CN-121978477-A - Photovoltaic power generation system fault identification method based on parallel arc fault constraint

CN121978477ACN 121978477 ACN121978477 ACN 121978477ACN-121978477-A

Abstract

The invention discloses a photovoltaic power generation system fault identification method based on parallel arc fault constraint, which relates to the technical field of photovoltaic power generation and comprises the steps of analyzing external characteristics of a photovoltaic cell under parallel arc fault, comprehensively considering voltage and current characteristics of a parallel arc and output characteristics of the photovoltaic cell according to the external characteristics of the photovoltaic cell, establishing a parallel arc constraint system model and control characteristics, and obtaining a parallel arc fault identification result according to the parallel arc constraint system model and the control characteristics. The parallel arc fault detection device can rapidly and accurately detect and identify the parallel arc fault detection result.

Inventors

CHEN KAILONG
ZHAO SHUAISHUAI
HE HUAWEI
ZOU YU
LI PEIYI

Assignees

南京国电南自电网自动化有限公司

Dates

Publication Date: 20260505
Application Date: 20260123

Claims (8)

1. The utility model provides a photovoltaic power generation system fault identification method of parallel arc fault constraint which is characterized in that the method comprises the following steps: Analyzing the external characteristics of the photovoltaic cells under parallel arc faults; according to the external characteristics of the photovoltaic cells, comprehensively considering the voltage and current characteristics of the parallel arcs and the output characteristics of the photovoltaic cells, and establishing a parallel arc constraint system model and control characteristics; And obtaining a parallel arc fault identification result according to the parallel arc constraint system model and the control characteristics.
2. The method for identifying the photovoltaic power generation system fault constrained by the parallel arc fault according to claim 1 is characterized in that the analysis process of the photovoltaic cell external characteristics under the parallel arc fault is that when the parallel arc fault occurs in a photovoltaic bus, the current branch generated by the parallel arc influences the overall external characteristics of the photovoltaic cell, the distance between two poles of the parallel arc is reduced, the power loss is increased, the output power of the photovoltaic parallel arc system is gradually reduced, and the photovoltaic cell external characteristics under the arc fault are expressed as: ; wherein: The current excited in the photovoltaic cell for photons, the value of which depends on the degree of illumination, the panel area and the temperature; And Equivalent parallel and series resistances of the photovoltaic cells; Is the saturation current in the absence of illumination; Is the charge of electrons with the size of K is Boltzmann constant, and the size is A is a constant factor, the positive bias voltage is 1 when the positive bias voltage is large, and 2;T when the positive bias voltage is small is the temperature of the photovoltaic cell; in order to be a fault clearance current, And Load current and voltage, respectively.
3. The method for identifying the faults of the parallel arc fault constraint photovoltaic power generation system according to claim 1, wherein the established parallel arc constraint system model and the control feature are classified according to the distance between the two poles of the arc, and the control feature is obtained according to the distance between the two poles of the arc.
4. A method for fault identification of a parallel arc fault constrained photovoltaic power generation system as claimed in claim 3, wherein the arc dipole spacing is Classification includes three cases; Case one: when the MPPT current of the photovoltaic array is larger than the arc current transmission capacity, the light Fu Muxian controls the MPPT voltage through the Boost converter, in the formula, For the critical arc spacing at which the line parallel arcs occur, Critical arc spacing for the bus current to flow entirely into the arc branches; input voltage of Boost converter The control characteristics are as follows: ; the input current control is characterized in that the MPPT current of the photovoltaic is larger than the current transmission capacity of the arc, the photovoltaic cell operates in an MPPT mode, and the input current of the Boost converter is the same The method comprises the following steps: ; The power control is characterized in that the input power of a Boost converter Power loss with parallel arc The method comprises the following steps of: ; In the formula, Representing the transfer limit of the arc current at a fixed two-stage spacing, And Representing the current and voltage of the photovoltaic array at the maximum power point; And a second case: When the arc current transfer capability is between the MPPT current of the photovoltaic array and the maximum current of the photovoltaic array, in the formula, A critical arc spacing for the bus voltage can be maintained for Boost, i.e., the bus voltage is arc-constrained below this spacing; The input voltage of the Boost converter is characterized by: = ; The input current of the Boost converter is characterized by: ; the power control is characterized in that the generated power of the photovoltaic array completely flows into the parallel arc branch circuit; And a third case: when the arc current transfer capability is greater than the maximum current of the photovoltaic array; The input voltage of the Boost converter is characterized in that the arc burns violently at the moment, and the voltage amplitude of the input Boost is lower than 30%; the input current of the Boost converter is characterized in that the current flows into the Boost converter to be close to 0 due to the short circuit formed by the electric arc due to the intense combustion of the electric arc; the power control feature is that the Boost converter is no longer able to draw power.
5. The parallel arc fault constrained photovoltaic power generation system fault identification method of claim 4, wherein the parallel arc fault identification process is: Firstly, obtaining output voltage and output current under Boost-MPPT control, judging whether the output current is reduced or not when the output voltage and the output current are not 0, if the output voltage is not reduced, then no fault exists, the Boost normally operates, if the output current is reduced, continuously judging whether the output voltage is reduced, if the output voltage is not reduced, then the first case of parallel arc is the case of adopting Boost-MPPT control to restore to a steady state, and otherwise, the second case and the third case of parallel arc are the case of adopting Boost-MPPT control to carry out arc extinction control.
6. A parallel arc fault constrained photovoltaic power generation system fault identification device, comprising: the photovoltaic cell external characteristic analysis module is used for analyzing the photovoltaic cell external characteristics under the parallel arc faults; the fault identification module is used for comprehensively considering the voltage and current characteristics of the parallel arc and the output characteristics of the photovoltaic cell according to the external characteristics of the photovoltaic cell and establishing a parallel arc constraint system model and control characteristics; And obtaining a parallel arc fault identification result according to the parallel arc constraint system model and the control characteristics.
7. A computer readable storage medium having stored thereon a computer program/instruction, which when executed by a processor, implements the steps of the parallel arc fault constrained photovoltaic power generation system fault identification method of any of claims 1 to 5.
8. A computer apparatus/device/system comprising: A memory for storing computer programs/instructions; A processor for executing the computer program/instructions to implement the steps of the parallel arc fault constrained photovoltaic power generation system fault identification method of any of claims 1 to 5.

