Fault Diagnostic System for a Multilevel Inverter Using a Neural Network

Khomfoi, Surin; Tolbert, Leon M.

doi:10.1109/tpel.2007.897128

Cited by 248 publications

(104 citation statements)

References 15 publications

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“…Preventing the model from defining a bias and choosing a statistical method that is robust and can solve complex relationships are also crucial. An Artificial Neural Network (ANN) is a popular statistical method which can explore the relationships between variables with high accuracy [1][2][3][4]. Essentially, the structure of an ANN is computer-based and consists of several simple processing elements operating in parallel [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Kayrı

2016

MCA

270

159

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Abstract:The objective of this study is to compare the predictive ability of Bayesian regularization with Levenberg-Marquardt Artificial Neural Networks. To examine the best architecture of neural networks, the model was tested with one-, two-, three-, four-, and five-neuron architectures, respectively. MATLAB (2011a) was used for analyzing the Bayesian regularization and Levenberg-Marquardt learning algorithms. It is concluded that the Bayesian regularization training algorithm shows better performance than the Levenberg-Marquardt algorithm. The advantage of a Bayesian regularization artificial neural network is its ability to reveal potentially complex relationships, meaning it can be used in quantitative studies to provide a robust model.

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Section: Introductionmentioning

confidence: 99%

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Kayrı

2016

MCA

270

159

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“…Power converter failures include open-circuit and short-circuit faults. They are caused by thermal stress, high electrical, wire disconnection, or gate driver failure [118][119][120][121]. The Park vector approach was first proposed in Ref.…”

Section: Fault Diagnosis Of Power Convertersmentioning

confidence: 99%

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Huang

Xiao

et al. 2017

Front. Mech. Eng.

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Electric power conversion system (EPCS), which consists of a generator and power converter, is one of the most important subsystems in a direct-drive wind turbine (DD-WT). However, this component accounts for the most failures (approximately 60% of the total number) in the entire DD-WT system according to statistical data. To improve the reliability of EPCSs and reduce the operation and maintenance cost of DD-WTs, numerous researchers have studied condition monitoring (CM) and fault diagnostics (FD). Numerous CM and FD techniques, which have respective advantages and disadvantages, have emerged. This paper provides an overview of the CM, FD, and operation control of EPCSs in DD-WTs under faults. After introducing the functional principle and structure of EPCS, this survey discusses the common failures in wind generators and power converters; briefly reviewed CM and FD methods and operation control of these generators and power converters under faults; and discussed the grid voltage faults related to EPCSs in DD-WTs. These theories and their related technical concepts are systematically discussed. Finally, predicted development trends are presented. The paper provides a valuable reference for developing service quality evaluation methods and fault operation control systems to achieve high-performance and high-intelligence DD-WTs.

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“…In industrial applications, where safe and reliable operation is always expected, it is important to monitor the condition of power electronic switches in inverters. As the number of level increases, number of power electronic switches also increases which leads to increase in probability of failure of any switch and hence any such fault should be detected at the earliest in order to avoid the operation of drive and motor under abnormal conditions [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers used the inverter current [8][9] and inverter output voltage [11][12][13][14][15] to develop the fault diagnostic system. Surin Khomfoi et al [13], developed an open-switch fault diagnostic system of a multilevel inverter using the output voltage FFT pattern and five parallel neural networks with 40 input neurons per network.…”

Section: Introductionmentioning

confidence: 99%

“…Surin Khomfoi et al [13], developed an open-switch fault diagnostic system of a multilevel inverter using the output voltage FFT pattern and five parallel neural networks with 40 input neurons per network. Since the size of the neural network is high, in another paper, Surin Khomfoi et al, proposed an alternative methodology in which combination of FFT, principal component analysis, genetic algorithm and neural network approach was used to detect the fault type and fault location [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of Open-Switch and Short-Switch Failure of Multilevel Inverters through DWT and ANN Approach using LabVIEW

Parimalasundar¹,

Vanitha²

2015

Journal of Electrical Engineering and Technology

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-In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

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Fault Diagnostic System for a Multilevel Inverter Using a Neural Network

Cited by 248 publications

References 15 publications

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data

Overview of condition monitoring and operation control of electric power conversion systems in direct-drive wind turbines under faults

Identification of Open-Switch and Short-Switch Failure of Multilevel Inverters through DWT and ANN Approach using LabVIEW

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