A method and system for detecting faults in a photovoltaic storage and distribution system

Abstract

The present invention discloses a method and system for detecting faults in a photovoltaic power storage and distribution system, which relates to the technical field of fault detection in photovoltaic power storage and distribution systems, and comprises the following steps: collecting system electrical quantity signals by setting a plurality of signal detection points in the photovoltaic power storage and distribution system, and dividing the system electrical quantity signals into time windows; decomposing the electrical quantity signals in the time window by using discrete wavelet transform with nonlinear chaos embedding to obtain optimal high-frequency wavelet coefficients; calculating the signal energy coefficient based on the optimal high-frequency wavelet coefficients, and calculating the energy mutation coefficient as the energy mutation feature of the signal by ratio with a preset normal state energy value; constructing a fault identification criterion based on the changing trend and value of the energy mutation feature in combination with a preset fault reference value, and detecting and judging whether a fault occurs; constructing an energy direction criterion based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, and determining the area to which the fault belongs according to the energy direction judgment results of a plurality of detection points.

Description

Technical Field

[0001] The present invention relates to the technical field of fault detection methods for photovoltaic storage and distribution systems, and more specifically, to a fault detection method for photovoltaic storage and distribution systems based on discrete wavelet transform. Background Art

[0002] Photovoltaic power generation, energy storage technologies, and DC power distribution are gaining widespread application in building power systems, forming a "PV-storage-DC-flexible" power distribution system that integrates power generation, storage, transmission, and flexible consumption. This system offers advantages such as compact structure, rapid response, and flexible energy management, making it a key technological path toward building electrification and low-carbon energy production.

[0003] However, due to the complex system structure and diverse operating modes, PV-storage-direct-flexible power distribution systems are susceptible to faults such as equipment aging, short circuits, and grounding during operation. Failure to promptly identify and isolate faults can lead to system voltage instability, power quality degradation, and even power outages. Therefore, research on efficient and reliable fault detection and location technologies is crucial to ensuring the safe operation of these systems.

[0004] Currently, most fault detection and protection methods rely on solutions from high-voltage direct current (HVDC) or low-voltage alternating current (LVAC) distribution systems. These methods suffer from the following drawbacks: First, the voltage and current signals in PV-storage systems lack power frequency characteristics, making traditional frequency-domain analysis methods difficult to apply and resulting in poor fault identification accuracy. Second, due to the short lines and low impedance of the system, traditional impedance-based or traveling-wave location methods cannot accurately locate the fault area. Therefore, a new fault detection method for PV-storage direct current (DC-FL) distribution systems that combines rapid fault detection with regional location is urgently needed.

[0005] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0006] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a fault detection method for a photovoltaic power storage and distribution system, which can accurately detect fault signals and accurately locate the fault.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for detecting faults in a photovoltaic power storage and distribution system comprises the following steps: S1, collecting system electrical quantity signals by setting up a plurality of signal detection points in the photovoltaic power storage and distribution system, and dividing the system electrical quantity signals into time windows; S2, decomposing the electrical quantity signals in the time window by using discrete wavelet transform to obtain optimal high-frequency wavelet coefficients; S3, calculating the signal energy coefficient based on the optimal high-frequency wavelet coefficients, and calculating the energy mutation coefficient as the energy mutation feature of the signal by ratio with a preset normal state energy value; S4, constructing a fault identification criterion based on the changing trend and value of the energy mutation feature in combination with a preset fault reference value to detect and judge whether a fault has occurred; S5, constructing an energy direction criterion based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, and determining the area to which the fault belongs based on the energy direction judgment results of a plurality of detection points; the fault identification criterion is based on the analysis of electrical quantity signals of different fault types, and uses the changing trend and value of the energy mutation coefficient in combination with the energy mutation coefficient values ​​of a plurality of sampling periods after the fault occurs to judge whether a fault signal has occurred.

