A reliable approach for detection of incipient faults of short-circuits in induction generators using machine learning

Filho, Pedro Pedrosa Rebouças; Nascimento, Navar Medeiros M. e; Sousa, Igor Rocha; Medeiros, Cláudio Marques de Sá; Albuquerque, Victor Hugo C. de

doi:10.1016/j.compeleceng.2018.07.046

Cited by 29 publications

(4 citation statements)

References 11 publications

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“…Due to the limitations of FFTs, such as the need for signal stationarity and long measurement times, there has been a growing preference for advanced signal processing methods based on higher order statistics (HOS) in recent years. These HOS-based techniques offer improved performance and have gained in popularity in various applications [39]. Spectral envelope [40] and cepstrum analysis [41] play a significant role in PMSM diagnostics, especially when it comes to situations where detecting mechanical damage.…”

Section: Time Domainmentioning

confidence: 99%

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

Jiang,

Ji,

Zhang

et al. 2024

WEVJ

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This article provides a comprehensive overview of state-of-the-art techniques for detecting and diagnosing stator winding inter-turn short faults (ITSFs) in permanent-magnet synchronous motors (PMSMs) for electric vehicles (EVs). The review focuses on the following three main categories of diagnostic approaches: motor model-based, signal processing-based, and artificial intelligence (AI)-based fault detection and diagnosis methods. Motor model-based methods utilize motor state estimation and motor parameter estimation as the primary strategies for ITSF diagnosis. Signal processing-based techniques extract fault signatures from motor measured data across time, frequency, or time-frequency domains. In contrast, AI-based methods automatically extract higher-order fault signatures from large volumes of preprocessed data, thereby enhancing the effectiveness of fault diagnosis. The strengths and limitations of each approach are thoroughly examined, providing valuable insights into the advancements in ITSF detection and diagnosis techniques for PMSMs in EV applications. The emphasis is placed on the application of signal processing methods and deep learning techniques in the diagnosis of ITSF in PMSMs in EV applications.

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Section: Time Domainmentioning

confidence: 99%

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

Jiang,

Ji,

Zhang

et al. 2024

WEVJ

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“…This session, depicts the outcomes and conversation of fault detection and classification in multiphase IM. It contains three kinds of fault (1) bearing (2) incipient (3) open phase. Initially, Induction motor (IM) behaviors are analyzed under normal and faulty conditions.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…More kinds of failure (for instance broken rotor bars or failure among turns, to name a few) should be assumed and more detection time may be permitted. Although multiphase units have some methods for fault detection in bibliography they are mainly based on discrete wavelet transform or hilbert-huang transform, but no one meet the goal [2,3].…”

Section: Introductionmentioning

confidence: 99%

Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique

Annamalai¹,

Sivakumaran²

2021

emitter

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In this paper, a hybrid system is performed with fault detection and diagnosis on multi-phase induction motor (IM). The proposed method is hybrid of integrated Harris Hawk optimization (IHHO) and gradient boosting decision trees (GBDT) thus called the GBDTI2HO method. Here, additional operators are included in this paper to improve HHO’s search behaviour namely crossover and mutation. Distorted waveforms are generated by different frequency patterns to indicate the time domain frequency as an assessment of failure. For this signal representation, the discrete wavelet transformation (DWT) is suggested. It extracts the characteristics and forwards them to IHHO technique to form the possible data sets. After the generation of the data set, GBDT classifies the ways of failure reached as winding of stator in multi-phase IM. The implementation of the proposed system is compared with existing systems, such as ANN, S-Transform and GBDT. The proposed method is executed on MATLAB/Simulink work platform to demonstrate the successfulness of proposed system, statistical measures are determined, as precision, sensitivity and specificity, mean median and standard deviation. For demonstrating the successfulness of proposed system, statistical measures are determined as precision, sensitivity, specificity, mean median as well as standard deviation. In 50 trails the proposed method, 0.98 for accuracy, 0.96 for specificity, 1.60 for recall as well as 0.97 for precision. In 100 trail the proposed method, 0.96 for accuracy, 0.93 for specificity, 0.87 for recall as well as 0.99 for precision.

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“…Employing classical machine learning algorithms, such as Support Vector Machine (SVM), shallow neural networks, genetic algorithm, fuzzy logic, and decision trees for ITF detection in induction machines has a long history [18–25]. However, over the last 2 decades, the new concept of Deep Learning (DL) brought about an evolution in the field of machine learning [26].…”

Section: Introductionmentioning

confidence: 99%

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

Alipoor

Mirbagheri

Moosavi

et al. 2022

IET Electric Power Appl

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Wind turbines are increasingly expanding worldwide and Doubly‐Fed Induction Generator (DFIG) is a key component of most of them. Stator winding fault is a major fault in this equipment and its incipient detection is of vital importance. However, there is a paucity of research in this field. In this study, a novel machine learning‐based method is proposed for incipient detection of inter‐turn short‐circuit fault (ITF) in the DFIG stator based on the current signals of the stator. The proposed method makes use of state‐of‐the‐art deep learning methods along with conventional signal processing tools and general machine learning techniques. More specifically, the incipient fault detection problem is regarded as a multi‐class classification problem and a Long Short‐Term Memory network, which is more appropriate for time‐series data is utilised for inference. Furthermore, a variant of the celebrated Empirical mode Decomposition analysis tool is used to extract some well‐known statistical features among which the most informative ones are selected using a new feature selection method. Our tests using experimental data in steady‐state conditions show that the proposed method can accurately detect ITF fault at its initial stage when only one turn is shorted. Moreover, its performance is considerably higher than that of a variety of machine learning‐based methods.

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A reliable approach for detection of incipient faults of short-circuits in induction generators using machine learning

Cited by 29 publications

References 11 publications

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

An Overview of Diagnosis Methods of Stator Winding Inter-Turn Short Faults in Permanent-Magnet Synchronous Motors for Electric Vehicles

Bearing/Incipient/Open Phase Fault Detection and Diagnosis of Multi-Phase Induction Motor Drives Equipped By GBDTI2HO Technique

Incipient detection of stator inter‐turn short‐circuit faults in a Doubly‐Fed Induction Generator using deep learning

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