New Algorithm Applied to Transformers' Failures Detection Based on Karhunen–Loève Transform

Castro, Bruno Albuquerque de; Binotto, Amanda; Ardila-Rey, Jorge Alfredo; Fraga, Jose Renato Castro Pompeia; Smith, Colin; Andreoli, André Luiz

doi:10.1109/tii.2023.3240590

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…In addition, there are a variety of motor ITSF diagnosis methods, such as the partial discharge method [89], instantaneous power decomposition method [90], negative sequence impedance method [91], torque harmonic component method [92], etc., which have been applied to a certain extent.…”

Section: Fault Localization Methods Based On Characteristics Of the M...mentioning

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: Fault Localization Methods Based On Characteristics Of the M...mentioning

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

View full text Add to dashboard Cite

show abstract

“…Further, the sensor based discharged detections were proposed in [21,22]. Similarly, in order to analyse the interturn short circuits, Hall-effect sensors have been attested along with PD sensors [23]. In addition, Karhunen-Loève Transform was incorporated to perform the short-circuits and discharges.…”

Section: Introductionmentioning

confidence: 99%

An intelligence expert system for quality health monitoring of power transformers using testing attributes

Manoj

Ranga

2023

Eng. Res. Express

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Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in Reference [29]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

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On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

Liu,

Jiao,

Nie

et al. 2024

High Voltage

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Partial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods.

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New Algorithm Applied to Transformers' Failures Detection Based on Karhunen–Loève Transform

Cited by 10 publications

References 27 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

An intelligence expert system for quality health monitoring of power transformers using testing attributes

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

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