[0009] In a preferred embodiment, the specific steps for obtaining the optimal high-frequency wavelet coefficients in S2 are as follows: performing wavelet decomposition and phase space reconstruction on the electrical quantity signal in the current time window to obtain the chaotic dynamics eigenvalue, and using the maximum chaotic dynamics eigenvalue as the optimization target to search the parameter space based on the particle swarm algorithm to obtain the optimal wavelet decomposition level; based on the optimal wavelet decomposition level, performing discrete wavelet transform on the electrical quantity signal in the time window to obtain the optimal high-frequency wavelet coefficients for subsequent energy analysis and fault identification.

[0010] In a preferred embodiment, the electrical quantity signal in the current time window is subjected to wavelet decomposition and phase space reconstruction to obtain chaotic dynamics eigenvalues, and the optimal wavelet decomposition level is obtained by searching the parameter space based on the particle swarm algorithm with the maximum chaotic dynamics eigenvalue as the optimization target. The specific steps are as follows: S21, setting the wavelet basis function and several decomposition layer combinations for each input time window signal, performing discrete wavelet transform, and obtaining high-frequency wavelet coefficients; S22, performing phase space reconstruction on the optimal high-frequency wavelet coefficients by setting chaotic embedding parameters to obtain a chaotic phase space trajectory set; S23, calculating the chaotic dynamics eigenvalue for each phase space trajectory in the chaotic phase space trajectory set; S24, taking the maximum chaotic dynamics eigenvalue as the optimization target, using particle swarm optimization to search for the optimal parameter combination in the parameter space, wherein the parameter space includes the wavelet decomposition level and the chaotic embedding parameter.

[0011] In a preferred embodiment, the discrete wavelet transform in S2 uses the 4th-order Daubechies wavelet as the wavelet basis function.

[0012] In a preferred embodiment, the S4 constructs a fault identification criterion based on the changing trend and value of the energy mutation characteristic and the preset fault reference value, and after detecting and judging whether the fault occurs, it also includes executing a fault type identification step if the fault occurs; the fault type identification step is specifically as follows: when the electrical quantity signals of the DC positive and negative poles of the detection point both meet the fault identification criterion, the fault type is determined to be a bipolar short circuit fault; when only the electrical quantity signal of one pole meets the fault identification criterion, the fault type is determined to be a single-pole grounding fault, and the fault type is further determined to be a positive pole grounding fault or a negative pole grounding fault based on the polarity of the signal.

[0013] In a preferred embodiment, the specific construction method of the energy direction criterion in S5 is as follows: based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, the energy mutation intensity coefficient of the electrical quantity signal corresponding to different fault types is calculated; the sign of the energy mutation intensity coefficient is judged, if the result is positive, it is a reverse mutation, and the signal flow direction is the specified reverse direction; if the result is negative, it is a forward mutation, and the signal flow direction is the specified positive direction.

[0014] In a preferred embodiment, the fault area identification in S5 is judged based on the energy flow direction criterion, and the judgment steps are as follows: S51, by setting detection points at both ends of each branch node of the photovoltaic storage and distribution system and installing measurement and control devices, the distribution grid topology is divided into several fault identification areas, each area is composed of a pair of adjacent detection points; S52, after the detection point identifies the fault signal, the signal flow direction is judged according to the constructed energy direction criterion; S53, if the signal flow directions of the detection points at both ends of a certain identification area both point to the inside of the area, it is judged that the fault occurs in the area, and the area is marked as the fault area.

[0015] A fault detection system for a photovoltaic power storage and distribution system comprises a sampling module, a wavelet analysis module, a fault detection module, a fault area identification module and a fault location module; the sampling module is used to collect system electrical quantity signals by setting a plurality of signal detection points in the photovoltaic power storage and distribution system, and divide the system electrical quantity signals into time windows; the wavelet analysis module is used to decompose the electrical quantity signals within the time window by using discrete wavelet transform with nonlinear chaos embedding to obtain optimal high-frequency wavelet coefficients; the fault detection module is used to calculate the signal energy coefficient based on the optimal high-frequency wavelet coefficients, and calculate the energy mutation coefficient as the energy mutation feature of the signal by ratio with a preset normal state energy value; the fault area identification module is used to construct a fault identification criterion based on the changing trend and value of the energy mutation feature in combination with a preset fault reference value, and detect and determine the occurrence of a fault; the fault location module is used to construct an energy direction criterion based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, and determine the area to which the fault belongs based on the energy direction determination results of a plurality of detection points.

