A Mechanical Fault Diagnosis Model of On-Load Tap Changer Based on Same-Source Heterogeneous Data Fusion

Liang, Xuedong; Wang, Youyuan; Gu, Hongrui

doi:10.1109/tim.2021.3064808

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…Use formula (9) to generate random parameters a, A and C; Judge whether the algorithm reaches the termination condition (t tmax). If the conditions are not met, repeat steps .…”

Section: Parameter Optimization Of Optimized Lstm Based On Igwomentioning

confidence: 99%

“…However, it is still challenging to cope with randomness and real-time diagnosis requirements for OLTCs by these mode decomposition methods. Recently, with the advances in artificial intelligence (AI) technologies, researchers also resort to hidden Markov model (HMM) [7], support vector machine (SVM) [8], convolutional neural network (CNN) [9] to help with OLTC fault diagnosis. Among these technologies, HMM cannot yield synchronized data, and thereby features are easily submerged in noise.…”

Section: Introductionmentioning

confidence: 99%

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A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

Yan

2021

Proceedings of CECNet 2021

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Recently, long short-term memory (LSTM) networks have been widely adopted to help with fault diagnosis for power systems. However, the parameters of LSTM networks are determined by prior knowledge and experience and thereby not capable of dealing with unexpected faults in volatile environments. In this paper, we propose and apply an improved grey wolf optimization (IGWO) algorithm to optimize the parameters of LSTM networks, aiming to circumvent the drawback of empirical LSTM parameters and enhance the fault diagnosis accuracy for on-load tap changers (OLTCs). The composite multiscale weighted permutation entropy and energy entropy yielded by the grasshopper optimization algorithm and variational mode decomposition (GOA-VMD) method are used as the inputs of LSTM networks. The IGWO algorithm is applied in an iterative manner to optimize the relevant super arithmetic of the LSTM. In this way, an IGWO-LSTM combination model is constructed to classify different faults diagnosed in OLTCs. Experimental results verify the diagnosis performance superiority of the proposed method over several widely used comparison benchmarks

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“…Use formula (9) to generate random parameters a, A and C; Judge whether the algorithm reaches the termination condition (t tmax). If the conditions are not met, repeat steps .…”

Section: Parameter Optimization Of Optimized Lstm Based On Igwomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

Yan

2021

Proceedings of CECNet 2021

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“…To address this, an online monitoring and fault diagnosis technology for OLTCs has emerged, significantly reducing costs and improving maintenance efficiency. Various signal processing and fault diagnosis methods have been proposed by scholars, including hidden Markov chain [3], short-time Fourier transform (STFT) [4], wavelet transform (WT) [5,6], empirical mode decomposition (EMD) [7,8], Hilbert transform (HT) [9,10], K-means cluster analysis [11,12], fuzzy C-means clustering (FCM) analysis [13,14], and others. These methods primarily rely on mechanical vibration signals for fault diagnosis of OLTC, but they have not yielded satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Electromechanical Joint Approach for Contact Pair Fault Diagnosis of Oil-Immersed On-Load Tap Changer

Li,

Dou,

et al. 2023

Electronics

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This paper presents a novel fault diagnosis method for oil-immersed on-load tap changers (OLTC) to address the issue of limited diagnostic accuracy. The proposed method combines the analysis of mechanical vibration signals and high-frequency current signals from the contact pair, aiming to improve the precision of fault diagnosis. To begin with, an experimental platform was used to simulate the OLTC contact, enabling the collection of mechanical vibration signals and high-frequency current signals under different operational states. These signals underwent wavelet packet transform for denoising, followed by correlation analysis to investigate their interrelationships across various states. Features were then extracted and analyzed using ensemble empirical mode decomposition and the Hilbert–Huang transform. Subsequently, a support vector machine (SVM) was employed to analyze both the mechanical vibration signal and high-frequency current signal, facilitating the classification of the OLTC contact state. The results demonstrated that the joint analysis of electrical and mechanical signals provided a comprehensive representation of the actual contact state under different conditions. The SVM classification achieved an error below 10% in predicting the values of the two signal types, validating the efficiency and feasibility of the proposed fault diagnosis method for OLTC contacts. The findings presented in this paper offer valuable insights for on-site fault diagnosis of practical OLTCs.

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“…call for frequent changes, accelerating wear and tear. Although malfunctioning of the equipment involved in the tap changer is not frequent [5], the on-load tap changer is one of the most error-prone parts in the transformer because its elements can suffer from both electrical and mechanical stress [6], [7]. The uncertainty on tap changer performance impacts on fundamental analysis tools like state estimation (see, for instance, [8] and [9]).…”

Section: Introductionmentioning

confidence: 99%

PMU-Based Estimation of Systematic Measurement Errors, Line Parameters, and Tap Changer Ratios in Three-Phase Power Systems

Pegoraro

Sitzia

Solinas

et al. 2022

IEEE Trans. Instrum. Meas.

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The availability of accurate data is fundamental for several monitoring and control applications of modern power grids. Nevertheless, the knowledge of critical data such as transmission line and transformer parameters is often affected by uncertainty. This can lead to important problems in the correct management of the power systems. In spite of a monitoring infrastructure that is being renewed thanks to new generation devices providing synchronized measurements, the actual values of line parameters and tap changer ratios are still affected by uncertainty sources that need to be properly considered. The behaviour of all the elements involved in the measurement chain must be duly modelled. This paper proposes an improved method to carry out the simultaneous estimation of line parameters, tap changer ratios, and systematic measurement errors for a three-phase power system. The proposed method is based on the suitable modelling of the measurement chain and on three-phase constraint equations (voltage drop and current balance) of all the components involved. Its effectiveness is confirmed by tests performed on a IEEE 14 bus test system reproduced as a threephase system under different operative conditions.

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A Mechanical Fault Diagnosis Model of On-Load Tap Changer Based on Same-Source Heterogeneous Data Fusion

Cited by 11 publications

References 23 publications

A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

A Novel OLTC Fault Diagnosis Method Based on Optimized Long Short-Term Memory Parameters

An Innovative Electromechanical Joint Approach for Contact Pair Fault Diagnosis of Oil-Immersed On-Load Tap Changer

PMU-Based Estimation of Systematic Measurement Errors, Line Parameters, and Tap Changer Ratios in Three-Phase Power Systems

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