ANN applications in fault locators

Purushothama, G K; Narendranath, A.U; Thukaram, D; Parthasarathy, K. R.

doi:10.1016/s0142-0615(00)00068-5

Cited by 48 publications

(29 citation statements)

References 7 publications

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“…The emerging techniques of Artificial Intelligence (AI) can be a solution to this problem, wherein all the short circuit analysis are carried out offline, and the fault is located online within short time. A brief comparison of various analytical techniques with the ANN method for fault location in Transmission System is provided in [6]. Among the various AI based techniques like Expert systems, Fuzzy Set and ANN systems, the ANN approach for fault location in transmission lines is found to be encouraging [7]- [9].…”

mentioning

confidence: 99%

Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems

Thukaram

Khincha

Vijaynarasimha

2005

IEEE Trans. Power Delivery

353

163

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Abstract-This paper presents an artificial neural network (ANN) and support vector machine (SVM) approach for locating faults in radial distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses measurements available at the substation, circuit breaker and relay statuses. The data is analyzed using the principal component analysis (PCA) technique and the faults are classified according to the reactances of their path using a combination of support vector classifiers (SVCs) and feedforward neural networks (FFNNs). A practical 52 bus distribution system with loads is considered for studies, and the results presented show that the proposed approach of fault location gives accurate results in terms of the estimated fault location. Practical situations in distribution systems, such as protective devices placed only at the substation, all types of faults, and a wide range of varying short circuit levels, are considered for studies. The results demonstrate the feasibility of applying the proposed method in practical distribution system fault diagnosis.

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mentioning

confidence: 99%

Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems

Thukaram

Khincha

Vijaynarasimha

2005

IEEE Trans. Power Delivery

353

163

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“…In [60], the ANN employs two distinct approaches to finding fault distance in distribution feeders using one-and two-end measurements. The scheme uses cascade correlation for fault detection, and both approaches perform equally well in accurately identifying fault location.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Protection of Distributed Generation: Challenges and Solutions

Shahzad¹,

Kahrobaee²,

Asgarpoor³

2017

EPE

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Distributed generation (DG) is the future of energy. This technology allows the bidirectional flow of power within an electrical network. Researchers are faced with many challenges to the accurate implementation of protection schemes for DG-connected distribution network. The schemes designed must satisfy the performance requirements of selectivity, reliability, and sensitivity. Most researchers opine that conventional protection schemes based on over current detection are insufficient to completely and accurately protect a DG-connected distributed power system. There are many challenges that need to be tackled before embarking upon the journey to successfully implement these schemes. This paper summarizes the major challenges which one can encounter while designing protection schemes for DG-connected distribution networks. Some possible solutions from the literature are also mentioned. Moreover, a suggested solution for protecting future active distribution networks is provided. It is expected that this paper will act as a benchmark for future researchers in this field to tackle the challenges related to the protection of active distribution networks.

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“…Fault location in two terminal overhead transmission lines, using ANN is based on fundamental components of the fault and pre-fault voltage and current magnitude measured in each phase at the reference end are discussed in [5]. The potential applicability of ANN techniques for determination of fault location and fault resistance on EHV transmission lines with remote end in feed is presented in [6]. An algorithm for estimating fault location on a transmission line is studied and tested using simulation data obtained from the Electro Magnetic Transient Program (EMTP) [7].…”

Section: Introductionmentioning

confidence: 99%

Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

Gayathri¹,

Kumarappan²

2015

IJCIS

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A new algorithm is developed to enhance the solution for the problems associated with double circuit transmission lines for the mutual coupling between the two circuits under fault conditions and which is highly variable in nature. The algorithm depends on the three-line voltages and the six line currents of double circuit lines at one end. It relies on the application of Support Vector Machine (SVM) and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. Fault resistance, mutual coupling between two circuits and initial prefault conditions are considered. The accuracy of this method has been assessed using a fault simulation software program. In the first state, the accuracy of the method was determined on the basis of SVM reconstructed method. In the second state, this method utilizes voltage and current data acquired at one common end of the two lines. This paper proposes a new hybrid approach for fault location on Extra High Voltage (EHV) lines using RBF based SVM with reconstructed input and Scaled Conjugate Gradient (SCALCG) based neural network method. Sample inputs are determined by MATLAB. The average error fault location in 400kV and 150km line is tested and the results prove that the proposed method is effective and reduces the error within a short duration of time using both RBF based reconstructed input of SVM and SCALCG based neural network.

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ANN applications in fault locators

Cited by 48 publications

References 7 publications

Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems

Artificial Neural Network and Support Vector Machine Approach for Locating Faults in Radial Distribution Systems

Protection of Distributed Generation: Challenges and Solutions

Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

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