Deep learning method based on autoencoder neural network applied to faults detection and diagnosis of photovoltaic system

Abstract

The paper presents an application of deep learning for fault detection in PV systems located in Algiers –Algeria which has a nominal power of 9.54 kW. Each PV array comprises 30 PV modules each string contains 15 PV modules in series. Fault detection and diagnosis of Photovoltaic Systems (PVS) is an important task for successful solar power generation. Several faults have been occurred such as short-circuit cases and open-circuit string cases in PV generator can leads to fire and risk. The design of efficient defect detection methods for PV systems presents a challenge especially for small scale PV farms. Faults detection methods are failed under the presence of noisy signals. This paper presents a deep learning (DL) based method for detection, diagnosis and classification of the aforementioned defects. The proposed procedure consists of four fundamental steps, first a heuristic optimization approach based on Coyote Optimization Algorithm (COA) the five-unknown electrical parameters of the One Diode Model (ODM) and their insertion into a PSIM-based simulation that aims to mimic the operating PV system. Second, database construction that includes current, voltage and power at the Maximum Power Point (MPP), module temperature and solar irradiance for the PV system is supposed under healthy and faulty operations condition at optimal operational conditions. Then, this is followed by the extraction of new features of the old database using unsupervised learning characteristics of the Auto-Encoder (AE). and last, supervised learning using the new database based on Artificial Neural Network (ANN) construction for PV fault detection and classification. The proposed technique has been validated using monitored data from a real operating PV system situated in Algeria. The obtained results have shown the effectiveness of the proposed technique detection and classification of various PV fault types.