[0016] The technical effects and advantages of the present invention's method for detecting faults in a photovoltaic power storage and distribution system are as follows:

[0017] 1. The present invention performs discrete wavelet transform on the electrical quantity signals of the photovoltaic storage and distribution system within a sliding time window, extracts the optimal high-frequency wavelet coefficients, and calculates the energy mutation coefficient. This enables rapid feature extraction and accurate identification of nonlinear and non-stationary fault signals, thereby improving the real-time performance and accuracy of fault detection.

[0018] 2. The present invention constructs an energy direction criterion based on the symbol information of the optimal high-frequency wavelet coefficients, and combines the directional judgment results of multiple detection points to quickly identify the direction of fault energy flow, thereby accurately locating the area to which the fault belongs, avoiding the problem of difficult positioning caused by low impedance or unclear traveling wave reflection in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of a method for detecting faults in a photovoltaic power storage and distribution system according to the present invention;

[0020] Figure 2 This is a graph showing the wavelet transform results of the same voltage fault signal with different window lengths according to the present invention;

[0021] Figure 3 This is the Lyapunov exponent distribution diagram under the wavelet decomposition and chaos parameter optimization of the present invention;

[0022] Figure 4 This is a schematic diagram of the fault signal conversion and fault judgment process of the present invention;

[0023] Figure 5 Schematic diagram of the flow process for determining the direction of signal energy flow according to the present invention;

[0024] Figure 6 This is a schematic diagram of the fault occurrence area of ​​the present invention;

[0025] Figure 7 This is a system schematic diagram of a fault detection method for a photovoltaic storage and distribution system according to the present invention. DETAILED DESCRIPTION

[0026] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0027] Example 1, Figure 1 The present invention provides a method for detecting faults in a photovoltaic power storage and distribution system, comprising the following steps:

[0028] S1: In the photovoltaic storage and distribution system, several signal detection points are set up to collect the system electrical quantity signals, and the system electrical quantity signals are divided into time windows.

[0029] In this embodiment, the electrical quantity signals are current and voltage signals;

[0030] The time window division specifically includes:

[0031] Wavelet transform experiments were performed on the collected signals in different time windows to obtain the characteristic expression of the signal and the wavelet transform calculation rate. The wavelet transform method was referenced from J. PFV. Discrete Wavelet Transformations: An Elementary Approach with Applications[M]. John Wiley & Sons, Inc.

[0032] The time window size is determined based on the feature expression and calculation rate.

[0033] It should be noted that the feature expression degree is evaluated by analyzing the change amplitude and stability of the wavelet coefficients in the signal after wavelet transformation, specifically including calculating the standard deviation and amplitude change rate of the wavelet coefficients;

[0034] The calculation rate is measured by the time required to calculate each window and the complexity of signal processing. Specifically, when the calculation rate is too high, it means that the window is too long, resulting in calculation delay;

[0035] The optimal time window size is determined by combining feature expression and calculation rate with experimental data.

[0036] The specific calculation formulas for the standard deviation and amplitude change rate of the wavelet coefficients are as follows:

[0037]

[0038]

[0039] in, is the i-th wavelet coefficient, is the mean of the wavelet coefficients, is the number of sampled data, is the standard deviation of the wavelet coefficients; and are the maximum and minimum values ​​in the wavelet coefficient sequence D, respectively. is the rate of change of the amplitude of the wavelet coefficients.

