Photovoltaic cable fault simulation test method

Abstract

The invention relates to a photovoltaic cable fault simulation test method. The method comprises the following steps: establishing an operation topology model and a reference impedance model according to an infrared thermal distribution diagram and a photovoltaic array topology diagram to identify a fault characteristic section; identifying a fault coupling area according to the fault feature section, and extracting a plurality of fault coupling loops according to the fault coupling area; all cable nodes of the fault coupling loop are mapped to an impedance-capacitive reactance space to obtain waveguide modal feature points, multi-field coupling degradation values of the waveguide modal feature points are calculated, and a fault diffusion track corresponding to the fault coupling loop is generated according to the multi-field coupling degradation values; taking a cable position corresponding to a modal wave trough point of the fault diffusion track as a dielectric failure point, and taking a cable position corresponding to a modal wave crest point as a physical failure point; and performing insulation failure verification according to the physical failure point and the TDR open circuit point, and performing breakdown conduction verification according to the dielectric failure point and the leakage current peak point.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fault simulation testing, in particular to a photovoltaic cable fault simulation testing method. BACKGROUND

[0002] Power communication cables often cause unexpected interruptions due to human damage, construction quality, and natural environmental factors during actual operation. For communication operation and maintenance personnel, the fault of the optical cable is often only manifested as the interruption of various services carried by the optical cable and the optical path alarm generated by the optical communication equipment network management, but it is impossible to know which cable segment is interrupted.

[0003] The traditional photovoltaic cable fault detection method has three technical defects: 1. The existing technology relies on single quantity of infrared temperature measurement or impedance spectrum for analysis: infrared thermal imaging can locate local overheating, but cannot distinguish conductor fracture from insulation; the frequency sweep impedance method can identify electrical abnormalities, but has insufficient sensitivity to early thermal aging induced hidden faults. Lead to missed or misdiagnosed compound faults. 2. The positioning method based on time domain reflection (TDR) is easily disturbed by the branch structure of the cable, and the positioning error is large in the multi-node photovoltaic array, which cannot meet the precise positioning demand in the complex bridge. The existing technology also lacks the ability to track the fault diffusion path, making it difficult to predict potential breakdown points. 3. The mainstream method only determines the fault according to a single detection result, and does not establish a spatial correlation verification mechanism for physical location and electrical characteristics. Therefore, the existing technology needs to solve the following technical problems: to develop a photovoltaic cable diagnosis method with multi-physical field cooperation, fault diffusion tracking ability, and spatial-electrical dual verification support, to break through the limitations of single parameter detection and realize accurate judgment of fault evolution. SUMMARY

[0004] In view of the deficiencies of the prior art, the embodiments of the present application provide a photovoltaic cable fault simulation testing method. The photovoltaic cable fault simulation testing method provided by the present application comprises the following steps:

[0005] The test terminal sends the photovoltaic array topology, multi-channel sweep instruction and thermal field acquisition instruction to the fault simulation cloud platform, multi-channel tester and infrared thermal imager respectively;

[0006] The infrared thermal imager acquires the infrared thermal distribution map of the photovoltaic cable in response to the received thermal field acquisition instruction and sends it to the fault simulation cloud platform; the multi-channel tester acquires the impedance spectrum response data of the photovoltaic cable in response to the received multi-channel sweep instruction and sends it to the fault simulation cloud platform;

[0007] The fault simulation cloud platform establishes an operation topology model and a reference impedance model of the photovoltaic cable according to the infrared thermal distribution map and the photovoltaic array topology map respectively, and identifies a fault feature section in the photovoltaic cable through multi-parameter aggregation analysis of the operation topology model and the reference impedance model; the fault feature section is a cable section with insulation deterioration or conductor fracture;

[0008] The fault simulation cloud platform takes the fault feature section and cable nodes directly electrically connected with the start cable node and the end cable node of the fault feature section as a fault coupling area, and extracts a plurality of fault coupling loops according to the fault coupling area; the fault coupling loop is a closed current path containing the fault feature section;

[0009] The fault simulation cloud platform maps all cable nodes of the fault coupling loop to an impedance-capacitance space to obtain a plurality of waveguide modal feature points, calculates a multi-field coupling degradation value of each waveguide modal feature point, and sequentially connects all waveguide modal feature points according to the multi-field coupling degradation value from small to large to generate a fault diffusion trajectory corresponding to the fault coupling loop;

[0010] The fault simulation cloud platform obtains a modal wave valley point and a modal wave peak point of the fault diffusion trajectory, takes a cable position corresponding to the modal wave valley point as a dielectric failure point, and takes a cable position corresponding to the modal wave peak point as a physical failure point;

[0011] The fault simulation cloud platform analyzes impedance spectrum response data to obtain a leakage current peak point and a TDR open point of the photovoltaic cable;

[0012] The fault simulation cloud platform performs insulation failure verification according to the physical failure point and the TDR open point, and performs breakdown conduction verification according to the dielectric failure point and the leakage current peak point, and if the insulation failure verification and the breakdown conduction verification are passed at the same time, a fault instruction is generated and sent to a test terminal.

