Abstract
In this study, a fault diagnostic system in a multi-level inverter using a MLP network is developed. Using a mathematical model, it is difficult to diagnose a Multilevel-Inverter Drive (MLID) system, because MLID system complexity has a non-linear factor and it consist of many switching devices. Therefore neural network classification is applied to fault diagnosis of MLID system. Multilayer perceptron networks (MLP) are used to identify the type and location of occurring faults from inverter output voltage measurement. Here, MLP network based fault identification system for five level cascade H-bridge Multilevel Inverter (MLI) is analyzed. The proposed system identifies the fault with a greater accuracy and the results to various input patterns are presented for easy comprehension.
Highlights
Demanding higher power ratings is a future scope for industry and Multilevel-Inverter Drive (MLID) systems have become a solution for high power applications
A Radial Basis Function Neural Network is trained with the patterns of the output voltage waveforms during various instances of misfiring of one or more switches in the Multilevel Inverter (MLI)
The neural network delivered an appreciable performance throughout the operating range of the MLI
Summary
Demanding higher power ratings is a future scope for industry and MLID systems have become a solution for high power applications. Multilayer perceptron networks (MLP) are used to identify the type and location of occurring faults from inverter output voltage measurement. The training algorithm for the RBFN (Lezana et al, 2010; Jun et al, 2012) as stated involves: Fig. 6: RBF Network architecture type activation as shown, which computes its output value based on the Euclidean distance of the weighted sum of the samples from the center vector of the neuron.
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