[0040] In this embodiment, wavelet transform experiments are performed on the same voltage fault signal with different window lengths. The experimental results are as follows: Figure 2 As shown in the figure, when the window length is 10 sampling points, the characteristics of the wavelet coefficients after wavelet transformation are not obvious, the standard deviation is about 0.0125, the amplitude change rate is about 0.09, the characteristic volatility is insufficient, and it is difficult to reflect the non-stationary characteristics of the fault disturbance. When the window length is 50 sampling points, the transformation effect is significantly enhanced, the standard deviation of the wavelet coefficients is about 0.0841, the amplitude change rate is increased to 0.53, the fault mutation characteristics are clear, the signal-to-noise ratio is high, and the computing resource overhead is within a controllable range. When the window length is further increased to 100 and 200 sampling points, although the standard deviation reaches 0.0895 and 0.0912 respectively, and the amplitude change rate remains in the range of 0.51–0.55, due to the excessively long window, the average time taken for each wavelet decomposition calculation increases to 3.1ms and 6.7ms respectively, which is not conducive to online real-time processing. After comprehensive consideration, this embodiment selects 50 as the signal window length for simulation and subsequent steps, that is, the signal analyzed in each sampling period is a collection of 50 sampling values ​​in the past 50ms.

[0041] S2, using discrete wavelet transform to decompose the electrical quantity signal in the time window to obtain optimal high-frequency wavelet coefficients.

[0042] In this embodiment, before the discrete wavelet transform in S2, to avoid distortion caused by edge effects, boundary compensation processing is also performed on the electrical quantity signal using a symmetric extension method. The symmetric extension method can avoid the introduction of discontinuous boundaries in a non-fault state, thereby enhancing the robustness of the transformation result.

[0043] The specific steps of obtaining the optimal high-frequency wavelet coefficients are as follows:

[0044] The electrical quantity signal in the current time window is subjected to wavelet decomposition and phase space reconstruction to obtain the chaotic dynamics eigenvalue. The optimal wavelet decomposition level is obtained by searching the parameter space based on the particle swarm algorithm with the maximum chaotic dynamics eigenvalue as the optimization target.

[0045] Based on the optimal wavelet decomposition level, discrete wavelet transform is performed on the electrical quantity signal within the time window to obtain optimal high-frequency wavelet coefficients for subsequent energy analysis and fault identification.

[0046] The electrical quantity signal in the current time window is subjected to wavelet decomposition and phase space reconstruction to obtain chaotic dynamics eigenvalues, and the optimal wavelet decomposition level is obtained by searching the parameter space based on the particle swarm algorithm with the maximum chaotic dynamics eigenvalue as the optimization target. The specific steps are as follows:

[0047] S21, for the current signal x(t) in a sliding time window with a sampling frequency of 1kHz and a length of 50 points, set the wavelet basis function Ψ and the number of decomposition layers Combine to form a candidate configuration:

[0048]

[0049] Based on candidate configuration , perform discrete wavelet transform (DWT) on the current signal x(t) and get the corresponding k High-frequency wavelet coefficients of layer DWT , the specific formula is as follows:

[0050]

[0051] S22, for each By setting the chaos embedding parameters such as chaos embedding dimension m and time delay τ, the phase space is reconstructed to form the phase space trajectory. The specific formula is as follows:

[0052]

[0053] The output is a set of chaotic phase space trajectories ;

[0054] S23, for the chaotic phase space trajectory set Each phase space trajectory , calculate the chaotic dynamics characteristic index. In this embodiment, the chaotic dynamics characteristic index is the Lyapunov index, and the specific calculation formula is as follows:

[0055]

[0056] in, is the nearest point in the phase space of the phase space trajectory of the kth candidate wavelet channel; is the local evolution time step; is the time window length.

[0057] In this paper, the Lyapunov exponent is used as a chaotic characteristic indicator to evaluate the stability and dynamic sensitivity of the nonlinear trajectory corresponding to each wavelet decomposition channel. The Lyapunov exponent reflects the exponential growth rate of the perturbation over time in phase space and is a core indicator of the chaotic complexity of a signal.

