An adaptive approach in distance protection using an artificial neural network

Khaparde, S.A.; Warke, Nilima; Agarwal, Sanjeev

doi:10.1016/0378-7796(96)01040-1

Cited by 38 publications

(11 citation statements)

References 7 publications

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“…of neuron is N i and all these neurons are bonded to their foregoing layer. Excitation signals are applied at input layer [10]. The neurons act like a processing units aligned in a specific way to accomplish a desired output by solving a non-linear process on its inputs.…”

Section: Model Of Neuronmentioning

confidence: 99%

Fault Identification and Classification in Transmission Line by Ann Technique Using Levenberg-Marquardt Algorithms

2017

IJAERD

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Section: Model Of Neuronmentioning

confidence: 99%

Fault Identification and Classification in Transmission Line by Ann Technique Using Levenberg-Marquardt Algorithms

2017

IJAERD

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“…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

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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.

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“…In [13] the researcher has developed a complete protection scheme for a single circuit transmission line. The work presented in [14] deals with the compensation of fault resistance using ANN, it does not classify the faults. A single line to ground fault location method employing wavelet fuzzy neural network to use post-fault transient and steady-state measurements in the distribution lines of an industrial system is proposed in [15], it does not classify the faults.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore a dedicated fault location algorithm has to be developed for the double circuit transmission lines. In previous years, various fault location algorithms on double circuit transmission lines have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Phase selection algorithm based on superimposed components for protection of double circuit transmission line was proposed in [1] and [2].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Based Fault Location Estimation of Double Circuit Transmission Line

Sahu¹

2017

IJRASET

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An accurate fault location estimation algorithm based on application of artificial neural networks (ANN) for the protection of double circuit transmission lines is presented. The proposed Artificial Neural Network (ANN) protection scheme uses fundamental components of three phase voltages and current signals of both the circuits to learn the hidden relationship in the input patterns. This method is adaptive to the variation of fault resistance, fault inception angle and fault location. The Simulation results show that all types of shunt faults can be correctly locatedunder varying system conditions. Large numbers of fault simulations using MATLAB/Simulink software has proved the accuracy and effectiveness of the proposed algorithm. Keywords: Fault location (FL); Double circuit transmission line; artificial neural networks (ANN). I.INTRODUCTION Double circuit transmission lines are being most widely used because it has increased the power transmission capability and reliability of the power system. Distance relays used for protection of transmission lines have problems of under-reach, over-reach and mal-operation due to high impedance faults. Further this problem of distance relay is compounded when the distance relays are used for protection of double circuit transmission lines due to mutual coupling effect between the parallel lines. All possible types of faults on double circuit transmission line should be detected, classified and located correctly. The fault location of double circuit lines becomes more difficult and complex than a single circuit line due to the effect of mutual coupling among the circuits. When the fault location algorithm developed for single circuit lines is directly used for double circuit lines, which is often the case in practice, the fault location estimation accuracy can't be guaranteed because of the mutual coupling effect. Therefore a dedicated fault location algorithm has to be developed for the double circuit transmission lines. In previous years, various fault location algorithms on double circuit transmission lines have been developed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Phase selection algorithm based on superimposed components for protection of double circuit transmission line was proposed in [1] and [2]. A faulted-phase selector based on superimposed sequence components combined with correlation theory was presented in [3]. A fault classifier based on fault-generated high-frequency noise and artificial neural networks was proposed in [4]. In [5] the wavelet transform was used to extract the characteristics of the transient signals to construct the phase selector. In [6] Wavelet fuzzy combined approach for fault classification of a series-compensated transmission linewas presented. The fault classification and faulted-phase selection based on the Initial current travelling wave was reported in [7]. In [8] and [9] the algorithm employs the faulted circuit and healthy circuit of two-parallel line as fault location model and fault type is identified usi...

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An adaptive approach in distance protection using an artificial neural network

Cited by 38 publications

References 7 publications

Fault Identification and Classification in Transmission Line by Ann Technique Using Levenberg-Marquardt Algorithms

Fault Identification and Classification in Transmission Line by Ann Technique Using Levenberg-Marquardt Algorithms

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

Artificial Neural Network Based Fault Location Estimation of Double Circuit Transmission Line

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