[0013] According to one preferred embodiment, the infrared thermal imager is a device with temperature field imaging function, which includes an infrared thermal imaging camera and a high-precision temperature sensor.

[0014] According to one preferred embodiment, the fault simulation cloud platform establishes an operation topology model and a reference impedance model of the photovoltaic cable according to the infrared thermal distribution map and the photovoltaic array topology map respectively, which includes:

[0015] The fault simulation cloud platform extracts material attribute parameters of all cable sections in the infrared thermal distribution map, obtains node equivalent impedances of all cable sections in the infrared thermal distribution map, and then obtains current phase angles of all cable sections in the infrared thermal distribution map;

[0016] The fault simulation cloud platform establishes an operating topology model of the photovoltaic cable based on the material property parameters, node equivalent impedance, and current phase angle of all cable sections. The operating topology model is represented in the form of triple data, including: real-time material properties, real-time node impedance, and real-time phase angle.

[0017] The fault simulation cloud platform analyzes the reference material property parameters of all cable sections in the photovoltaic array topology diagram, obtains the reference node equivalent impedance of all cable sections in the photovoltaic array topology diagram, and then obtains the reference current phase angle of all cable sections in the photovoltaic array topology diagram;

[0018] The fault simulation cloud platform establishes a baseline impedance model for photovoltaic cables based on the baseline material property parameters, baseline node equivalent impedance, and baseline current phase angle of all cable sections. The baseline impedance model is represented in the form of triplet data, including: baseline material properties, baseline node impedance, and baseline phase angle.

[0019] According to a preferred embodiment, the fault simulation cloud platform performs multi-parameter aggregation analysis by running a topology model and a reference impedance model to identify fault characteristic sections in photovoltaic cables, including:

[0020] Extract the material property characteristics, impedance distribution characteristics and phase change characteristics of each cable segment in the operating topology model, and perform feature aggregation on the material property characteristics, impedance distribution characteristics and phase change characteristics of each cable segment in the operating topology model to obtain a multi-field state feature tensor for each cable segment in the operating topology model;

[0021] Mapping the multi-field characteristic tensors of each cable section in the running topology model to the cable state space to generate several dynamic response points;

[0022] Extracting the material property characteristics, impedance distribution characteristics, and phase change characteristics of each cable segment in the benchmark impedance model, and performing feature aggregation on the material property characteristics, impedance distribution characteristics, and phase change characteristics of each cable segment in the benchmark impedance model to obtain a multi-field state feature tensor for each cable segment in the benchmark impedance model;

[0023] The multi-field state characteristic tensor of each cable section in the benchmark impedance model is mapped to the cable state space to generate several reference characteristic zero points.

[0024] According to a preferred embodiment, the fault simulation cloud platform performs multi-parameter aggregation analysis by running a topology model and a reference impedance model to identify fault characteristic sections in photovoltaic cables, including:

[0025] Randomly select a dynamic response point as the target response point, calculate the transient cross-correlation index between the target response point and each other dynamic response point, connect the target response point with the other dynamic response points in descending order of transient cross-correlation index, and generate a dynamic coupling trajectory starting from the target response point; repeat this step until a dynamic coupling trajectory starting from each dynamic response point is generated;

[0026] A reference feature zero point is randomly selected as the target feature zero point, and the transient cross-correlation index between the target feature zero point and each other reference feature zero point is calculated. The target feature zero point and the other reference feature zero points are connected in descending order of the transient cross-correlation index to generate a benchmark coupling trajectory starting from the target feature zero point. This step is repeated until a benchmark coupling trajectory starting from each reference feature zero point is generated.

[0027] According to a preferred embodiment, the fault simulation cloud platform performs multi-parameter aggregation analysis by running a topology model and a reference impedance model to identify fault characteristic sections in photovoltaic cables, including:

[0028] Step 1: randomly select a dynamic response point as the target response point, obtain a dynamic coupling trajectory starting from the target response point, and use the reference characteristic zero point corresponding to the target response point as the target characteristic zero point to obtain a reference coupling trajectory starting from the target characteristic zero point;

[0029] Step 2: extracting the trajectory features of the dynamic coupling trajectory and the reference coupling trajectory, and calculating the feature similarity between the trajectory features of the dynamic coupling trajectory and the trajectory features of the reference coupling trajectory. When the feature similarity is less than a similarity threshold, the dynamic coupling trajectory is marked as an abnormal coupling trajectory.

[0030] Step 3: Repeat steps 1 to 2 until all dynamic response points are traversed to obtain multiple abnormal coupling trajectories; the intersection of the abnormal coupling trajectories is used as the abnormal coupling point;

[0031] Step 4: locate the dynamic response point closest to each abnormal coupling point in the cable state space and use it as the abnormal response point. Mark the cable section corresponding to the abnormal response point as the fault characteristic section.

