A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Yadav, Anamika; Swetapadma, Aleena

doi:10.1016/j.ijepes.2014.12.079

Cited by 72 publications

(39 citation statements)

References 24 publications

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“…The physical interpretation of ψ is revealed by the two components in (8). The dominant element is ∆I u , which is a 2-sparse vector with nonzero values exactly corresponding to the terminal buses of the faulted line.…”

Section: B Physical Interpretation Of the Featuresmentioning

confidence: 99%

“…Prior methods in this context can be W. Li categorized into two classes: 1) conventional, and 2) widearea algorithms. The conventional algorithms for use in oneend, double-end and multi-end transmission lines include impedance-based, traveling-wave based and artificial intelligent methods [3]- [8]. The impedance-based method computes the fault currents according to the measured voltages and known bus impedance and then determines the fault distance to the measured point using distributed parameters [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Deka

Chertkov

et al. 2019

IEEE Trans. Power Syst.

133

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Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availability. This paper proposes a faulted line localization method based on a Convolutional Neural Network (CNN) classifier using bus voltages. Unlike prior data-driven methods, the proposed classifier is based on features with physical interpretations that improve the robustness of the location performance. The accuracy of our CNN based localization tool is demonstrably superior to other machine learning classifiers in the literature. To further improve the location performance, a joint phasor measurement units (PMU) placement strategy is proposed and validated against other methods. A significant aspect of our methodology is that under very low observability (7% of buses), the algorithm is still able to localize the faulted line to a small neighborhood with high probability. The performance of our scheme is validated through simulations of faults of various types in the IEEE 39-bus and 68bus power systems under varying uncertain conditions, system observability, and measurement quality.

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Section: B Physical Interpretation Of the Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Deka

Chertkov

et al. 2019

IEEE Trans. Power Syst.

133

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show abstract

“…The UPFC modelling and controller design presented in the scheme is referred [24], [25]. Spectral Energy [26] based one terminal (sending end bus) is the key point for deciding the fault pattern and classification. The relaying theory based application is referred [27].…”

Section: Studied Systemmentioning

confidence: 99%

Fault Detection & Classification in UPFC Integrated Transmission Line Using DWT

Mishra¹,

Swain²,

Tripathy³

2017

IJPEDS

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Fault detection and classification in UPFC (Unified Power Flow Controller) integrated transmission line using single terminal based DWT (Discrete Wavelet Transform) is proposed. The current is extracted from the sending end bus and processed through wavelet transform to evaluate the spectral energy (SE) using db4 mother wavelet. Three level decomposition is framed to extract the fundamental frequency component from non-stationary signal, considering sampling frequency of 2kHz system. The fundamental frequency component of respective phase currents are used to compute SE at sending end. The SE of individual phase current is the key factor for deciding the fault pattern detection and classification. The advantage of using this it requires less cost and protect entire transmission line with minimal fault detection time.are simulated by considering the parameter like fault resistance, source impedance, fault inception angle, multi-location fault, reverse power flow and UPFC system parameter. The scheme works reliable and efficient to detect and classify the fault within a cycle of time period 20ms.

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“…There are some shortcomings with these methods regarding computational speed, accuracy, and reliability of the whole process. The discrete wavelet transform and continuous wavelet transform along with ANN have also been implemented in this context described in Yadav and Swetapadma and Lala and Karmakar for transmission and distribution systems respectively. Some researchers have proposed impedance‐based fault detection and localization techniques using principal component analysis for distribution systems.…”

Section: Introductionmentioning

confidence: 99%

Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach

Lala

Karmakar

Ganguly

2018

Int Trans Electr Energ Syst

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Summary The paper presents a Stockwell transform (ST) and artificial neural network‐based approach for the detection and localization of faults in distribution systems considering the complexities of network architecture and the distributed generation (DG) integration. Firstly, a faulty‐line detection technique is developed based upon the total harmonic distortion of the fault current signal captured from the line ends. Then, the ST coefficients of the faulty signal are used as an attribute to classify the fault. Finally, the root‐mean‐square values calculated from the ST coefficients are used for the fault localization. The algorithm is tested on an IEEE 13‐bus unbalanced and a 52‐bus practical Indian balanced distribution system with DG. The results are compared with some existing algorithms. The comparisons and the test results show that the proposed technique can be a useful tool in detecting the faults in complicated fault scenarios.

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A single ended directional fault section identifier and fault locator for double circuit transmission lines using combined wavelet and ANN approach

Cited by 72 publications

References 24 publications

Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

Fault Detection & Classification in UPFC Integrated Transmission Line Using DWT

Detection and localization of faults in smart hybrid distributed generation systems: A Stockwell transform and artificial neural network-based approach

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