[0058] Compared with traditional energy indicators or transformation coefficient modulus statistics, the Lyapunov exponent is more suitable for transient response analysis under dynamic disturbances. It can effectively enhance the system's ability to recognize early signs of electrical faults, improve the adaptability and generalization ability of decomposition level selection, and thus improve the sensitivity and accuracy of fault detection.

[0059] S24, using the particle swarm optimization algorithm, with the maximum Lyapunov exponent as the objective function, in the parameter space ( , , ) Search for the optimal wavelet decomposition level and optimal chaotic embedding dimension and optimal time delay , the specific formula is as follows:

[0060] Obj( , , ) = max( )

[0061] In this embodiment, the sampled current signal x(t) is subjected to the above steps S21-S25, and the result is as follows: Figure 3 shown. Figure 3 Shows the number of wavelet decomposition layers , Chaos Embedding Dimension and time delay The changes of Lyapunov exponent in three-dimensional parameter space. The analysis results show that when Therefore, this embodiment preferably adopts a layer of wavelet decomposition and uses the extracted high-frequency wavelet coefficients as the basis for subsequent energy analysis.

[0062] It should be noted that the optimal chaotic embedding dimension and optimal time delay , used to reconstruct the phase space of the feedback signal. During the search process, the choice of embedding dimension and time delay parameters directly affects the stability and nonlinear dynamic characteristics of the signal's phase space trajectory, thereby affecting the calculation of the Lyapunov exponent. Through particle swarm optimization, the optimal chaotic embedding dimension and time delay parameters for the current signal characteristics were ultimately determined, ensuring the stability and accuracy of the maximum Lyapunov exponent.

[0063] The discrete wavelet transform uses the 4th order Daubechies (db4) wavelet as the wavelet basis function;

[0064] The db4 wavelet is an orthogonal, compactly supported wavelet with strong mutation detection capability, suitable for high-frequency feature extraction of nonlinear transient signals. Compared with wavelets such as Haar, the db4 wavelet has better smoothness while ensuring high-frequency resolution, facilitating subsequent energy calculation.

[0065] In this embodiment, a sliding analysis window containing 50 sampling points is preferably extracted within three consecutive sampling periods after the fault occurs, and wavelet transform analysis is performed on the data within each window. Since the sampling frequency is 1kHz, each analysis window covers a 50ms period. The three windows correspond to historical data segments collected 1ms, 2ms, and 3ms after the fault occurs. By continuously analyzing these three windows, the dynamic changing trends of the fault signal characteristics can be observed, improving the robustness and accuracy of the fault judgment criteria.

[0066] It should be noted that compared with traditional Fourier transform, the present invention can more accurately extract fault features in non-stationary signals through discrete wavelet transform. Combined with subsequent energy mutation analysis, it shows good real-time performance and recognition accuracy in both simulation and hardware experiments.

[0067] S3, calculating the signal energy coefficient based on the optimal high-frequency wavelet coefficient, and calculating the energy mutation coefficient as the energy mutation feature of the signal through the ratio with the preset normal state energy value.

[0068] In a photovoltaic-storage-DC flexible AC / DC system, when a fault occurs, its voltage and current signals are often accompanied by obvious high-frequency disturbances, which manifests as rich transient fault information. Discrete wavelet transform can decompose the original signal into multi-scale frequency bands, among which the high-frequency components can better reflect the characteristics of drastic changes caused by sudden events. However, the wavelet coefficients themselves are discrete sequences, and it is difficult to directly construct a stability criterion. Therefore, this embodiment further introduces the energy calculation of high-frequency wavelet coefficients as a descriptive indicator to effectively extract the fault mutation characteristics contained in the signal. By performing a ratio operation on this energy value and the baseline energy under normal operating conditions, a quantitative "energy mutation coefficient" can be formed as the core parameter for fault judgment.

[0069] The calculation formula of the signal energy coefficient is as follows:

[0070]

[0071] in, represents the i-th optimal high-frequency wavelet coefficient;

[0072] The calculation formula of the energy mutation coefficient is as follows:

[0073]

[0074] in, is the signal energy coefficient value calculated under normal conditions, a voltage and current signal calculated from the two-terminal topology simulation model. As shown in Table 1; For the n Periodic real-time signal energy coefficient value; is the energy mutation coefficient of the real-time signal in the nth cycle.

