Photovoltaic array fault diagnosis and early warning method

Abstract

The invention relates to a photovoltaic array fault diagnosis and early warning method comprising the following steps: combining an Elman nerve network optimized by a non-linear least square method and a decision tree with experience knowledge so as to form a fault diagnosis model; collecting present photovoltaic array operation data and meteorology data, and computing errors when compared with historical normal state data; using the fault diagnosis model to obtain the corresponding fault type and credibility when the error is bigger than a threshold; finally integrally evaluating so as to obtain the final fault type credibility, and selectively carrying out fault early warning according to the credibility values; updating a fault knowledge base according to the field actual measurement conditions. The method combines the LM-Elman nerve network and the decision tree with experience knowledge so as to built the fault diagnosis model, thus improving the history data sensitivity, providing better prediction effect when compared with a BP network, and improving the network convergence speed and training precision; the experience knowledge is supplemented, thus providing stronger robustness; the method can timely detect and diagnose, thus reducing fault incidence rate, and ensuring the photovoltaic power station to stably work.

Description

technical field [0001] The invention belongs to the technical field of photovoltaic power plants, and in particular relates to a photovoltaic array fault diagnosis and early warning method. Background technique [0002] In recent years, as the energy crisis has become increasingly prominent, governments of various countries have begun to vigorously promote the research and development of new renewable energy projects. As a representative of new energy sources, solar energy has natural advantages such as clean, environmentally friendly, renewable, and sustainable, which enables the rapid development of photovoltaic power generation technology. However, with the expansion of the scale of photovoltaic power plants and the popularity of distributed photovoltaic power plants, photovoltaic cell module failures often occur. At present, most photovoltaic power plants assemble photovoltaic arrays in series or in parallel. Therefore, when the failure of a certain component cannot be...

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Problems solved by technology

[0014] The purpose of this invention is to solve the problems of untimely fault detection and high operation and maintenance costs in the current photovoltaic power station system, and proposes a photovoltaic array fault diagnosis and early warning method, using LM-Elman neural network and decision tree combined with empirical knowledge Construct a fault diagnosis model, build and train a neural network for a photovoltaic array fault diagnosis model based on historical data, establish a fault knowledge base and a decision tree model

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