Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis

Baghli, Mehdi; Delpha, Claude; Hallouche, A.; Mba, David; Wang, Tianzhen

doi:10.3390/en12071372

Cited by 15 publications

(14 citation statements)

References 24 publications

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“…The first method for setting detection threshold T h is to select T h =0.99D S N max , where D S N max is the maximum value of D S N in the healthy case. If CUSUM decision value is higher than T h , a SC fault is detected [39]. Hence, according to the 500 simulation results, T h =640.4445.…”

Section: Thresholdmentioning

confidence: 99%

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Incipient short-circuit fault diagnosis of lithium-ion batteries

Meng

Boukhnifer

Delpha

et al. 2020

Journal of Energy Storage

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Section: Thresholdmentioning

confidence: 99%

“…• The probability of detection (PD), which represents the ability to correctly detect fault occurrence [39].…”

Section: Robustness Of Fault Detectionmentioning

confidence: 99%

Incipient short-circuit fault diagnosis of lithium-ion batteries

Meng

Boukhnifer

Delpha

et al. 2020

Journal of Energy Storage

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“…Next, a Residual Evaluator examines the generated residual through rms evaluation function over a finite time window or through some other similar norms. Studies show that the best tradeoff result is achieved when a Ratio-type Performance Index [23][24] is defined and optimized. A ratio index is also used within this paper.…”

Section: Pi Equivalent Compensationmentioning

confidence: 99%

“…In another approach, [22] proposes to detect over-current. Finally, [23][24] provide the control system background and outline the requirement for a Ratio-type Performance Index.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Observer-Based Evaluation of Slow Performance Deterioration Due to Component Ageing in DC—DC Converters

Neacșu

2021

IEEE Open J. Ind. Electron. Soc.

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This paper presents the design, analysis, and evaluation of a method for detection of the performance degradation due to component ageing, after-shock change in parameters, or excess in environmental conditions. The major novelty of this method consists of using a statistical based decision process rather than one-off threshold-based switch for detection of deterioration in performance. The method builds upon the previous observer-based method for abrupt (catastrophic) fault detection, and uses a Cumulative Sum algorithm for detection of slow trends in performance. Firstly, a review outlines the rates of change in various parameters due to ageing of components. The design and a comprehensive analysis of the proposed method are performed offline in MATLAB® using an average State Space model. Contrary to previously expensive observer-based methods, the implementation is proposed on a low-cost microcontroller, and demonstrated at a 142 kHz sampling and switching rate. Timing diagrams and resources for implementation are also discussed.

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“…Various intelligent fault detection methods in PV systems have been proposed based on computational intelligent methods and modern signal processing techniques to detect grid faults correctly and to improve the performance of PV systems [20][21][22][23][24][25][26]. The cumulative sum algorithm (CUSUM) [27] and permanent magnet synchronous motor [28] have been used to improve the performances of fault detection and diagnosis. In spite of the performed research work in this field, a more accurate intelligent fault detection method in PV systems is still required.…”

Section: Introductionmentioning

confidence: 99%

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

et al. 2019

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A new protection scheme based on applying a combination of wavelet multi-resolution singular spectrum entropy and support vector machine is proposed to identify different types of grid faults in a three-phase grid-tied photovoltaic system. In this technique, discrete wavelet transform with multi-resolution singular spectrum entropy is utilized to extract the unique features of three-phase voltage signals at the point of common coupling. The three-phase voltage signals are decomposed to provide detail and approximation coefficients of wavelet transform. Then, various features between different types of grid faults can be extracted by a combination of multi resolution analysis and spectrum analysis with entropy as the output. The constructed features vector is utilized as input data of a support vector machine classifier to identify and classify various types of faults. The results illustrate that the proposed intelligent technique not only recognizes different types of grid faults correctly, but also performs quickly in identifying grid faults in a grid-connected photovoltaic system. Apart from this, a graphical investigation is executed to observe the effects of different types of grid faults in photovoltaic (PV) operation which highlight the necessity of intelligent protection methods to protect PV systems.

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Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis

Cited by 15 publications

References 24 publications

Incipient short-circuit fault diagnosis of lithium-ion batteries

Incipient short-circuit fault diagnosis of lithium-ion batteries

A Low-Cost Observer-Based Evaluation of Slow Performance Deterioration Due to Component Ageing in DC—DC Converters

A Fast Fault Identification in a Grid-Connected Photovoltaic System Using Wavelet Multi-Resolution Singular Spectrum Entropy and Support Vector Machine

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