[0075] Table 1

[0076]

[0077] In this embodiment, the corresponding voltage and current signals are calculated for different faults. , taking the window length as 50, analyzing the value of the energy mutation coefficient in three sampling cycles (sampling cycle is 1ms) after the fault occurs, the simulation and calculation results are shown in Table 2:

[0078] Table 2

[0079]

[0080] S4, based on the changing trend and value of the energy mutation characteristics and combined with the preset fault reference value, builds a fault identification criterion to detect and determine whether a fault occurs.

[0081] In this embodiment, the fault identification criteria are as follows:

[0082]

[0083] in, is the set normal state energy mutation coefficient;

[0084] The fault identification criteria are constructed by combining the preset fault reference values ​​to detect and determine whether a fault has occurred, as follows:

[0085] From the analysis of the fault reference value data in Table 2, it can be seen that when a fault occurs, the magnitude of the energy mutation coefficient of the voltage and current signals of the fault pole can reach 10e6 and 10e7, while the magnitude of the non-fault pole signals can reach 10e6 and 10e7. The magnitude is not as high as this. In addition, the data within 3ms after the fault will first increase and then decrease.

[0086] Therefore, if Figure 4 As shown, the real-time signal energy mutation coefficient 1ms after the fault Greater than , real-time signal energy mutation coefficient 2ms after fault Greater than And it is greater than the real-time signal energy mutation coefficient 3ms after the fault When , it is determined that the line at this point has a fault;

[0087] If only Greater than , it is determined that the line at this point has no fault but has an abnormality, and an abnormal signal is issued.

[0088] The fault identification criterion is constructed based on the changing trend and value of the energy mutation characteristic in combination with the preset fault reference value, and after detecting and judging whether a fault has occurred, the step of performing a fault type identification step is also included if a fault occurs;

[0089] The fault type identification step is as follows: Figure 4 As shown, the details are as follows:

[0090] When the electrical quantity signals of the DC positive and negative poles of the detection point both meet the fault identification criteria, the fault type is determined to be a bipolar short circuit fault;

[0091] When only one pole's electrical quantity signal meets the fault identification criteria, the fault type is determined to be a single-pole grounding fault, and the fault type is further determined to be a positive grounding fault or a negative grounding fault based on the polarity of the signal.

[0092] S5, constructing an energy direction criterion based on the signal energy coefficient and the sign information of the optimal high-frequency wavelet coefficient, and determining the fault area according to the energy direction judgment results of several detection points.

[0093] In this embodiment, the specific method for constructing the energy direction criterion is as follows:

[0094] Based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, the energy mutation intensity coefficient of the electrical quantity signal corresponding to different fault types is calculated;

[0095] The sign of the energy mutation intensity coefficient is judged. If the result is positive, it is a reverse mutation, and the signal flow direction is the specified reverse direction. If the result is negative, it is a forward mutation, and the signal flow direction is the specified positive direction.

[0096] The specific calculation formula of the energy mutation intensity coefficient is as follows:

[0097]

[0098] in, Indicates taking Symbols;

[0099] In a specific embodiment, for different fault corresponding current signals, the corresponding ， Taking the window length as 50, the value of the current energy mutation coefficient in the three sampling cycles after the fault occurs is analyzed. The fault reference values ​​of the simulation and calculation results are shown in Table 3:

[0100] Table 3

[0101]

[0102] Combined with the rules shown in Table 3, the criterion for the energy flow direction of the fault signal of the single-ended measurement and control device can be defined as follows:

[0103] Set 1ms, 2ms, 3ms after the fault They are ， ， , for sgn( ) is used for judgment. If the result is 1, it is a reverse mutation and the signal flow direction is the specified reverse direction. If the result is -1, the signal flow direction is the specified positive direction. In addition, the following criteria need to be added to prevent misjudgment:

[0104]

[0105] in is the set normal state energy mutation intensity coefficient; in summary, the signal energy flow direction judgment process is as follows Figure 6 As shown, specifically:

[0106] After a fault occurs, determine whether If so, continue checking and If the signs are the same, continue to check and Are the signs the same? If they are the same, continue to judge whether If so, then according to If the fault direction is determined by It is a reverse mutation, and the direction of signal flow is the opposite of the prescribed direction. The signal flow direction is the specified positive direction.

