Transmission line protection scheme using Wavelet based alienation coefficients

Gafoor, Shaik Abdul; Yadav, Sandeep; Prashanth, P.; Krishna, T.V.Rama

doi:10.1109/pecon.2014.7062409

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…An asymmetrical fault line searching and locating scheme is developed using the fault direction distinguishing method and its associated communication system. A more up-to-date method for locating faults in a distribution network that includes DG units is the multi-layer perceptron neural network (MLPNN) (Jiang et al, 2003;Gafoor et al, 2014). As a result of the MLPNN's structure and training algorithm, however, its speed is not ideal for applications requiring rapid and precise fault finding (Kordestani et al, 2016;Bayrak, 2018).…”

Section: Introductionmentioning

confidence: 99%

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Gogula

Edward²

2023

Front. Energy Res.

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High Impedance Fault detection in a solar photovoltaic (PV) and wind generator integrated power system is described in this paper using discrete wavelet transform and a neural network with radial basis function (NNRBF). For this paper, the integration of solar photovoltaic and wind systems was modelled in a MATLAB/Simulink environment to create an IEEE 13-bus system. Microgrids (MG’s) are mostly powered by renewable energy. Uncertainty about renewables has shifted attention to ensuring a steady supply and long-term viability. It has been addressed in the paper whether or not a small-scale distant end source connection may be made at the terminal of a radial distribution feeder. Some typical power system problems compromise the reliability of the grid’s power supply. To solve this problem, this study suggests a criterion algorithm based on the neural network with radial basis function (NNRBF), and a defect detection method based on the discrete wavelet transform (DWT). The MATLAB/Simulink model of the system is then used to produce fault and travelling wave signals. The db4 wavelet is used to deconstruct the travelling wave signals into detail and approximate signals, which are then combined with the data from the two-terminal travelling wave localization approach for fault detection. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). The results show that both the criterion algorithm and the fault detection algorithm are reliable in their assessments of whether or not faults exist in the power system, and that neither algorithm is particularly sensitive to variations in fault type, fault detection, fault initial angle, or transition resistance. After that, the optimal maximum coefficients of the wavelets are extracted and fed into the proposed radial basis function neural network (NNRBF). Overhead distribution system faults are simulated in Matlab/Simulink, and the technique is rigorously validated across a wide range of system situations. It has been shown through simulations that the proposed method can be relied upon to successfully and dependably protect high impedance fault (Hi-Z).

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Section: Introductionmentioning

confidence: 99%

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Gogula

Edward²

2023

Front. Energy Res.

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“…An alienation (AL) coefficient based network protection technique using local bus data, is developed by Masoud and Mahfouz [17]. The wavelet transform based AL coefficients, which were obtained from local buses, are utilized to develop the network protection method by Gafoor, et al [18]. References [19], [20] and [21] presented transmission line protection schemes, which utilize wavelet depended alienation technique.…”

Section: Introductionmentioning

confidence: 99%

Protection Scheme using Wavelet-Alienation-Neural Technique for UPFC Compensated Transmission Line

Rathore¹,

Mahela²,

Khan

et al. 2021

IEEE Access

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Fault analysis (detection, classification and location) of transmission network is of great importance in power system. A Wavelet-Alienation-Neural (WAN) technique has been developed for the fault analysis of Unified Power Flow Controller (UPFC) compensated transmission network. The detection and classification of various outages are accomplished by alienation of wavelet based approximate coefficients computed from current signals. The precise location of faults is carried out by an Artificial Neural Network fed from estimated approximate coefficients computed from voltage and current signals of the same quarter cycle. The robustness of the algorithm is proved with the case studies of varying fault locations, sampling frequency, system parameters, effects of noise, fault incipient angle, different control strategies and fault path impedances. INDEX TERMS Fault detection, fault classification, fault location, unified power flow controller (UPFC).

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“…Wavelet transform not only has good overall waveform analysis ability but also has outstanding time-frequency localization analysis ability. It can effectively obtain the detailed information of the signal in the time and frequency domains and has the ability to determine the singular point of the signal and analyze the degree of signal distortion [4,5]. In [6], wavelet transform was applied to fault detection of the fars power distribution system, which improved detection accuracy and achieved good results.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

Wang

2020

Complexity

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The safety and stability of the power supply system are affected by some faults that often occur in power system. To solve this problem, a criterion algorithm based on the chaotic neural network (CNN) and a fault detection algorithm based on discrete wavelet transform (DWT) are proposed in this paper. MATLAB/Simulink is used to establish the system model to output fault signals and travelling wave signals. Db4 wavelet decomposes the travelling wave signals into detail signals and approximate signals, and these signals are combined with the two-terminal travelling wave location method to achieve fault location. And the wavelet detail coefficients are extracted to input to the proposed chaotic neural network. The results show that the criterion algorithm can effectively determine whether there are faults in the power system, the fault detection algorithm has the capabilities of locating the system faults accurately, and both algorithms are not affected by fault type, fault location, fault initial angle, and transition resistance.

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Transmission line protection scheme using Wavelet based alienation coefficients

Cited by 11 publications

References 7 publications

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Fault detection in a distribution network using a combination of a discrete wavelet transform and a neural Network’s radial basis function algorithm to detect high-impedance faults

Protection Scheme using Wavelet-Alienation-Neural Technique for UPFC Compensated Transmission Line

Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform

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