Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system

Eri̇şti̇, Hüseyin

doi:10.1016/j.measurement.2012.07.014

Cited by 89 publications

(45 citation statements)

References 24 publications

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“…Therefore, the application based on fuzzy logic theory in fault diagnostics is closer to human thinking habits and language expression. Fuzzy logic is an effective pattern recognition method, which has been successfully used in power [43], transmission line [44], transportation [45], and industrial production [46]. Fuzzy logic mainly imitates human's logical thinking and thus has strong capability of expressing knowledge.…”

Section: Fuzzy Logic and Neuro-fuzzy Systems (Nfss)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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Section: Fuzzy Logic and Neuro-fuzzy Systems (Nfss)mentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…The authors found that the ANFIS controller is more robust to parameter variations while a pure fuzzy solution is able to regulate in minimal overshoot. ANFIS model was used by Eristi for fault diagnosis of series compensated transmission lines, too [16]. Eristi utilized Wavelet Transformation (WT) and Norm entropy to achieve effective feature extraction of the fault signals.…”

Section: Wali Et Al Compared Intelligent Controllers Such Asmentioning

confidence: 99%

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Viharos

Kis

2015

Measurement

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Both fuzzy logic, as the basis of many inference systems, and Neural Networks, as a powerful computational model for classification and estimation, have been used in many application fields since their birth. These two techniques are somewhat supplementary to each other in a way that what one is lacking of the other can provide. This led to the creation of Neuro-Fuzzy systems which utilize fuzzy logic to construct a complex model by extending the capabilities of Artificial Neural Networks. Generally speaking all type of systems that integrate these two techniques can be called Neuro-Fuzzy systems. Key feature of these systems is that they use inputoutput patterns to adjust the fuzzy sets and rules inside the model. The paper reviews the principles of a NeuroFuzzy system and the key methods presented in this field, furthermore provides survey on their applications for technical diagnostics and measurement.

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“…Time frequency‐based techniques are proposed for the protection of series compensated line, among which S‐transform and wavelet transform have drawn more attention because of their capability of representing the signals in localized time frequency domains. Intelligent systems like fuzzy logic, artificial neural network, support vector mechanism, and adaptive neuro‐fuzzy interface system are implemented along with wavelet transform and S‐transform. It is difficult to design an intelligence system as they require numerous learning cases and also consume more than one cycle to analyze the fault.…”

Section: Introductionmentioning

confidence: 99%

“…11 But it suffers from the problem of current and voltage inversions caused by series compensating circuit. Time frequency-based techniques are proposed for the protection of series compensated line, among which S-transform 12 and wavelet transform 13,14 have drawn more attention because of their capability of representing the signals in localized time frequency domains. Intelligent systems like fuzzy logic, 15,16 artificial neural network, 12,17 support vector mechanism, 18,19 and adaptive neuro-fuzzy interface system 14 are implemented along with wavelet transform and S-transform.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional curve variation approach-based quick fault detection and classification scheme for series compensated transmission line system

Moparthi

Chinta

Nakka

2018

Int Trans Electr Energ Syst

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Summary Series compensation improves the power transfer capability and the stability of a transmission system. Inclusion of series compensating devices along with their protecting equipment introduces the problems to conventional protective relaying during power system faults. In this paper, a new protection strategy is proposed based on the instantaneous active and reactive power variations. The applicability of the proposed method to a series compensated transmission system is studied with numerous fault cases. Instantaneous power quantities are measured, and respective active and reactive power quantities are reproduced onto a two‐dimensional coordinated system with reactive power as ordinate and active power as abscissa. The locus of ordered pair of instantaneous power quantities (p(t), q(t)) forms a P‐Q trajectory. The quadrant in which the starting point of P‐Q trajectory (p0(t), q0(t)) lies is computed and taken as reference. This P‐Q portrait is analyzed to detect the fault depending on the condition that the operating point shifts its quadrant under fault condition. Fault will be classified based on the polarities of extracted powers. Classification tree is formed by a set of yes/no consequences to classify the fault. The proposed method is tested for different types of faults, at distinct locations and fault inceptions on 500‐kV, 50‐Hz test system. Results thus obtained show the potentiality of proposed strategy for the protection of series compensated transmission system concerning the speed, computational burden, and mathematical complexity.

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Fault diagnosis system for series compensated transmission line based on wavelet transform and adaptive neuro-fuzzy inference system

Cited by 89 publications

References 24 publications

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Survey on Neuro-Fuzzy systems and their applications in technical diagnostics and measurement

Two-dimensional curve variation approach-based quick fault detection and classification scheme for series compensated transmission line system

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