An enhanced machine learning based approach for failures detection and diagnosis of PV systems

• Published in: Energy conversion and management Vol. 151; pp. 496 - 513
• Main Authors: Garoudja, Elyes, Chouder, Aissa, Kara, Kamel, Silvestre, Santiago
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2017; Elsevier Science Ltd
• Subjects: Accuracy; Arrays; Artificial intelligence; Artificial neural network; Artificial neural networks; Back propagation networks; Best-so-far ABC; Classifiers; Computer simulation; Diagnosis; Direct current; Fault detection; Fault Detection and diagnosis; Fault diagnosis; Learning algorithms; Learning theory; Machine learning; Modules; Neural networks; Parameter identification; Photovoltaic; Photovoltaic cells; Probabilistic neural network; Test procedures; Fault Detection and diagnosis; Photovoltaic; Probabilistic neural network; Artificial neural network; Best-so-far ABC
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2017.09.019

•PNN fault detection and diagnosis based approach for PV systems.•The proposed approach has given accurate results for the studied faults.•Its effectiveness has been tested under noisy and noiseless data.•PNN and ANN has been compared for all considered faults. In this paper, a novel procedure for fault detection and diagnosis in the direct current (DC) side of PV system, based on probabilistic neural network (PNN) classifier, is proposed. The suggested procedure consists of four main stages: (i) PV module parameters extraction, (ii) PV array simulation and experimental validation (iii) elaboration of a relevant database of both healthy and faulty operations, and (iv) network construction, training and testing. In the first stage, the unknown electrical parameters of the one diode model (ODM) are accurately identified using the best-so-far ABC algorithm. Then, based on these parameters the PV array is simulated and experimentally validated by using a PSIM™/Matlab™ co-simulation. Finally, efficient fault detection and diagnosis procedure based on PNN classifier is implemented. Four operating cases were tested in a grid connected PV system of 9.54kWp: Healthy system, three modules short-circuited in one string, ten modules short-circuited in one string, and a string disconnected from the array. Moreover, the PNN method was compared, under real operating conditions, with the feed forward back-propagation Artificial Neural Network (ANN) classifiers method, for noiseless and noisy data to evaluate the suggested method’s accuracy and test its aptitude to support noisy data. The obtained results have demonstrated the high efficiency of the proposed method to detect and diagnose DC side anomalies for both noiseless and noisy data cases.