Description

Photovoltaic power generation system fault identification method based on parallel arc fault constraint Technical Field The invention relates to a fault identification method of a parallel arc fault constraint photovoltaic power generation system, and belongs to the technical field of photovoltaic power generation. Background With the increasing exhaustion of traditional fossil energy and the continuous deterioration of ecological environment, energy structures of various countries are accelerating the transformation to renewable energy. As a typical representative of clean energy, photovoltaic power generation technology has become a middle-flow column in the new energy field by virtue of abundant resources, no pollution, wide distribution advantages and the like. However, with the popularization of photovoltaic power generation technology, the probability of faults is greatly improved, and in particular, the problem of arc faults is increasingly prominent. The arc fault not only can lead to the reduction of the system efficiency, but also can cause potential safety hazards such as fire disaster and the like, and seriously threatens the stable operation and the personal and property safety of the photovoltaic system. Parallel arc faults are a common type of arc fault, and are particularly dangerous. However, the existing fault detection and identification methods still have a plurality of defects when dealing with parallel arc faults, and are difficult to meet the requirements of practical application. The traditional arc fault detection method mainly relies on transient waveform time domain and frequency domain analysis of Boost input current. However, this method has a poor effect of identifying the parallel arc fault, because the parallel arc fault may only show a slight current change in the early stage, and it is difficult to accurately identify the parallel arc fault by simple time domain and frequency domain judgment. In addition, the photovoltaic power generation system is greatly influenced by external factors such as illumination intensity, ambient temperature and the like, and the factors can cause great fluctuation of current and voltage during normal operation of the system, so that the difficulty of fault identification based on time domain and frequency domain analysis and judgment is further increased. Aiming at the complexity and diversity of parallel arc faults, some scholars propose a fault detection scheme based on multi-sensor fusion. By installing a plurality of sensors, such as a current sensor, a voltage sensor, a temperature sensor and the like, in the photovoltaic system, the operation state of the system is monitored in real time so as to improve the accuracy of fault detection. However, this solution has the problems of high sensor cost, complex installation and high difficulty in data fusion, which limits its application in large-scale photovoltaic systems. In addition, the multi-sensor fusion scheme still has difficulty in realizing rapid and accurate identification of parallel arc faults when facing complex fault scenes. In recent years, with the development of signal processing technology and intelligent algorithms, students detect arc faults based on feature extraction and pattern recognition. However, these methods generally require a large amount of fault sample data for training, and under different types of photovoltaic power generation systems or different operation conditions, fault characteristics may be different, so that the model generalization capability is insufficient, and the method is difficult to widely popularize in practical application. In terms of fault recognition algorithms, students have attempted to classify and identify arc faults using machine learning algorithms, such as support vector machines, neural networks, and the like. Although these algorithms improve the accuracy of fault identification to some extent, they have a high demand for computational resources and are deficient in real-time. Particularly in a large-scale distributed photovoltaic power generation system, the data volume is huge, the difficulty of real-time processing and analysis is extremely high, and the requirement of rapid fault detection is difficult to meet. In addition, a large amount of labeling data is required in the training process of the machine learning algorithm, and obtaining high-quality arc fault labeling data often faces difficulty in practical application, which limits the application effect of the arc fault labeling data in fault identification. In summary, the existing parallel arc fault detection and identification methods still have many limitations in terms of accuracy, real-time, generalization capability, cost effectiveness, and the like. Therefore, the parallel arc fault identification method based on the unified model is urgently needed, the electrical characteristics, the operation environment and the fault characteristics of the photovoltaic power g