Comprehensive Study on Transformer Fault Detection via Frequency Response Analysis

Kakolaki, Seyed Ebrahim Hosseini; Hakimian, Vahid; Sadeh, Javad; Rakhshani, Elyas

doi:10.1109/access.2023.3300378

Cited by 6 publications

(3 citation statements)

References 135 publications

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“…The first one is avoiding collisions. To prevent collisions between seagulls, the algorithm calculates their new positions by adding an extra variable A, as shown in (11) [38].…”

Section: A Population Migrationmentioning

confidence: 99%

“…Consequently, accurately finding out the incipient defects as soon as possible is of great importance to ensuring the stability of the grid. Among off-line diagnostic techniques, the frequency response analysis (FRA) method has been comprehensively employed to explore the inner faults of the winding due to its high sensitivity, good economy, and good repeatability of data [7], [8], [9], [10], [11]. The FRA is a comparative method that relies on the accurate interpretation of the deviation of FRA traces caused by faults.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM

Wang,

Qiu,

Xie

et al. 2024

IEEE Access

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Power transformers are the vital and expensive components of the power system. Timely identifying and diagnosing the transformer faults is critical to maintaining the stability of the power grid. As a sensitive and economical tool, the frequency response analysis (FRA) method has been widely employed to detect winding faults. However, it is still a challenge to accurately identify the fault types and degrees only by the FRA method. In this article, a new diagnosis method that combines the FRA method with a kernelbased extreme learning machine (KELM) optimized by a seagull optimization algorithm (SOA), is proposed to diagnose the fault types and degrees of the winding. First, a series of FRA tests are performed on a laboratory winding model under three different faults to obtain the FRA dataset. Furthermore, various numerical indices are applied to extract the characteristics of FRA signatures to train the SOA-KELM model. Then, the trained SOA-KELM model is utilized to classify fault types and degrees of the winding. Finally, the feasibility and superiority of SOA-KELM are verified by comparing with SOA optimized support vector machine (SOA-SVM) and random forest (SOA-RF), particle swarm optimization (PSO) algorithm optimized KELM (PSO-KELM), PSO-SVM, PSO-RF, SVM, RF, and KELM from the aspects of diagnosis accuracy and running time. The comprehensive comparison results show that SOA-KELM has the best diagnosis performance.

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“…The first one is avoiding collisions. To prevent collisions between seagulls, the algorithm calculates their new positions by adding an extra variable A, as shown in (11) [38].…”

Section: A Population Migrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM

Wang,

Qiu,

Xie

et al. 2024

IEEE Access

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show abstract

“…Several TW EC models, encompassing two or 3 TW models, were investigated in Refs. [33][34][35][36][37]. In these configurations, high voltage (HV), medium voltage (MV), and low voltage (LV) windings are depicted using the EC model shown in Figure 1.…”

Section: Analysis Of Existing Ec Modelmentioning

confidence: 99%

Derivation of transformer winding equivalent circuit by employing the transfer function obtained from frequency response analysis data

Zhang

2024

IET Electric Power Appl

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Precisely interpreting frequency response analysis (FRA) curves is crucial when investigating mechanical malfunctions in power transformer windings (TWs). However, many conventional explanations of FRA features cannot be validated by an equivalent circuit (EC) that represents winding structures using resistance, inductance, and capacitance components in a ladder network. Incorrect interpretation can lead to misdiagnosis and confusion in asset management. Previous ECs are often proposed by analysing winding structures without rigorous verification compared to the corresponding FRA data. The specific EC is acquired using the transfer function (TF) derived from the measured FRA data. This allows for the establishment of the relationship between TW FRA curves, TF equations, and EC topologies. The precise interpretation of FRA curves can be achieved by closely observing the FRA curves created from TFs and ECs. The remarkable similarity between the measured and modelled FRA curves verifies the authenticity of the derived ECs. This significant achievement clarifies many misunderstandings regarding FRA and represents a substantial advancement in FRA technology.

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A novel estimation methodology for multi-resonance equivalent inductance of transformer winding for inter-turn short-circuit fault detection

Rao,

Mitra,

Pramanik

2024

Electric Power Systems Research

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Comprehensive Study on Transformer Fault Detection via Frequency Response Analysis

Cited by 6 publications

References 135 publications

Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM

Diagnosing Fault Types and Degrees of Transformer Winding Combining FRA Method With SOA-KELM

Derivation of transformer winding equivalent circuit by employing the transfer function obtained from frequency response analysis data

A novel estimation methodology for multi-resonance equivalent inductance of transformer winding for inter-turn short-circuit fault detection

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