[0032] According to a preferred embodiment, the multi-field coupling degradation value of the waveguide mode characteristic point is calculated using the following formula

[0033]

[0034] Where D is the multi-field coupling degradation value, Z real is the real-time node impedance, Z ref is the reference node impedance, T max is the maximum temperature of the material, T minambient temperature, temperature rise gradient, theta nom reference phase angle, ||theta|| is the offset of the real-time phase angle relative to the reference phase angle, alpha, beta, and gamma are weight coefficients.

[0035] According to a preferred embodiment, the insulation failure verification process comprises: applying a pulse voltage at the physical failure point, and detecting the partial discharge amount of the physical failure point; determining whether the positions of the physical failure point and the TDR open point coincide within an allowable error range; if the positions of the physical failure point and the TDR open point coincide within the allowable error range and the partial discharge amount of the physical failure point is greater than the discharge amount threshold, the insulation failure verification is passed.

[0036] The breakdown conduction verification process comprises: applying a working voltage at the dielectric failure point, and detecting the leakage current of the dielectric failure point; determining whether the positions of the leakage current peak point and the dielectric failure point coincide within an allowable error range; if the positions of the leakage current peak point and the dielectric failure point coincide within the allowable error range and the leakage current is greater than the leakage current threshold, the breakdown conduction verification is passed.

[0037] According to a preferred embodiment, the formula for calculating the transient cross-correlation index is:

[0038]

[0039] wherein C ij is the transient cross-correlation index of dynamic response point i and dynamic response point j, z i is the impedance sequence of dynamic response point i, z j is the impedance sequence of dynamic response point j, is the real-time phase angle of dynamic response point i, is the real-time phase angle of dynamic response point j.

[0040] The present application has the following beneficial effects:

[0041] 1. By fusing the electrical characteristics of infrared thermal field distribution and sweep frequency impedance spectrum, combining dynamic modeling of material properties, realizing electromagnetic-thermal-mechanical multi-field coupling analysis, breaking through the limitations of traditional single-parameter detection, accurately identifying hidden faults such as conductor fracture and insulation deterioration, and effectively solving the misdiagnosis and missed judgment problems of photovoltaic cables caused by multi-factor coupling.

[0042] 2. By impedance-capacitance space mapping and deterioration value sorting connection, intuitively presenting the evolution trend of the fault along the cable path, converting abstract faults into locatable dielectric failure points and physical failure points, and improving the spatial positioning accuracy of fault points in complex networks.

[0043] 3. Through the double-checking process of insulation failure verification and breakdown conduction verification, through the cooperation of impulse voltage test and working voltage test, and in association with the spatial coincidence criterion of TDR open point and leakage current peak point, a closed-loop logic chain of fault diagnosis is formed, which verifies from the dual dimensions of electrical characteristics and physical location to reduce the risk of false positives. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A flowchart of a photovoltaic cable fault simulation test method is provided for an exemplary embodiment. DETAILED DESCRIPTION

[0045] 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 elements throughout the drawings. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present invention. Instead, they are merely examples of apparatuses and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0046] The terminology used in the present invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the present invention. As used in the present invention and the appended claims, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "and / or," as used herein, refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0047] It will be understood that, although the terms first, second, third, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used only to distinguish one piece of information from another. For example, a first piece of information could later be referred to as a second piece of information without departing from the scope of the present invention. As used herein, the word "if' can be interpreted to mean "when" or "upon" or "in response to determining" taking into account the context in which the term is used.

[0048] Referring to Figure 1 A photovoltaic cable fault simulation test method is provided in the present invention, which includes:

[0049] S1, the test terminal sends a photovoltaic array topology, a multi-channel sweep instruction and a thermal field acquisition instruction to the fault simulation cloud platform, the multi-channel tester and the infrared thermal imager respectively; the infrared thermal imager acquires the infrared thermal distribution of the photovoltaic cable in response to the received thermal field acquisition instruction and sends it to the fault simulation cloud platform; the multi-channel tester acquires the impedance spectrum response data of the photovoltaic cable in response to the received multi-channel sweep instruction and sends it to the fault simulation cloud platform.

[0050] Optionally, the infrared thermal imager is a device with temperature field imaging function, comprising an infrared thermal imaging camera and a high-precision temperature sensor.

[0051] Optionally, the photovoltaic array topology map is a digital model describing the electrical connection and spatial layout of the photovoltaic power station, representing the physical location and logical association relationship of components, cables and electrical nodes.

[0052] Optionally, the multi-channel tester is an electronic measuring device for photovoltaic cable fault detection, which obtains the impedance spectrum characteristics of the cable network in the wide frequency domain through synchronous excitation and response collection.

