Improved Structure of PNN Using PCA in Transformer Fault Diagnostic

Paydarnia, Hossein; Hajiaghasi, Salman; Abbaszadeh, Karim

doi:10.1007/s13369-014-1004-z

Cited by 13 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Energies 2019, 12, 4170 2 of 18 DGA interpretation methods [1], including key gas method [2,3], IEC three-ratio method [4,5], Duval triangle method [6], Rogers ratio method [7] and Dornenburg ratio method [8], Duval pentagon [9], Mansour pentagon method [10,11], etc., are available to identify the different types of faults occurring in operating transformers. Although the commonly used methods are simple and effective in transformer fault diagnosis, they suffer from defects such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which will affect the reliability of fault analysis [12].With the development of artificial intelligence (AI), machine learning and pattern recognition methods have been widely used in power transformer fault diagnosis, including artificial neural network (ANN) [13][14][15], support vector machine (SVM) [16][17][18][19][20][21][22][23][24], probabilistic neural network [25,26], Bayesian neural network [27], fuzzy logic [28][29][30], deep belief network [31], expert system [32,33], which make up for the shortcomings of the traditional DGA methods, directly or indirectly improve the accuracy of transformer fault diagnosis, and provide a new idea for high-precision transformer fault diagnosis. Although these methods have achieved good results, there are also some shortcomings.…”

mentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

View full text Add to dashboard Cite

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

show abstract

mentioning

confidence: 99%

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Few of conventional approaches in the literature have been assessed on the TEP benchmark in terms of the detectability abilities. We cite, among them, the principal component analysis (PCA) and its varieties [23][24][25][26]30,35]. We name as well the multivariate exponentially weighted moving average (MEWMA) [27,28] and MEWMA-PCA [32] that combines between PCA and MEWMA.…”

Section: Detectabilitymentioning

confidence: 99%

A New Kernel-Based Classification Algorithm for Systems Monitoring: Comparison with Statistical Process Control Methods

et al. 2014

View full text Add to dashboard Cite

The paper presents a new Kernel-based monitoring algorithm compared with statistical process control methods, such as DISSIM and MS-PCA and some other methods widely used in process control applications. The proposed algorithm is a modified version of the well known support vector domain description (SVDD). The last one is commonly used for one-classification problems, named also novelty detection. In this paper, we have used a modified SVDD endowed with useful tools to manage multi-classification problems. The proposed classifier is also able to deal with stationary as well as non-stationary data. The principle is based on the dynamic update of the training set through a recursive deletion/insertion procedure according to adequate rules. In order to reduce the computational complexity and improve the rapidity of convergence, the algorithm considers in each run a limited frame of samples for the training process. To prove its effectiveness, the approach is assessed at first on artificially generated data. Then, we have performed a case study applied on chemical process.

show abstract

“…The leakage flux fault detection strategies are classified into the vibration-based method (VBM) and search coil-based method (SCBM), and flux leakage-based methods; they were studied and compared. This modelling aims to achieve an FRA -The capacitive effect can be detected at high frequency -This method needs previous data on the transformer -This method needs complicated tools for detection -Offline method -Needs expert's opinion and sophisticated instruments [12,[16][17][18] Negative sequence -The signal for fault detection is available -Unable to locate the fault [19,20] Partial discharge -Well-established method in power utilities -This method is under the influence of tank and windings [21][22][23] Flux-based method -Precise and accurate -Exact fault location detection -Changes in the transformer structure -Models developed for verification purposes -Requires the details of the transformer structure and sensors [7,8,11,24] Voltage and current measurement -Models developed for verification purpose -Unable to locate the fault [9,25,26] Differential protection -Classical robust method -Online monitoring is possible -Unable to detect inter-turn faults at initial levels -Mainly depends on the precision of the current transformer -Sensitive to winding insulations breakdown -Sensitive to the structure of the transformer [27][28][29] Intelligent approach -Detect minute faults -Robust against missing data…”

Section: Introductionmentioning

confidence: 99%

Transformer inter‐turn failure detection based on leakage flux and vibration analysis

Hajiaghasi

Ahmadi

Goleij

et al. 2021

IET Electric Power Appl

Self Cite

View full text Add to dashboard Cite

Inter‐turn winding fault is one of the most critical failures of the transformers. Here, a comprehensive transformer leakage flux analysis under inter‐turn winding fault is presented, and the feasibility and sensitivity of leakage flux‐based fault detection methods are investigated. The leakage flux fault detection strategies are classified into vibration‐based methods and search coil‐based methods. Flux leakage‐based methods have been studied and compared. Moreover, the effects of various important factors, such as fault severity, fault location, load power factor, and loading rate in transformer under inter‐turn faults are studied. Furthermore, two new online methods based on leakage flux and vibration analysis are proposed. In the first method, search coils voltage analysis is used for fault detection. In the second one, the vibration of the transformer is measured using the Digital Accelerometer (ADXL) sensor and compared with the vibration of the normal conditions, and based on the amount of vibration, fault detection is performed. The simulation and experimentation results on 20/0.4 kV, 50 kVA distribution transformer demonstrated that the implementation of leakage flux‐based methods provides valuable information on the transformer behaviour, which can be used to detect transformer winding faults.

show abstract

Improved Structure of PNN Using PCA in Transformer Fault Diagnostic

Cited by 13 publications

References 13 publications

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

A New Kernel-Based Classification Algorithm for Systems Monitoring: Comparison with Statistical Process Control Methods

Transformer inter‐turn failure detection based on leakage flux and vibration analysis

Contact Info

Product

Resources

About