Fault Diagnosis Based on Machine Learning for the High Frequency Link of a Grid-Tied Photovoltaic Converter for a Wide Range of Irradiance Conditions

The objective of this work is to select a Machine Learning Technique (MLT) to develop a fault diagnosis scheme for the power switching devices of the High Frequency link (HF link) in a grid-tied Photovoltaic (PV) system, without increasing the total number of sensors, and being capable to operate online. Artificial Neural Network (ANN), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Naive Bayes (NB) algorithms are considered to solve the problem of fault classification. These four MLTs are compared using the specificity and sensitivity indexes. The inputs of the models are obtained from the mean value of the signals given by the Discrete Wavelet Transform (DWT) of the dc link voltage and the power extracted from the PV panels. Support vector machine algorithm is chosen as the most suitable classifier to diagnose single and simultaneous open circuit faults with lower computational effort. Simulation and real-time hardware-based experimental tests demonstrate that the MLTs are suitable and reliable to diagnose open circuit faults in a wide range of irradiance levels, ranging from 200 W/m2 to 1000 W/m2, even under 6 %25 and 12 %25 measurement errors, without increasing the overall system cost. © 2013 IEEE.

Fault diagnosis; high frequency link; machine learning; photovoltaic systems Barium compounds; Discrete wavelet transforms; Fault detection; Neural networks; Photovoltaic cells; Power converters; Support vector machines; Condition; Faults diagnosis; High frequency HF; High frequency link; Machine learning techniques; Machine-learning; On-machines; Open-circuit fault; Photovoltaic converter; Photovoltaic systems; Failure analysis

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