[0053] Optionally, the impedance spectrum response data is a set of electrical response data of the cable network in the frequency domain collected by the multi-channel tester under the sweep excitation, representing the impedance characteristic change rule of the photovoltaic cable at different frequencies, composed of three parts: amplitude spectrum, impedance modulus sequence corresponding to the frequency point; phase spectrum, voltage-current phase difference curve with frequency; complex impedance matrix, frequency domain distribution of real part resistance component and imaginary part reactance component.

[0054] Optionally, the infrared thermal distribution map is a visual map of the spatial distribution of the surface temperature field of the photovoltaic cable obtained by infrared thermal imaging technology, representing the temperature difference abnormal phenomenon of each part of the cable caused by current heat effect, poor contact or insulation defect. Composed of three parts: temperature data layer, absolute temperature value of each pixel point, highest / lowest temperature coordinate marker; thermal field gradient layer, temperature rise rate vector isotherm distribution topology; material mapping layer, different material area boundary, thermal conductivity abnormal area annotation, etc.

[0055] S2, the fault simulation cloud platform establishes the operation topology model and the reference impedance model of the photovoltaic cable according to the infrared thermal distribution map and the photovoltaic array topology map respectively, and performs multi-parameter aggregation analysis through the operation topology model and the reference impedance model to identify the fault feature section in the photovoltaic cable.

[0056] Optionally, the fault feature section is a cable section with insulation degradation or conductor breakage.

[0057] Optionally, the cable section includes a start cable node, an end cable node, and a connecting cable between the start cable node and the end cable node.

[0058] In a preferred embodiment, the fault simulation cloud platform establishes the operation topology model and the reference impedance model of the photovoltaic cable according to the infrared thermal distribution map and the photovoltaic array topology map respectively, comprising:

[0059] The fault simulation cloud platform extracts material attribute parameters of all cable sections in the infrared thermal distribution map, obtains node equivalent impedance of all cable sections in the infrared thermal distribution map, and then obtains current phase angles of all cable sections in the infrared thermal distribution map;

[0060] The fault simulation cloud platform establishes an operating topology model of the photovoltaic cable according to the material attribute parameters, the node equivalent impedance, and the current phase angles of all the cable sections; the operating topology model is expressed in the form of a triple data set, including real-time material attributes, real-time node impedance, and real-time phase angles;

[0061] The fault simulation cloud platform analyzes reference material attribute parameters of all cable sections in the photovoltaic array topology map, obtains reference node equivalent impedance of all cable sections in the photovoltaic array topology map, and then obtains reference current phase angles of all cable sections in the photovoltaic array topology map;

[0062] The fault simulation cloud platform establishes a reference impedance model of the photovoltaic cable according to the reference material attribute parameters, the reference node equivalent impedance, and the reference current phase angles of all the cable sections; the reference impedance model is expressed in the form of a triple data set, including reference material attributes, reference node impedance, and reference phase angles.

[0063] Optionally, the operating topology model is a cable electrical-thermal state model constructed based on real-time monitoring data, and the operating topology model includes real-time material attributes, real-time node impedance, and real-time phase angles; the real-time material attributes include thermal conductivity of an insulation layer and temperature coefficient of resistance of a conductor, which are identified from the infrared thermal distribution map; the real-time node impedance includes impedance values of each cable node at a sweep frequency point; and the real-time phase angles include node voltage-current phase differences.

[0064] Optionally, the reference impedance model is an ideal cable model in a design state of the photovoltaic array. The reference impedance model is composed of reference material attributes, reference node impedance, and reference phase angles. The reference material attributes are standard insulation material parameters, the reference node impedance is a nominal impedance, and the reference phase angles are theoretical phase angles, which are usually 0.

[0065] In a preferred embodiment, the fault simulation cloud platform performs multi-parameter aggregation analysis on the operating topology model and the reference impedance model to identify a fault feature section in the photovoltaic cable, including:

[0066] Material attribute features, impedance distribution features, and phase change features of each cable section in the operating topology model are extracted, and the material attribute features, the impedance distribution features, and the phase change features of each cable section in the operating topology model are aggregated to obtain a multi-state feature tensor of each cable section in the operating topology model;

[0067] The multi-state feature tensor of each cable section in the operating topology model is mapped to a cable state space to generate a plurality of dynamic response points.

[0068] extracting the material attribute feature, the impedance distribution feature and the phase change feature of each cable section in the reference impedance model, and performing feature aggregation on the material attribute feature, the impedance distribution feature and the phase change feature of each cable section in the reference impedance model, to obtain a multi-state feature tensor of each cable section in the reference impedance model;

[0069] mapping the multi-state feature tensor of each cable section in the reference impedance model to a cable state space to generate a plurality of reference feature zero points.

[0070] Optionally, the multi-state feature tensor is a multi-dimensional feature matrix fused in the electrical / thermal / mechanical field, F = [material attribute feature, impedance distribution feature, phase change feature], wherein the material attribute feature includes thermal conductivity and resistance temperature coefficient; the impedance distribution feature includes a sweep frequency impedance sequence; and the phase change feature includes a phase change rate and a phase fluctuation standard deviation.

