electrical response characteristic model at least includes current characteristics in a fault state; based on the electrical response characteristic model, fault feature information reflecting the distortion characteristics of the fault current waveform is extracted, and the degree of waveform distortion represented by the fault feature information is analyzed; the degree of waveform distortion is compared with a preset reference to generate a comparison result; and the fault direction of the distribution network is determined based on the comparison result.

[0087] The specific implementation of the above operations can be referred to the foregoing embodiments, which will not be described here.

[0088] The storage medium can include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0089] Since the instructions stored in the storage medium can execute the steps in any one of the fault discrimination methods for the distributed photovoltaic active distribution network provided by the embodiment of the present application, the beneficial effects that can be achieved by any one of the fault discrimination methods for the distributed photovoltaic active distribution network provided by the embodiment of the present application can be achieved, which will not be described here in detail.

[0090] The above describes in detail a fault discrimination method for a distributed photovoltaic active distribution network and related equipment provided by the embodiments of the present application. The principles and implementation manners of the present application are described by using specific examples. The above description of the embodiments is only used to help understand the method of the present application and its core idea. Meanwhile, for those skilled in the art, the specific implementation manners and application ranges will be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A fault diagnosis method for distributed photovoltaic active power distribution networks, characterized in that, Includes the following steps: An electrical response characteristic model of a distribution network including distributed photovoltaics is constructed when a single-phase ground fault occurs. The electrical response characteristic model includes at least the current characteristics under the fault state. Based on the electrical response characteristic model, fault feature information reflecting the waveform distortion characteristics of fault current is extracted, and the degree of waveform distortion characterized by the fault feature information is analyzed. The waveform distortion level is compared with a preset benchmark to generate a comparison result; The direction of the fault in the distribution network is determined based on the comparison results.

2. The fault diagnosis method for distributed photovoltaic active distribution networks according to claim 1, characterized in that, The construction of the electrical response characteristic model of a distribution network including distributed photovoltaic power when a single-phase ground fault occurs includes: Analyze the impact mechanism of distributed photovoltaic (PV) grid connection on the fault current characteristics of distribution networks; Establish a basic electrical parameter model of the power distribution network under normal operating conditions; Based on the aforementioned basic electrical parameter model and single-phase grounding fault conditions, an electrical response characteristic model is derived.

3. The fault diagnosis method for distributed photovoltaic active distribution networks according to claim 1, characterized in that, The step of extracting fault feature information reflecting the waveform distortion characteristics of fault current based on the electrical response characteristic model includes: The transient current component characteristics under fault conditions are analyzed from the electrical response characteristic model; The transient current component characteristics are mathematically transformed to generate intermediate characteristic quantities that reflect waveform distortion characteristics. Based on the intermediate feature quantity, the fault feature information is constructed and output.

4. The fault diagnosis method for distributed photovoltaic active distribution networks according to claim 3, characterized in that, The step of performing mathematical transformation on the transient current component characteristics to generate intermediate feature quantities reflecting waveform distortion characteristics includes: The transient current component characteristics after mathematical transformation are coupled with the modulation function for calculation. The intermediate feature quantity containing characteristic harmonic components is generated through the coupling operation.

5. The fault diagnosis method for distributed photovoltaic active distribution networks according to claim 3, characterized in that, The analysis of the waveform distortion degree characterized by the fault feature information includes: Based on the fault characteristic information, the time-domain signal of the fault current to be analyzed is obtained; The fault current time-domain signal is discretely sampled to form a current data sequence; The current data sequence is subjected to waveform distortion analysis, and a quantitative index value representing the degree of distortion is output.

6. The fault diagnosis method for distributed photovoltaic active distribution networks according to claim 5, characterized in that, The waveform distortion analysis of the current data sequence, outputting a quantitative index value characterizing the degree of distortion, includes: Calculate the harmonic spectrum distribution of the current data sequence; The waveform distortion index is calculated based on the harmonic spectrum distribution.

7. The fault diagnosis method for distributed photovoltaic active distribution networks according to claim 1, characterized in that, Determining the fault direction of the distribution network based on the comparison result includes: When the comparison result shows that the waveform distortion is greater than the preset benchmark, it is determined to be a reverse direction fault. The reverse direction fault is characterized by the fault point being located at the protection installation point in the direction relative to the line side of the distributed photovoltaic power source. When the comparison result shows that the waveform distortion is less than or equal to the preset benchmark, it is determined to be a positive direction fault. The positive direction fault is characterized by the fault point being located at the protection installation point in the direction relative to the bus side of the distributed photovoltaic power source.

8. A fault detection device for distributed photovoltaic active power distribution networks, characterized in that, include: A construction module is used to construct an electrical response characteristic model of a distribution network including distributed photovoltaics when a single-phase ground fault occurs. The electrical response characteristic model includes at least the current characteristics under the fault state. The analysis module is used to extract fault feature information reflecting the waveform distortion characteristics of fault current based on the electrical response characteristic model, and to analyze the degree of waveform distortion characterized by the fault feature information. The comparison module is used to compare the waveform distortion level with a preset benchmark and generate a comparison result; The determination module is used to determine the fault direction of the distribution network based on the comparison result.

9. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the fault detection method for a distributed photovoltaic active distribution network as described in any one of claims 1-7.

10. A storage medium, characterized in that, The system stores a computer program capable of being loaded by a processor and executed as described in any one of claims 1-7 for fault diagnosis of a distributed photovoltaic active distribution network.

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