Fault Diagnosis in a Photovoltaic Grid-Tied CHB Multilevel Inverter Based on a Hybrid Machine Learning and Signal Processing Technique

Abstract

An open circuit fault diagnosis scheme for the power switches of the output inverters in a cascaded H bridge multilevel converter is proposed in this research work, which is designed to be used as part of a large-scale photovoltaic generation system with a rating of 1 MW and 13.2 kV. A two stage classifier is the main core of the technique, in which two different approaches are combined, 1) a signal processing algorithm and 2) a machine learning method, having an artificial neural network classifier as its main model. The general fault diagnosis scheme is developed in a per-phase basis of the converter, using only three neural network classifiers for the three phase configuration. The method is suitable to diagnose open circuit faults under different irradiance conditions and parametric variations, and is specified to keep the same level of complexity even if the general structure of the topology is changed. The total sensor count is not incremented since the proposed algorithm uses the individual dc bus voltages and the output currents already required to execute the control system. The performance of the fault diagnosis scheme is tested at a simulation level using Matlab - Simulink / Simscape, considering a wide range of irradiance conditions, even under an irregular distribution of this parameter, and also assuming single open circuit faults in the power switches of one phase.

Keywords

• Fault diagnosis
• PV generation
• multilevel CHB converter
• artificial intelligence
• machine learning
• neural network