Mini-inverter fault detecting method based on neural network expert system

A micro-inverter, neural network technology, applied in biological neural network models, information technology support systems, instruments, etc., can solve problems such as personal safety and equipment safety threats, equipment and personal safety hazards, high voltage transformers, etc. The effect of strengthening self-learning ability, narrowing the detection range and timely maintenance

Inactive Publication Date: 2013-09-11
江西中能电气科技股份有限公司
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Problems solved by technology

[0003] When the photovoltaic system needs maintenance, it is generally necessary to disconnect the system from the grid first. If the photovoltaic module continues to work in this case, the primary side of the transformer will generate a high voltage, which will pose a

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  • Mini-inverter fault detecting method based on neural network expert system
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  • Mini-inverter fault detecting method based on neural network expert system

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Embodiment Construction

[0048] The present invention will be further described below in conjunction with specific embodiments and accompanying drawings, but the protection scope of the present invention should not be limited thereby.

[0049] figure 1 It is a flowchart of a micro-inverter fault detection method based on a neural network expert system in the present invention. As shown in the figure, the fault detection method includes the following steps:

[0050] Step 1: Build an initial knowledge base;

[0051] The data in the initial knowledge base refers to the real-time working data of the micro-inverter obtained through experiments, including: the output voltage and current of the photovoltaic cell module obtained under different ambient light and temperature conditions, the input of the DC side of the micro-inverter Voltage and current, grid-connected voltage and current output by the AC side of the micro-inverter, grid voltage and grid frequency;

[0052] The structure of the micro-inverte...

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Abstract

The invention discloses a mini-inverter fault detecting method based on a neural network expert system. The mini-inverter fault detecting method includes: step 1, building an initial knowledge base, step 2, confirming a neural network topology structure and network parameters, and building an expert system based on the neural network comprising an input layer, a middle layer and an output layer, and step 3, sending data detected in real time to the expert system through a data processing module of the neural network expert system, calling data from the knowledge base by the expert system to compare the data with the data detected in real time, judging whether the system is faulted or not by the aid of speculation and analysis of an inference engine, outputting faulted information, timely detecting fault, providing related handing information and adopting multi-layer inputting topological structure. The mini-inverter fault detecting method is simple, efficient and high in detecting accuracy, so that maintenance staff can fix the fault as soon as possible, and loss and hazard caused by the fault can be prevented.

Description

technical field [0001] The invention relates to a micro-inverter fault detection method based on a neural network expert system. Background technique [0002] Contemporary solar photovoltaic systems generally include: DC photovoltaic panels for generating DC power; micro-inverters for converting DC power into AC power; AC interfaces for receiving AC power from other modules or connecting to the AC grid; communication lines for For transmitting data and control signals, etc. [0003] When the photovoltaic system needs maintenance, it is generally necessary to disconnect the system from the grid first. If the photovoltaic module continues to work in this case, the primary side of the transformer will generate a high voltage, which will pose a great threat to personal safety and equipment safety. [0004] If the system cannot identify the fault in time and take effective remedial measures, when an over-current or over-voltage fault occurs in the system, it will cause great dan...

Claims

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Application Information

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IPC IPC(8): G01R31/00G01R19/165G06N3/02
CPCY04S10/50
Inventor 杨建张鹏飞粟梅姚福林阮璇
Owner 江西中能电气科技股份有限公司
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