Photovoltaic fault detection method based on improved particle swarm optimization Elman network

Abstract

The invention relates to a photovoltaic failure detection method based on an improved particle swarm optimization Elman network, which is characterized by comprising the following steps: (1) initializing particle swarm algorithm; (2) constructing an Elman neural network topology structure; (3) determining the particle evaluation function and calculating the particle fitness value; (4) updating theparticles and introducing the mutation operator to obtain new population particles: re-determining the individual extreme value and the global extreme value, and obtaining the optimal particle when reaching the set precision or the maximum number of iterations; (5) obtaining the optimal weight values according to the optimal particles obtained in the step (4) to carry out network training and result prediction. The method obtains the optimal weight value of the neural network through the improved particle swarm algorithm, overcoming the defect of the Elman neural network trapped in local optimal solution, greatly improving the prediction efficiency and speed, and facilitating the maintenance and management of the photovoltaic power generation system.

Description

technical field [0001] The invention relates to a photovoltaic fault detection method based on an improved particle swarm optimization Elman network, and belongs to the technical field of fault prediction of photovoltaic power generation systems. Background technique [0002] With the rapid development of the economy, the use of electricity is also increasing, and the consumption of non-renewable resources is huge. Therefore, in recent years, countries have vigorously developed the photovoltaic industry. The emergence of this technology has greatly alleviated the energy crisis. Although the photovoltaic power generation technology is becoming more and more mature, the faults in the photovoltaic power generation system are still worthy of attention and urgently need to be properly resolved. Therefore, a photovoltaic fault detection method based on improved particle swarm optimization Elman network is proposed, which not only saves labor costs but also has important economic s...

AI Technical Summary

Problems solved by technology

[0003] At present, there are many methods for fault detection of photovoltaic systems at home and abroad. The traditional method is manual detection, but this method is not advisable for fault detection of photovoltaic systems in harsh environments.

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