[0107] Furthermore, the fault area identification is performed based on the energy flow direction criterion, and the judgment steps are as follows:

[0108] S51, by setting detection points at both ends of each branch node of the photovoltaic storage distribution system and installing measurement and control devices, the distribution grid topology is divided into several fault identification areas, each area consisting of a pair of adjacent detection points;

[0109] S52, after the detection point identifies the fault signal, the signal flow direction is determined according to the constructed energy direction criterion;

[0110] S53: If the signal flow directions of the detection points at both ends of a certain identification area are both directed to the interior of the area, it is determined that the fault occurs in the area, and the area is marked as a fault area.

[0111] It should be noted that after identifying the direction of energy flow, each measurement and control device in the power grid system can obtain the fault point direction information. Each fault identification area divided by the measurement and control device determines whether the fault occurs in this area based on the fault direction information calculated by the measurement and control devices at both ends. If the fault occurs in this judgment area, the following will occur Figure 6 Situation shown: The fault directions determined by the measurement and control devices at 2 on the left and 3 on the right are opposite, pointing to the area within 1.

[0112] It should be noted that after the fault area is obtained, the location of the fault area in the entire photovoltaic storage direct-flexible power distribution system can be obtained based on the number or corresponding position of the measurement and control device or the fault judgment area.

[0113] Figure 7 It is a fault detection system for the photovoltaic storage and distribution system, including a sampling module, a wavelet analysis module, a fault detection module, a fault area identification module, and a fault location module;

[0114] The sampling module is used to collect the system electrical quantity signals by setting up several signal detection points in the photovoltaic storage and distribution system, and divide the system electrical quantity signals into time windows;

[0115] Wavelet analysis module, which is used to decompose the electrical quantity signal within the time window using discrete wavelet transform embedded with nonlinear chaos to obtain the optimal high-frequency wavelet coefficients;

[0116] The fault detection module is used to calculate the signal energy coefficient based on the optimal high-frequency wavelet coefficient, and calculate the energy mutation coefficient as the energy mutation feature of the signal by comparing it with the preset normal state energy value;

[0117] The fault area identification module is used to build fault identification criteria based on the changing trend and value of the energy mutation characteristics and the preset fault reference value to detect and determine whether a fault has occurred;

[0118] The fault location module is used to construct an energy direction criterion based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, and determine the fault area according to the energy direction judgment results of several detection points.

[0119] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0120] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0121] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0122] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting faults in a photovoltaic power storage and distribution system, characterized in that: The following steps are involved: Set up several signal detection points in the photovoltaic storage and distribution system to collect system electrical quantity signals and divide the system electrical quantity signals into time windows; The discrete wavelet transform with nonlinear chaos embedding is used to decompose the electrical quantity signal in the time window to obtain the optimal high-frequency wavelet coefficient. Specifically, the electrical quantity signal in the current time window is subjected to wavelet decomposition and phase space reconstruction to obtain the chaotic dynamics eigenvalue. The optimal wavelet decomposition level is obtained by searching the parameter space based on the particle swarm algorithm with the maximum chaotic dynamics eigenvalue as the optimization target. Based on the optimal wavelet decomposition level, discrete wavelet transform is performed on the electrical quantity signal within the time window to obtain the optimal high-frequency wavelet coefficients; The signal energy coefficient is calculated based on the optimal high-frequency wavelet coefficient, and the energy mutation coefficient is calculated as the energy mutation feature of the signal by comparing it with the preset normal state energy value; Based on the changing trend and value of energy mutation characteristics, combined with the preset fault reference value, a fault identification criterion is constructed to detect and determine whether a fault has occurred; An energy direction criterion is constructed based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, and the fault area is determined based on the energy direction judgment results of several detection points; The steps for obtaining the optimal wavelet decomposition level are as follows: Set the wavelet basis function and several decomposition layer combinations for each input time window signal, perform discrete wavelet transform, and obtain high-frequency wavelet coefficients; The chaotic embedding parameters are set for the high-frequency wavelet coefficients to reconstruct the phase space and obtain a set of chaotic phase space trajectories. Calculate the chaotic dynamics eigenvalue for each phase space trajectory in the chaotic phase space trajectory set; Taking the maximum chaotic dynamics eigenvalue as the optimization goal, particle swarm optimization is used to search for the optimal parameter combination in the parameter space, which includes the wavelet decomposition level and the chaos embedding parameter.

