Transmission line fault classification using modified fuzzy
            <i>Q</i>
            learning

Malik, Hasmat; Sharma, Rajneesh

doi:10.1049/iet-gtd.2017.0331

Cited by 39 publications

(13 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The indicators can be used to simulate the instrument output devices and displays data accordingly. The block diagram gives the graphical representation of source code with provision of functions and structures from built‐in libraries …”

Section: Fault Detection Methodsmentioning

confidence: 99%

“…Figure depicts the interfacing facility between the host computer 1 (MATLAB) and host computer 2 (LabVIEW). The data exchange between the host computers can be done through the TCP/IP communication facility …”

Section: Fault Detection Methodsmentioning

confidence: 99%

“…Because of these aforementioned advantages, the proposed and also many literature works carried out the pre‐processing of signal using the DWT analysis for training the classifiers such as ANN, Fuzzy, Modified Fuzzy Q‐learning, Fuzzy‐ARTMAP and ANFIS classifier. The work in presents classification of all the conventional faults such as LG, LL, LLG and LLLG fault by using the classifier such as ANN, ANFIS, Chebyshev Neural Network (Ch‐NN), Decision tree, Fuzzy and Modified Fuzzy Q‐learning, and multi‐layer perceptron (MLP) neural network with its accuracy of classification lies in the range of 92% to 100%. Nevertheless, all these works, presents the identification and location of conventional fault without considering the HIF.…”

Section: Comparison With Literature Workmentioning

confidence: 99%

See 2 more Smart Citations

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Veerasamy

Wahab

Vinayagam³

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper proposes a discrete wavelet transform (DWT)‐based Graphical Language classifier algorithm for identification of high‐impedance fault (HIF) in medium voltage (MV) distribution network of 13.8 kV. The proposed method of classifier is developed using virtual instrumentation LabVIEW facility, for detection of various faults such as symmetrical, unsymmetrical, and HIF in the system. Initially, the MV distribution feeder network has been modeled in MATLAB/Simulink, and the DWT analysis has been carried out with the introduction of various faults in the network to extract the features. The extracted features such as SD and energy values from the fault current signals have been applied to the proposed classifier algorithm to identify the type of fault. The effectiveness of the presented method has been tested and compared with the similar conventional fuzzy‐based approach. The results indicate that the proposed classifier algorithm outperforms to give 100% accuracy, while the fuzzy‐based approach misclassifies the double line to ground fault (LLG), three‐phase fault (LLLG), and HIF. Furthermore, the proposed algorithm with LabVIEW facility is more flexible and can be implemented in real time using data acquisition unit for obtaining fault current signal from power system.

show abstract

Section: Fault Detection Methodsmentioning

confidence: 99%

Section: Fault Detection Methodsmentioning

confidence: 99%

Section: Comparison With Literature Workmentioning

confidence: 99%

See 1 more Smart Citation

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Veerasamy

Wahab

Vinayagam³

et al. 2020

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…In [8], a method for detecting and classifying faults based on computational intelligence is presented, where fault identification is accomplished by stochastic components of the voltage and current signals. Fault identification is also tackled using fuzzy techniques [9][10][11] reaching 99% in some cases of accuracy. Such methods do not require a training process, but their generalisation is more complex.…”

Section: Introductionmentioning

confidence: 99%

Fault detection and classification in transmission lines based on a PSD index

Guillén

Paternina²,

Ortiz-Bejar

et al. 2018

IET Generation, Transmission & Distribution

View full text Add to dashboard Cite

“…It is characterized by its ability to track the transient phenomena that associates the faults [2]. Artificial intelligence techniques such as Artificial Neural Network (ANN), Fuzzy, and Artificial Neural Network Fuzzy inference system (ANFIS), have an extensive usage in faults detection and classification process in power transmission line [3][4][5][6][7]. Hybrid techniques were coming out to overcome the drawbacks of one approach during its application.…”

Section: Introductionmentioning

confidence: 99%

A cumulative standard deviation sum based method for high resistance fault identification and classification in power transmission lines

Musa

et al. 2018

Prot Control Mod Power Syst

View full text Add to dashboard Cite

High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification. In this paper, the standard deviation and accumulation method are employed to perform the fault detection and classification. It is primarily built in two stages. Firstly, the standard deviations for the measured current's signals of the local and remote terminals is computed to extract the fault feature. Secondly, the cumulative approach is used to enlarge the fault feature to perform the high resistance fault. The proposed scheme is known as Standard Deviation Index (SDI), and it is obtained for the three phases and zero sequence. The proposed algorithm has been tested through different fault circumstances such as multiple faults locations, fault resistances, and fault inception time. Moreover, far-end faults with high-resistance, faults happened nearby the terminal, faults considering variable loading angle, sudden load change, different sampling frequency, bad signaling and a fault occurred in the presence of series compensation are also discussed. The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to 1.5kΩ and can be detected within a millisecond after the fault inception. Additionally, the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.

show abstract

Transmission line fault classification using modified fuzzy Q learning

Cited by 39 publications

References 20 publications

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

A novel discrete wavelet transform‐based graphical language classifier for identification of high‐impedance fault in distribution power system

Fault detection and classification in transmission lines based on a PSD index

A cumulative standard deviation sum based method for high resistance fault identification and classification in power transmission lines

Contact Info

Product

Resources

About