[0071] Optionally, the dynamic response point is a real-time cable state representation point obtained by mapping the multi-state feature tensor of the topology model, and represents a mathematical projection of a current cable health state; and the reference feature zero point is an ideal cable reference point obtained by mapping the multi-state feature tensor of the reference model, and represents a theoretical stable point when the cable is fault-free.

[0072] In a preferred embodiment, the fault simulation cloud platform identifies the fault feature section in the photovoltaic cable by performing multi-parameter aggregation analysis on the topology model and the reference impedance model, and includes:

[0073] randomly selecting one dynamic response point as a target response point, calculating a transient cross-correlation index of the target response point with respect to each of the other dynamic response points, connecting the target response point with the other dynamic response points in descending order of the transient cross-correlation index to generate a dynamic coupling trajectory starting from the target response point; and repeating the step until a dynamic coupling trajectory starting from each of the dynamic response points is generated.

[0074] randomly selecting one reference feature zero point as a target feature zero point, calculating a transient cross-correlation index of the target feature zero point with respect to each of the other reference feature zero points, connecting the target feature zero point with the other reference feature zero points in descending order of the transient cross-correlation index to generate a reference coupling trajectory starting from the target feature zero point; and repeating the step until a reference coupling trajectory starting from each of the reference feature zero points is generated.

[0075] Optionally, the transient cross-correlation index is an index for quantifying the dynamic correlation strength between the dynamic response points, and is used to evaluate the cooperative state of the cable section by comprehensively evaluating the impedance change synchronicity and the phase consistency, and is used to identify abnormal electrical coupling relationship. The greater the transient cross-correlation index, the stronger the correlation between the two dynamic response points.

[0076] Preferably, the formula for calculating the transient cross-correlation index is:

[0077]

[0078] wherein C ij is the transient cross-correlation index of dynamic response point i and dynamic response point j, z i is the impedance sequence of dynamic response point i, z j is the impedance sequence of dynamic response point j, is the real-time phase angle of dynamic response point i, is the real-time phase angle of dynamic response point j.

[0079] In a preferred embodiment, the fault simulation cloud platform identifies the fault feature section in the photovoltaic cable by running the topology model and the reference impedance model for multi-parameter aggregation analysis includes:

[0080] Step one, randomly select a dynamic response point as a target response point, obtain a dynamic coupling trajectory starting from the target response point, and take the reference feature zero point corresponding to the target response point as a target feature zero point, and obtain a reference coupling trajectory starting from the target feature zero point;

[0081] Optionally, each dynamic response point has a reference feature zero point corresponding thereto, and both correspond to the same cable section. The dynamic response point is used to represent the actual cable state of the cable section, and the reference feature zero point is used to represent the ideal cable state of the cable section.

[0082] For example, dynamic response point A and reference feature zero point A are used to represent the actual cable state and the ideal cable state of cable section A, respectively. Dynamic response point A, reference feature zero point A, and cable section A correspond to each other uniquely.

[0083] Step two, extract the trajectory features of the dynamic coupling trajectory and the reference coupling trajectory, and calculate the feature similarity of the trajectory features of the dynamic coupling trajectory and the reference coupling trajectory, and mark the dynamic coupling trajectory as an abnormal coupling trajectory when the feature similarity is less than a similarity threshold;

[0084] Step three, repeat steps one to two until all dynamic response points are traversed, and obtain a plurality of abnormal coupling trajectories; take the intersection between the abnormal coupling trajectories as an abnormal coupling point;

[0085] Step four, locate the dynamic response point closest to each abnormal coupling point in the cable state space, and take it as an abnormal response point, and mark the cable section corresponding to the abnormal response point as a fault feature section.

[0086] S3, the fault simulation cloud platform takes the fault feature section and the cable nodes directly electrically connected with the start cable node and the end cable node of the fault feature section as a fault coupling area, and extracts a plurality of fault coupling loops according to the fault coupling area.

[0087] Optionally, the fault coupling loop is a closed current path containing the fault feature section.

[0088] The start cable node, the end cable node of the fault feature section and the adjacent cable nodes directly electrically connected with the start cable node and the end cable node form a fault coupling area.

[0089] S4, the fault simulation cloud platform maps all cable nodes of the fault coupling loop to an impedance-capacitance space to obtain a plurality of waveguide modal feature points, calculates a multi-field coupling degradation value of each waveguide modal feature point, and sequentially connects all waveguide modal feature points according to the multi-field coupling degradation value from small to large to generate a fault diffusion trajectory corresponding to the fault coupling loop.

[0090] Optionally, the multi-field coupling degradation value is used as a numerical index for quantifying the comprehensive degradation degree of the cable node, and the synergistic degradation effect caused by the fault is characterized by fusing the electric-thermal-phase multi-physical field deviation.

