BP neural network-based photovoltaic grid-connected power station fault detection device based on BP neural network

Abstract

The invention discloses a BP neural network-based photovoltaic grid-connected power station fault detection device, belongs to the technical field of solar photovoltaic power generation, and is characterized by comprising a photovoltaic array, a control module and a temperature sensor array group; the photovoltaic array is formed by connecting a plurality of photovoltaic assemblies in series; the control module detects whether the photovoltaic array breaks down or not according to the current value and the voltage value of the photovoltaic array; and the control module calculates whether the photovoltaic array has faults such as hot spots, subfissure, shielding and the like according to the measured value of the temperature sensor of the photovoltaic array.

Description

technical field [0001] The invention relates to a fault detection device for a photovoltaic grid-connected power station based on a BP neural network, and belongs to the technical field of solar photovoltaic power generation. Background technique [0002] With the urgent need for new energy applications and the continuous expansion of the scale of solar photovoltaic power generation, the operation, maintenance, management and other aspects of photovoltaic power generation urgently need intelligent transformation and upgrading to improve efficiency and reduce costs. Therefore, it is necessary to accurately understand the working status of each photovoltaic panel in real time. Although there are some monitoring methods at present, none of them can correctly judge the operating status of photovoltaic modules. For example, the technical method disclosed in Patent Application No. 2020203163891 "A Component-Level Photovoltaic Power Station Fault Detection System". Since the work...

AI Technical Summary

Problems solved by technology

Since the working state of photovoltaic modules is greatly affected by temperature, only detecting the output terminal voltage cannot accurately judge whether the operating state of photovoltaic modules is good or bad

The current photovoltaic power plants can only monitor a string of photovoltaic battery modules and cannot take into account the influence of the backplane temperature, cannot determine the type of fault, and cannot meet the operation and maintenance requirements of large-scale photovoltaic power plants. This technology is designed to solve this problem. Designed

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