2. The method for detecting faults in a photovoltaic power storage and distribution system according to claim 1, wherein: The discrete wavelet transform uses the 4th-order Daubechies wavelet as the wavelet basis function.

3. The method for detecting faults in a photovoltaic power storage and distribution system according to claim 2, wherein: The fault identification criterion is constructed based on the changing trend and value of the energy mutation characteristic in combination with the preset fault reference value, and after detecting and judging whether a fault has occurred, the step of performing a fault type identification step is also included if a fault occurs; The fault type identification steps are as follows: When the electrical quantity signals of the DC positive and negative poles of the detection point both meet the fault identification criteria, the fault type is determined to be a bipolar short circuit fault; When only one pole's electrical quantity signal meets the fault identification criteria, the fault type is determined to be a single-pole grounding fault, and the fault type is further determined to be a positive grounding fault or a negative grounding fault based on the polarity of the signal.

4. The method for detecting faults in a photovoltaic power storage and distribution system according to claim 3, wherein: The specific construction method of constructing the energy direction criterion is as follows: Based on the sign information of signal energy coefficient and high-frequency wavelet coefficient, the energy mutation intensity coefficient of the electrical quantity signal corresponding to different fault types is calculated; The sign of the energy mutation intensity coefficient is judged. If the result is positive, it is a reverse mutation, and the signal flow direction is the specified reverse direction. If the result is negative, it is a forward mutation, and the signal flow direction is the specified positive direction.

5. The method for detecting faults in a photovoltaic power storage and distribution system according to claim 4, wherein: The determination of the fault region, wherein the fault region identification is based on the energy flow direction criterion, is performed in the following specific steps: By setting detection points at both ends of each branch node of the photovoltaic storage distribution system and installing measurement and control devices, the distribution grid topology is divided into several fault identification areas, each of which is composed of a pair of adjacent detection points; After the detection point identifies the fault signal, the signal flow direction is determined based on the constructed energy direction criterion; If the signal flow directions of the detection points at both ends of a certain identification area are both pointing to the inside of the area, it is determined that the fault occurs in the area and the area is marked as the fault area.

6. A fault detection system for a photovoltaic power storage and distribution system for implementing the method according to any one of claims 1 to 5, characterized in that: include: The sampling module is used to collect the system electrical quantity signals by setting up several signal detection points in the photovoltaic storage and distribution system, and divide the system electrical quantity signals into time windows; Wavelet analysis module, which is used to decompose the electrical quantity signal within the time window using discrete wavelet transform embedded with nonlinear chaos to obtain the optimal high-frequency wavelet coefficients; The fault detection module is used to calculate the signal energy coefficient based on the optimal high-frequency wavelet coefficient, and calculate the energy mutation coefficient as the energy mutation feature of the signal by comparing it with the preset normal state energy value; The fault area identification module is used to build fault identification criteria based on the changing trend and value of the energy mutation characteristics and the preset fault reference value to detect and determine whether a fault has occurred; The fault location module is used to construct an energy direction criterion based on the sign information of the signal energy coefficient and the optimal high-frequency wavelet coefficient, and determine the fault area according to the energy direction judgment results of several detection points.

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