[0091] Preferably, the multi-field coupling degradation value of the waveguide modal feature point is calculated by the following formula

[0092]

[0093] Wherein, D is the multi-field coupling degradation value, Z real is the real-time node impedance, Z ref is the reference node impedance, T max is the maximum material temperature, T min is the ambient temperature, is the temperature rise gradient, θ nom is the reference phase angle, ‖θ‖ is the deviation of the real-time phase angle relative to the reference phase angle, α, β, γ are weight coefficients.

[0094] S5, the fault simulation cloud platform obtains the modal wave valley point and the modal wave peak point of the fault diffusion trajectory, takes the cable position corresponding to the modal wave valley point as a dielectric failure point, and takes the cable position corresponding to the modal wave peak point as a physical failure point; the fault simulation cloud platform analyzes the impedance spectrum response data to obtain the leakage current peak point and the TDR open circuit point of the photovoltaic cable.

[0095] Optionally, the dielectric failure point is a critical position where the electric field breakdown of the cable insulation medium occurs; the physical failure point is a physical position where the conductor mechanical structure is broken or the contact failure occurs; the dielectric failure point focuses on the collapse of insulation performance, and the physical failure point focuses on the conductor physical breakage, which together constitute the core physical representation of the cable fault.

[0096] Optionally, the modal trough point is an impedance minimum point in the fault diffusion trajectory dominated by the capacitive effect, reflecting the abnormal increase of the dielectric constant of the cable section insulation medium, corresponding to the most serious position of insulation deterioration. The modal peak point is an impedance maximum point in the fault diffusion trajectory dominated by the inductive effect, representing the inductance mutation caused by the physical interruption of the conductor, such as the dramatic increase of the magnetic flux caused by the copper core breakage, accurately pointing to the conductor breakage / contact failure point.

[0097] S6, the fault simulation cloud platform performs insulation failure verification according to the physical failure point and the TDR open point, and performs breakdown conduction verification according to the dielectric failure point and the leakage current peak point, and if the insulation failure verification and the breakdown conduction verification are passed at the same time, a fault instruction is generated and sent to the test terminal.

[0098] Preferably, the insulation failure verification process comprises: applying a pulse voltage at the physical failure point, and detecting the partial discharge amount of the physical failure point; determining whether the positions of the physical failure point and the TDR open point coincide within an allowable error range; if the positions of the physical failure point and the TDR open point coincide within the allowable error range and the partial discharge amount of the physical failure point is greater than a discharge amount threshold, the insulation failure verification is passed.

[0099] Preferably, the breakdown conduction verification process comprises: applying a working voltage at the dielectric failure point, and detecting the leakage current of the dielectric failure point; determining whether the positions of the leakage current peak point and the dielectric failure point coincide within an allowable error range; if the positions of the leakage current peak point and the dielectric failure point coincide within the allowable error range and the leakage current is greater than a leakage current threshold, the breakdown conduction verification is passed.

[0100] Optionally, the TDR open point represents the electrical positioning point of the conductor physical interruption, reflecting the impedance mutation caused by the cable breakage or contact failure, which is obtained by time domain reflection detection.

[0101] Optionally, the leakage current peak point is a leakage current mutation position when the insulation medium is close to breakdown, representing a precursor of the formation of a partial discharge channel.

[0102] The application realizes electromagnetic-thermal-mechanical multi-field coupling analysis by fusing the electrical characteristics of infrared thermal field distribution and sweep frequency impedance spectrum, combining dynamic modeling of material properties, breaking through the limitation of traditional single parameter detection, accurately identifying hidden faults such as conductor fracture and insulation deterioration, and effectively solving the misdiagnosis and missed judgment problems of photovoltaic cables caused by multi-factor coupling; through impedance-capacitance space mapping and deterioration value sorting connection, the evolution trend of the fault along the cable path is intuitively presented, the abstract fault is converted into a locatable dielectric failure point and a physical failure point, and the spatial positioning accuracy of the fault point in the complex network is improved; through the double verification process of insulation failure verification and breakdown conduction verification, through the cooperation of pulse voltage test and working voltage test, and in combination with the spatial coincidence criterion of TDR open point and leakage current peak point, a closed-loop logic chain of fault diagnosis is formed, and the false alarm risk is reduced through the cross verification of electrical characteristics and physical location from two dimensions.

[0103] Computer program instructions for carrying out operations of the present application can be in assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and a procedural programming language such as the "C" language or the like. Computer readable program instructions can be executed completely on a user computer, partially on a user computer, as an independent software package, partially on a user computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet by using an Internet service provider). In some embodiments, by utilizing the state information of computer readable program instructions to individualize customize electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGA) or programmable logic arrays (PLA), the electronic circuits can execute computer readable program instructions, thereby realizing various aspects of the present application.

