A neural space vector fault location for parallel double-circuit distribution lines

Martins, L. Sousa; Martins, J. F.; Pires, V. Fernão; Alegria, C.M.

doi:10.1016/j.ijepes.2004.10.004

Cited by 20 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To achieve this, the line currents of the three phase system are measured and transformed using equations (1) and (2). This approach has already been used in power system protection [11].…”

Section: Proposed Line Current Differential Protectionmentioning

confidence: 99%

Transmission lines protection based on the current eigenvalues differential concept

Pires

Guerreiro

Fortunato

et al. 2010

10th IET International Conference on Developments in Power System Protection (DPSP 2010). Managing the Change

View full text Add to dashboard Cite

This work presents a new approach for a current differential protection of the transmission lines. The proposed approach is based on the Clarke-Concordia transformation and principal component analysis. First the acquired current signals are transformed into "αβo" components by applying the ClarkeConcordia transformation. This allows obtaining typical patterns. To identify these patterns a principal component analysis is performed. Several tests under different fault conditions were performed. The obtained results allow verifying the effectiveness of the proposed approach.

show abstract

“…To achieve this, the line currents of the three phase system are measured and transformed using equations (1) and (2). This approach has already been used in power system protection [11].…”

Section: Proposed Line Current Differential Protectionmentioning

confidence: 99%

Transmission lines protection based on the current eigenvalues differential concept

Pires

Guerreiro

Fortunato

et al. 2010

10th IET International Conference on Developments in Power System Protection (DPSP 2010). Managing the Change

View full text Add to dashboard Cite

show abstract

“…However these techniques did not identify the fault direction and section. Besides there are other techniques developed for fault detection and location using ANNs [26][27][28][29], Thevenin equivalent impedance and compensation factor [30], Clarke Concordia transformation, eigen value approach and NN [31], Kohonen network approach [32], Radial basis neural network [33,34] and synchronized phasor measurement units (PMU) [35,36]. However these techniques did not identify the fault type, fault direction and the faulty section.…”

Section: Introductionmentioning

confidence: 99%

ANN based directional relaying scheme for protection of Korba-Bhilai transmission line of Chhattisgarh state

Yadav

Dash

Ashok

2016

Prot Control Mod Power Syst

View full text Add to dashboard Cite

As it is crucial to protect the transmission line from inevitable faults consequences, intelligent scheme must be employed for immediate fault detection and classification. The application of Artificial Neural Network (ANN) to detect the fault, identify it's section, and classify the fault on transmission lines with improved zone reach setting is presented in this article. The fundamental voltage and current magnitudes obtained through Discrete Fourier Transform (DFT) are specified as the inputs to the ANN. The relay is placed at section-2 which is the prime section to be protected. The ANN was trained and tested using diverse fault datasets; obtained from the simulation of different fault scenarios like different types of fault at varying fault inception angles, fault locations and fault resistances in a 400 kV, 216 km power transmission network of CSEB between Korba-Bhilai of Chhattisgarh state using MATLAB. The simulation outcomes illustrated that the entire shunt faults including forward and reverse fault, it's section and phase can be accurately identified within a half cycle time. The advantage of this scheme is to provide a major protection up to 99.5% of total line length using single end data and furthermore backup protection to the forward and reverse line sections. This routine protection system is properly discriminatory, rapid, robust, enormously reliable and incredibly responsive to isolate targeted fault.

show abstract

“…Neural space vector to fault location for parallel double circuit distribution power lines is presented in [12]. An ANN based application for fault location in electrical transmission lines has been presented in [13].…”

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

View full text Add to dashboard Cite

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.

show abstract

A neural space vector fault location for parallel double-circuit distribution lines

Cited by 20 publications

References 9 publications

Transmission lines protection based on the current eigenvalues differential concept

Transmission lines protection based on the current eigenvalues differential concept

ANN based directional relaying scheme for protection of Korba-Bhilai transmission line of Chhattisgarh state

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

Contact Info

Product

Resources

About