Fault diagnosis system for a multilevel inverter using a principal component neural network

Khomfoi, Surin; Tolbert, Leon M.

doi:10.1109/pesc.2006.1712246

Cited by 16 publications

(19 citation statements)

References 12 publications

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“…However, many neurons are used to train the network (i.e. one neuron for each harmonic); therefore, principal component analysis (PCA) can be used to reduce the number of input neurons as proposed in [15,16]. PCA is a method used to reduce the dimensionality of an input space without losing a significant amount of information (variability) [13].…”

Section: Diagnostic Signalsmentioning

confidence: 99%

“…The comparison in classification performance between the network proposed in [14] and the principal component neural network (PC-NN) is discussed in [16]. By using PCA, the size of input neurons can be reduced from 40 nodes to 5 nodes (i.e.…”

Section: Diagnostic Signalsmentioning

confidence: 99%

“…By using PCA, the size of input neurons can be reduced from 40 nodes to 5 nodes (i.e. 5 harmonics instead of 40 harmonic components) [16]. The results show that the PC-NN has a better overall classification performance by 5 % points; however, the multivariable optimization technique such as genetic algorithm (GA) could improve the classification performance; therefore, a GA is applied to select the valuable principal components to train the neural network as shown in Fig.…”

Section: Diagnostic Signalsmentioning

confidence: 99%

“…7. These 3-D plots of PC scores are transformed with training data set used in [16]. We can see that the classification between normal and faults could be a linear problem, whereas the classification among faults is a nonlinear problem.…”

Section: Diagnostic Signalsmentioning

confidence: 99%

See 3 more Smart Citations

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

Khomfoi

Tolbert

2007

APEC 07 - Twenty-Second Annual IEEE Applied Power Electronics Conference and Exposition

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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.

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Section: Diagnostic Signalsmentioning

confidence: 99%

Section: Diagnostic Signalsmentioning

confidence: 99%

Section: Diagnostic Signalsmentioning

confidence: 99%

Section: Diagnostic Signalsmentioning

confidence: 99%

See 2 more Smart Citations

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

Khomfoi

Tolbert

2007

APEC 07 - Twenty-Second Annual IEEE Applied Power Electronics Conference and Exposition

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show abstract

“…Several researchers have investigated the identification and classification capabilities of Neural network for processing power electronic waveforms [10] and estimating electrical quantities such as fundamental rms current, displacement factor and power factor. Neural network based techniques for fault diagnostics [11][12][13] require significant changes in the system performance in order to detect them, but the methodology proposed in this paper is detecting even the small changes in the system characteristics. This paper deals with monitoring and supervising the performance of the asymmetric half bridge converter circuit in order to detect deviations in the circuit components long before they lead to malfunctions and major failures.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for monitoring the asymmetric half bridge DC–DC converter

Gnanasaravanan¹,

Rajaram²

2012

International Journal of Electrical Power & Energy Systems

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Improvement in Fault Clearance Time of the Cascaded H-Bridge Multilevel Inverter Using Novel Technique Based on Frequency Detection

Phukan

Singh

2021

Modeling, Simulation and Optimization

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Fault diagnosis system for a multilevel inverter using a principal component neural network

Cited by 16 publications

References 12 publications

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

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

Artificial Neural Network for monitoring the asymmetric half bridge DC–DC converter

Improvement in Fault Clearance Time of the Cascaded H-Bridge Multilevel Inverter Using Novel Technique Based on Frequency Detection

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