A Diagnostic Technique for Multilevel Inverters Based on a Genetic-Algorithm to Select a Principal Component Neural Network

Abstract

A genetic-algorithm-based selective principal component neural network method for fault diagnosis system in a multilevel inverter is proposed in this paper. Multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults from inverter output voltage measurement. Principal component analysis (PCA) is utilized to reduce the neural network input size. A lower dimensional input space will also usually reduce the time necessary to train a neural network, and the reduced noise may improve the mapping performance. The genetic algorithm is also applied to select the valuable principal components. The neural network design process including principal component analysis and the use of genetic algorithm is clearly described. The comparison among MLP neural network (NN), principal component neural network (PC-NN), and genetic algorithm based selective principal component neural network (PC-GA-NN) are performed. Proposed networks are evaluated with a simulation test set and an experimental test set. The PC-NN has improved overall classification performance from NN by about 5% points, whereas PC-GA-NN has better overall classification performance from NN by about 7.5% points. The overall classification performance of the proposed networks is more than 90%.Index Terms -Fault diagnosis, genetic algorithm, multilevel inverter, neural network, principal component analysis.

Fault Detection and Classification in MMC-HVDC Systems Using Learning Methods

“…To build the aforementioned artificial intelligence machine, feature extraction techniques such as Fourier analysis [20,21], wavelet transform [14,15], Clarke transform [12] or feature subset selection techniques, such as principal component analysis (PCA) [10,22] and multidimensional scaling (MDS), plays an important role. Sometimes to select suitable sub-features, the genetic algorithm (GA) [10,22,23] or particle swarm optimization (PSO) [24] are employed. It is well known that feature extraction has always been a bottleneck in the field of fault diagnosis.…”