Health Monitoring and Fault Detection in Photovoltaic Systems in Central Greece Using Artificial Neural Networks

Abstract

The operation and maintenance of a photovoltaic system is a challenging task that requires scientific soundness, and has significant economic impact. Faults in photovoltaic systems are a common phenomenon that demands fast diagnosis and repair. The effective and accurate diagnosis and categorization of faults is based on information received from the photovoltaic plant monitoring and energy management system. This paper presents the application of machine learning techniques in the processing of monitoring datasets of grid connected systems in order to diagnose faults. In particular, monitoring data from four photovoltaic parks located in Central Greece are analyzed. The existing data are divided for training and validation procedures. Different scenarios are examined first, in order to observe and quantify the behavior of artificial neural networks in already known faults. In this process, the faults are divided in three main categories. The system’s performance deviation against the prediction of the trained artificial neural network in each fault category is processed by health monitoring methodology in order to specify it quantitatively.

Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks

“…Days with reported faults are rejected from Figure 22 in order to decouple the effect of degradation. The error in these faulty conditions was over 30% for faults in a string, whereas a faulty panel was associated with a deviation of 10-20% and faults of near shading of 6-10% compared to 5% during normal operation [58]. Most of the PV panels' manufacturers declare a linear degradation of the semiconductor, which is included in the warranty conditions.…”

Accurate Identification of Partial Discharge Signals in Cable Terminations of High-Speed Electric Multiple Unit Using Wavelet Transform and Deep Belief Network

“…The realization of accurate identification of different discharge signals of cable terminals is helpful to obtain accurate partial discharge signals of cable terminals and to diagnose the process of internal fault occurrence in cable terminals [32][33][34]. With rapid developments in artificial intelligence (AI), its techniques are now widely used in electrical and energy systems [35][36][37][38][39][40][41][42][43][44][45][46]. These AI-related techniques include not only advanced methods but also foundational data analysis methods, which are integral to developing AI-powered pattern recognition methods.…”

Label-Free Fault Detection Scheme for Inverters of PV Systems: Deep Reinforcement Learning-Based Dynamic Threshold

“…Second, the threshold methods estimate the power generation of PV systems, and compare the estimations with actually measured power generation. By analyzing the differences, users detect different abnormalities, such as shading effects [27], snow accumulation [28], maximum power point tracking error [29], and faulty conditions of DC-AC converters [15,30,31]. The threshold methods have the advantage of working well even without fault-labeled data.…”

“…The threshold methods have the advantage of working well even without fault-labeled data. However, the thresholds are typically predetermined [31], or determined by monitoring data profiles [30], which has room for improvement.…”