Fault diagnosis and neutral point voltage control when the 3-level inverter faults occur

Son, Ho-In; Kim, Tae-Jin; Kang, Daeseok; Hyun, Dong-Seok

doi:10.1109/pesc.2004.1354806

Cited by 28 publications

(5 citation statements)

References 10 publications

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“…A solution based on the load current space vector pattern is presented in [21]. This method compares the current-space vector locus obtained during the normal and faulty operation of the converter, allowing an easy identification of an OC fault.…”

Section: Wwwietdlorgmentioning

confidence: 99%

Fault diagnostic algorithm for three‐level neutral point clamped AC motor drives, based on the average current Park's vector

Mendes¹,

Abadi²,

Cruz³

2014

IET Power Electronics

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This study presents a new fault diagnostic technique for a semiconductor open-circuit fault in a neutral-point clamped inverter, based on the average current Park's vector approach. This method relies on the measurement of two motor line currents and is able to perform a complete diagnosis in less than half of the inverter output current period. The implementation of this diagnostic technique requires no additional current or voltage sensors besides the ones already used by the control algorithm of the converter. The diagnostic method uses the modulus and angle of the normalised average current Park's vector to detect the fault and identify the faulty insulated gate bipolar transistor (IGBT) pair, respectively. Simultaneously, the normalised average values corresponding to the positive and negative parts of the motor supply currents waveforms are used to identify the inner faulty IGBTs with even and odd numbers, respectively. The diagnostic method is also robust to transient conditions imposed by load and speed variations. The performance and effectiveness of the proposed diagnostic technique are validated both by the simulation and experimental results.

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Section: Wwwietdlorgmentioning

confidence: 99%

Fault diagnostic algorithm for three‐level neutral point clamped AC motor drives, based on the average current Park's vector

Mendes¹,

Abadi²,

Cruz³

2014

IET Power Electronics

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“…Output voltage harmonics is measured to find the fault detection in the cascaded H-bridge Inverter as in [7][8], which suits for the phase shift PWM. Multilevel Inverters controlled by the SVPWM control [9][10]. A medium voltage drive with the cascaded H-bridge is experimented with the fault detection technique.…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection of Multilevel Inverter using Optimized Radial Basis Function based Artificial Neural Network in Renewable Energy Power Generation Application

Manjunath¹,

Kusagur²

2018

IJCA

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The Optimized Artificial Neural Network based fault detection of the multilevel inverters are of research interest since the HVDC system and Electrical Drives system has started using the multilevel inverters as the power processing units. While the power supply to the multilevel inverters is constant the results of the THD obtained would be stable. But the multilevel inverters that are used with Renewable Energy Resources as the power supply, the variation in the supply would largely affect the THD values thus a robust model for the fault detection of multilevel inverters which not only considers the THD but the positive sequence voltage, negative sequence voltage, zero sequence voltage and angle of the inverter output is considered. This paper is an attempt to develop a robust fault detection method using the optimized Artificial Neural Network (ANN) using the Radial Basis function. The objective function of this optimization algorithm is the minimization of the Mean Square Error while the ANN is getting trained. The optimization is carried out by the use of the weight and the bias value search in the search space, which would enhance the training of the ANN. Particle Swarm Optimization (PSO) and the Cuckoo Search Algorithm (CSA) are considered for the optimization algorithms. Matlab based implementation is carried out and the results are measured and tabulated. It is observed that the CSA algorithm is performing better while training the ANN for the fault detection.

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“…However, fault detection and diagnosis in multilevel inverter found notable movements only in the last decade. Various methods are introduced for fault detection of the converter and/or identification of faulty power semiconductor switch in them, based on current-vector patterns [11], output voltage frequency analysis [12], park's vector approach [13], wavelet transform [14], sliding mode observer [15], mass centre of the voltage pattern [16], normalized phase current and the change of the neutral-point voltage [17] in multilevel inverters.…”

Section: Introductionmentioning

confidence: 99%

Open transistor fault detection in asymmetric multilevel inverter

Raj

George

Jagadanand

2015

2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

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The number of power semiconductor switches and DC sources will increase with the increase in the number of levels of output voltage of the multilevel inverter. As the number of power semiconductor switches increases, the possibility of its failure and hence the breakdown of the inverter operation also escalate in a nonlinear manner. Hence, the detection of the failure of the power semiconductor switch is of paramount importance, considering the availability and reliability of the system is concerned. The gate unavailability or the transistor open circuit fault detection in asymmetrical multilevel inverter by means of monitoring the mean value of the output voltage is presented in this paper. Simulation of this method is carried out in both binary and trinary combinations of asymmetric multilevel inverter. Keywords-Multilevel inverter (MLI), cascaded H-bridge (CHB), power semiconductor switch fault, fault detection, condition monitoring, asymmetric multilevl inverter.

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Fault diagnosis and neutral point voltage control when the 3-level inverter faults occur

Cited by 28 publications

References 10 publications

Fault diagnostic algorithm for three‐level neutral point clamped AC motor drives, based on the average current Park's vector

Fault diagnostic algorithm for three‐level neutral point clamped AC motor drives, based on the average current Park's vector

Robust Fault Detection of Multilevel Inverter using Optimized Radial Basis Function based Artificial Neural Network in Renewable Energy Power Generation Application

Open transistor fault detection in asymmetric multilevel inverter

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