[0104] The non-transitory computer readable storage medium described in the application stores computer instructions, and when the computer instructions are executed by a processor, the processor executes the above method.

[0105] Those skilled in the art can understand that all or part of the steps of the above method can be instructed by a program to related hardware (for example, a processor, an FPGA, an ASIC, etc.), and the program can be stored in a readable storage medium, such as a read-only memory, a magnetic disk or an optical disk, etc. All or part of the steps of the above embodiments can also be implemented by using one or more integrated circuits. Accordingly, each module in the above embodiments can be implemented in the form of hardware, for example, by using an integrated circuit to implement its corresponding function, or can be implemented in the form of a software function module, for example, by using a processor to execute a program / instruction stored in a memory to implement its corresponding function. The embodiments of the present application are not limited to any specific form of combination of hardware and software.

[0106] In addition, each functional unit in each of the embodiments herein can be integrated in one processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0107] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such an understanding, the technical solutions herein, essentially or in the form of a contribution to the prior art, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments herein. The foregoing storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, etc.

[0108] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the scope of protection of the present application.

Claims

1. A photovoltaic cable fault simulation test method, characterized in that: The following steps are involved: The test terminal sends the photovoltaic array topology diagram, multi-channel frequency sweep instructions, and thermal field acquisition instructions to the fault simulation cloud platform, multi-channel tester, and infrared thermal imager respectively; The infrared thermal imager collects the infrared heat distribution map of the photovoltaic cable in response to the received thermal field acquisition instruction and sends it to the fault simulation cloud platform; The multi-channel tester collects impedance spectrum response data of the photovoltaic cable in response to the received multi-channel frequency sweep instruction and sends the data to the fault simulation cloud platform; The fault simulation cloud platform establishes an operating topology model and a baseline impedance model of the photovoltaic cable based on the infrared heat distribution map and the photovoltaic array topology map, respectively. It then uses the operating topology model and the baseline impedance model to perform multi-parameter aggregation analysis to identify fault characteristic sections in the photovoltaic cable. The fault characteristic section is a cable section where insulation degradation or conductor breakage occurs; The fault simulation cloud platform uses the fault characteristic section and the cable nodes directly electrically connected to the starting cable node and the ending cable node of the fault characteristic section as the fault coupling area, and extracts several fault coupling loops based on the fault coupling area; The fault coupling loop is a closed current path including a fault characteristic section; The fault simulation cloud platform maps all cable nodes in the fault coupling loop into the impedance-capacitance space to obtain several waveguide modal feature points. It then calculates the multi-field coupling degradation value for each waveguide modal feature point and connects all waveguide modal feature points in ascending order of multi-field coupling degradation value to generate the fault diffusion trajectory corresponding to the fault coupling loop. The fault simulation cloud platform obtains the modal trough and modal peak points of the fault diffusion trajectory, and uses the cable position corresponding to the modal trough point as the dielectric failure point, and the cable position corresponding to the modal peak point as the physical failure point; The fault simulation cloud platform analyzes the impedance spectrum response data to obtain the leakage current peak point and TDR open circuit point of the photovoltaic cable; The fault simulation cloud platform performs insulation failure verification based on the physical failure point and TDR open circuit point, and performs breakdown continuity verification based on the dielectric failure point and leakage current peak point. If both the insulation failure verification and breakdown continuity verification are passed, a fault instruction is generated and sent to the test terminal.

2. The fault simulation test method according to claim 1, characterized in that: The infrared thermal imager is a device with temperature field imaging function, including: an infrared thermal imaging camera and a high-precision temperature measurement sensor.

3. The fault simulation test method according to claim 2, characterized in that: The fault simulation cloud platform establishes the operation topology model and benchmark impedance model of the photovoltaic cable based on the infrared heat distribution map and the photovoltaic array topology map, including: The fault simulation cloud platform extracts the material property parameters of all cable sections in the infrared thermal distribution map, obtains the node equivalent impedance of all cable sections in the infrared thermal distribution map, and then obtains the current phase angle of all cable sections in the infrared thermal distribution map; The fault simulation cloud platform establishes an operating topology model of the photovoltaic cable based on the material property parameters, node equivalent impedance, and current phase angle of all cable sections. The operating topology model is represented in the form of triple data, including: real-time material properties, real-time node impedance, and real-time phase angle. The fault simulation cloud platform analyzes the reference material property parameters of all cable sections in the photovoltaic array topology diagram, obtains the reference node equivalent impedance of all cable sections in the photovoltaic array topology diagram, and then obtains the reference current phase angle of all cable sections in the photovoltaic array topology diagram; The fault simulation cloud platform establishes a baseline impedance model for photovoltaic cables based on the baseline material property parameters, baseline node equivalent impedance, and baseline current phase angle of all cable sections. The baseline impedance model is represented in the form of triplet data, including: baseline material properties, baseline node impedance, and baseline phase angle.

