Wavelet Packet Entropy and RBFNN Based Fault Detection, Classification and Localization on HVAC Transmission Line

Patel, Bikash; Bera, Parthasarathi; Saha, Binoy

doi:10.1080/15325008.2018.1431817

Cited by 17 publications

(7 citation statements)

References 30 publications

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“…RBFNN has often used as a key method for the development of fault models using the wavelet features. Research works of [33,34] illustrate similar works using RBFNN. Samantaray, S. R. et al ( 2007) in [33] presents a distance relaying scheme for estimation of fault location using radial basis function neural network (RBFNN); although the fault classification is done using support vector machine (SVM).…”

Section: Application Of Radial Basis Function Neural Network (Rbfnn) For Fault Analysismentioning

confidence: 82%

“…The work uses one cycle pre-fault and one cycle post-fault signal. in [34] also proposes wavelet packet entropy and RBFNN-based analysis for fault detection, classification and localization technique for HVAC transmission line. The work considers the dynamics of alternator and also considers the effect of transformers.…”

Section: Application Of Radial Basis Function Neural Network (Rbfnn) For Fault Analysismentioning

confidence: 99%

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Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

Mukherjee¹,

Kundu

Das

2021

J. Inst. Eng. India Ser. B

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Transmission lines are one of the most widely distributed engineering systems meant for transmitting bulk amount of power from one corner of a country to the farthest most in the other directions. The expansion of the lines over different terrains and geographic locations makes these most vulnerable to different kinds of atmospheric calamities which more often develops faults in line. It is imperative to remove the faulty line at the earliest to restrict undue outflow of bulk power through the faulted point as well as restore system stability earliest to resume normal power flow operation. Here lays the importance of having a robust fault identification, classification and localization algorithm which would be successfully able to drive as well as actuate the digital relaying system. Researchers have worked out several methodologies in developing improved power system protection algorithms which would be able to serve to eliminate faults immediately on occurrence of the same. A brief yet exhaustive review has been presented in this article including the several methodologies adopted by numerous researchers for developing effective fault diagnosis schemes, mentioning about the highlights as well as the shortcoming of each of the methods. This compact and effective survey of literature works would help researchers to take up appropriate techniques for different purposes of transmission line fault analysis.

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Section: Application Of Radial Basis Function Neural Network (Rbfnn) For Fault Analysismentioning

confidence: 82%

Section: Application Of Radial Basis Function Neural Network (Rbfnn) For Fault Analysismentioning

confidence: 99%

Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

Mukherjee¹,

Kundu

Das

2021

J. Inst. Eng. India Ser. B

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“…A detailed study was carried out to classify the faults and their locations in a transmission line through MATLAB simulation [16]. An investigation was carried out near both ends of the transmission line to identify the faulty conditions by extracting the signals of the wavelet packet (db-6) entropy and classifying the type of fault and location of them through RBFNN [17]. The high-impedance fault (HIF) has been detected and classified through the wavelet-based DWT technique, and its accuracy has been checked by the RBFNN.…”

Section: Introductionmentioning

confidence: 99%

Fault detection through discrete wavelet transforms and radial basis function neural network in shunt compensated distribution systems

Joshi,

Patel

2024

Eng. Res. Express

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This paper discusses a critical study of fault detection, classification, and identification in distribution systems compensated through a distribution static synchronous compensator (D-STATCOM) using discrete wavelet transforms (DWT) and a radial basis neural network (RBFNN). The efficacy of the proportional integral (PI) controller is discussed in the simulation during normal, voltage sag, and swell conditions. Then, the Daubechies mother wavelet (db4) is used here to extract and decompose the fault current signals because of its high accuracy of detection with less processing time. The models are subjected to different conditions of faults, such as line to ground (LG), line to line (LL), double line to ground (LLG), and three phases (LLL and LLLG) with different fault resistance. The novelty of the scheme is that DWT and RBFNN techniques were compared and proven effective in demonstrating the faulty conditions. To validate the proposed approach, a simulation study is carried out in MATLAB with various operating conditions, and it is shown that the proposed method can depend on abundantly protecting the distribution grid from faulty conditions.

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“…For a dynamic system, data is the performance of system characteristics, so feature extraction has become one of the key problems in machine learning and data mining [17]. The common feature extraction techniques such as Fourier analysis [18], [19], wavelet transform [20], [21], Clarke transform [22], and common feature subset selection techniques such as principal component analysis (PCA) [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

Che

2021

IEEE Access

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If the fault sources can be located in a timely and accurate manner when an unmanned aerial vehicle (UAV) has an early minor fault, a major accident can be avoided. In light of the early fault of the six-rotor UAV motors, this paper proposes a fusion fault diagnosis model based on Conformal Fourier Transform (CFT) and the improved Self-Organizing Feature Map (SOM) neural network. Firstly, to overcome the weakening of UAV fault information caused by external interference and solve the problem that the non-uniform distribution of the collected flight data affects the accuracy of the frequency domain processing, on the basis of changing the processing method of the Fourier integral kernel and using high-order numerical integration, an improved Conformal Fourier Transform (CFT) algorithm optimized by spectrum refinement strategy is proposed. Then this algorithm is used to further intensify the main features and build a clear fault state in a cycle processing method. Secondly, an improved SOM neural network is designed. We use the unweighted average distance (UPGMA) of the agglomerative hierarchical clustering algorithm to detect the clustering areas of each pattern category of the data samples, and the weight vectors of different clustering areas are used to randomly initialize the connection weights of SOM network, thereby improving the clustering speed and convergence effect. On this basis, CFT algorithm is integrated into the improved SOM network to realize the multi-module collaboration strategy and finally form a new fusion fault diagnosis model. INDEX TERMS CFT algorithm, improved SOM network, main feature intensification, hierarchical clustering, fault diagnosis.

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Wavelet Packet Entropy and RBFNN Based Fault Detection, Classification and Localization on HVAC Transmission Line

Cited by 17 publications

References 30 publications

Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

Transmission Line Faults in Power System and the Different Algorithms for Identification, Classification and Localization: A Brief Review of Methods

Fault detection through discrete wavelet transforms and radial basis function neural network in shunt compensated distribution systems

Fusion Fault Diagnosis Model for Six-Rotor UAVs Based on Conformal Fourier Transform and Improved Self-Organizing Feature Map

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