4. The fault simulation test method according to claim 3, characterized in that: The fault simulation cloud platform performs multi-parameter aggregation analysis by running topology models and benchmark impedance models to identify fault characteristic sections in photovoltaic cables, including: Extract the material property characteristics, impedance distribution characteristics and phase change characteristics of each cable segment in the operating topology model, and perform feature aggregation on the material property characteristics, impedance distribution characteristics and phase change characteristics of each cable segment in the operating topology model to obtain a multi-field state feature tensor for each cable segment in the operating topology model; Mapping the multi-field characteristic tensors of each cable section in the running topology model to the cable state space to generate several dynamic response points; Extracting the material property characteristics, impedance distribution characteristics, and phase change characteristics of each cable segment in the benchmark impedance model, and performing feature aggregation on the material property characteristics, impedance distribution characteristics, and phase change characteristics of each cable segment in the benchmark impedance model to obtain a multi-field state feature tensor for each cable segment in the benchmark impedance model; The multi-field state characteristic tensor of each cable section in the benchmark impedance model is mapped to the cable state space to generate several reference characteristic zero points.

5. The fault simulation test method according to claim 4, characterized in that: The fault simulation cloud platform performs multi-parameter aggregation analysis by running topology models and benchmark impedance models to identify fault characteristic sections in photovoltaic cables, including: Randomly select a dynamic response point as the target response point, calculate the transient cross-correlation index between the target response point and each other dynamic response point, connect the target response point with the other dynamic response points in descending order of transient cross-correlation index, and generate a dynamic coupling trajectory starting from the target response point; repeat this step until a dynamic coupling trajectory starting from each dynamic response point is generated; A reference feature zero point is randomly selected as the target feature zero point, and the transient cross-correlation index between the target feature zero point and each other reference feature zero point is calculated. The target feature zero point and the other reference feature zero points are connected in descending order of the transient cross-correlation index to generate a benchmark coupling trajectory starting from the target feature zero point. This step is repeated until a benchmark coupling trajectory starting from each reference feature zero point is generated.

6. The method according to claim 5, characterized in that The fault simulation cloud platform performs multi-parameter aggregation analysis by running topology models and benchmark impedance models to identify fault characteristic sections in photovoltaic cables, including: Step 1: randomly select a dynamic response point as the target response point, obtain a dynamic coupling trajectory starting from the target response point, and use the reference characteristic zero point corresponding to the target response point as the target characteristic zero point to obtain a reference coupling trajectory starting from the target characteristic zero point; Step 2: extracting the trajectory features of the dynamic coupling trajectory and the reference coupling trajectory, and calculating the feature similarity between the trajectory features of the dynamic coupling trajectory and the trajectory features of the reference coupling trajectory. When the feature similarity is less than a similarity threshold, the dynamic coupling trajectory is marked as an abnormal coupling trajectory. Step 3: Repeat steps 1 to 2 until all dynamic response points are traversed to obtain multiple abnormal coupling trajectories; the intersection of the abnormal coupling trajectories is used as the abnormal coupling point; Step 4: locate the dynamic response point closest to each abnormal coupling point in the cable state space and use it as the abnormal response point. Mark the cable section corresponding to the abnormal response point as the fault characteristic section.

7. The fault simulation test method according to claim 6, characterized in that: The multi-field coupling degradation value of the waveguide mode characteristic point is calculated using the following formula Where D is the multi-field coupling degradation value, Z real is the real-time node impedance, Z ref is the reference node impedance, T max is the maximum temperature of the material, T min is the ambient temperature, is the temperature rise gradient, θ nom is the reference phase angle, ‖θ‖ is the offset of the real-time phase angle relative to the reference phase angle, and α, β, and γ are weight coefficients.

8. The fault simulation test method according to claim 7, characterized in that: The insulation failure verification process includes: applying a pulse voltage at the physical failure point and detecting the partial discharge amount at the physical failure point; determining whether the location of the physical failure point coincides with the TDR open point within the allowable error range; if the location of the physical failure point coincides with the TDR open point within the allowable error range and the partial discharge amount at the physical failure point is greater than the discharge amount threshold, the insulation failure verification is passed; The breakdown conduction verification process includes: applying an operating voltage at the dielectric failure point and detecting the leakage current at the dielectric failure point; judging whether the position of the leakage current peak point coincides with the position of the dielectric failure point within the allowable error range; if the position of the leakage current peak point coincides with the position of the dielectric failure point within the allowable error range and the leakage current is greater than the leakage current threshold, the breakdown conduction verification is passed.

9. The fault simulation test method according to claim 8, characterized in that: The calculation formula of the transient cross-correlation index is: Among them, C ij is the transient cross-correlation index between dynamic response point i and dynamic response point j, z i is the impedance sequence of the dynamic response point i, z j is the impedance sequence of the dynamic response point j, is the real-time phase angle of the dynamic response point i, The real-time phase angle of dynamic response point j.

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