Fault location using synchronized sequence measurements

Chun, Wang; Jia, Qingquan; Li, Xinbin; Dou, Chunxia

doi:10.1016/j.ijepes.2007.06.027

Cited by 38 publications

(10 citation statements)

References 14 publications

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“…Since the bus impedance matrix for any system during the fault instant can be seen as a constant matrix, the rule of superposition can be applied for multi-phase faults and multiple faults in the system, their equivalent bus injections will appear in multiple phases or multiple terminal buses instead of a single pair in the case of single phase to ground fault. It has to be clarified that although the equivalent injection representations of fault have been built from scratch by the authors, it also appears in literatures such as [28] and has been utilized in a different formulation.…”

Section: Derivation Of Equivalent Injectionmentioning

confidence: 99%

Fault Location Using Wide-Area Measurements and Sparse Estimation

Feng

Abur

2016

IEEE Trans. Power Syst.

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Section: Derivation Of Equivalent Injectionmentioning

confidence: 99%

Fault Location Using Wide-Area Measurements and Sparse Estimation

Feng

Abur

2016

IEEE Trans. Power Syst.

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“…The HMI provides important information about the current state of the network. El-Zonkoly (2006), Wang et al (2008) and Castillo et al (2008) (PSO) and gravitational search algorithm (GSA), both inspired from nature. PSO assumes that a group of birds can be distributed randomly over an area for food search, while the food is concentrated only at certain locations.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of phasor measurement units to obtain network observability using a hybrid PSO–GSA algorithm

Aminzadeh

Miri

2015

Australian Journal of Electrical and Electronics Engineering

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a department of Electrical Engineering, Payame noor university, 19395-4697, tehran, iran; b department of Electrical Engineering, university of science and technology, tehran, iran ABSTRACT The rapid growth and expansion of power grids makes analysis and control of such systems a complicated task. Control, management and stability analysis of a power network needs sufficient knowledge of its current state. The traditional measurement system gives little consideration onto the required costs and is no longer a possibility. Phasor measurement units (PMUs) can effectively construct an optimal measurement system to obtain network observability. These units can potentially detect any fault in normal or contingency conditions. Optimal placement of PMUs is very important to minimize the number of costly and unnecessary PMU installations at different buses. In this study, we merged the abilities of particle swarm optimization (PSO) in exploitation and gravitational search algorithm (GSA) in exploration to offer a high-speed hybrid PSO-GSA algorithm for optimal placement of PMU units. The effectiveness of the proposed algorithm has been compared to the prevailing techniques. ARTICLE HISTORY

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“…Fault location in transmission lines are presented, using the calculation of fault distance phasor at voltage and current at one of the line terminals, and also the system equivalents at both line terminals [17]. Fault location formulas using synchronized sequence voltage and current at two-ends of faulted transmission lines are presented in [18]. Artificial neural networks [19] are used to classify and locate the faults and wavelet transform [20] is also used to identify and classify the faults.…”

Section: Introductionmentioning

confidence: 99%

Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

Gayathri¹,

Kumarappan²

2014

International Journal of Computational Intelligence Systems

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A new algorithm is developed to enhance the solution for the problems associated with double circuit transmission lines for the mutual coupling between the two circuits under fault conditions and which is highly variable in nature. The algorithm depends on the three-line voltages and the six line currents of double circuit lines at one end. It relies on the application of Support Vector Machine (SVM) and frequency characteristics of the measured single end positive sequence voltage and current measurement of transient signals of the system. Fault resistance, mutual coupling between two circuits and initial prefault conditions are considered. The accuracy of this method has been assessed using a fault simulation software program. In the first state, the accuracy of the method was determined on the basis of SVM reconstructed method. In the second state, this method utilizes voltage and current data acquired at one common end of the two lines. This paper proposes a new hybrid approach for fault location on Extra High Voltage (EHV) lines using RBF based SVM with reconstructed input and Scaled Conjugate Gradient (SCALCG) based neural network method. Sample inputs are determined by MATLAB. The average error fault location in 400kV and 150km line is tested and the results prove that the proposed method is effective and reduces the error within a short duration of time using both RBF based reconstructed input of SVM and SCALCG based neural network.

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Fault location using synchronized sequence measurements

Cited by 38 publications

References 14 publications

Fault Location Using Wide-Area Measurements and Sparse Estimation

Fault Location Using Wide-Area Measurements and Sparse Estimation

Optimal placement of phasor measurement units to obtain network observability using a hybrid PSO–GSA algorithm

Double Circuit EHV Transmission Lines Fault Location with RBF Based Support Vector Machine and Reconstructed Input Scaled Conjugate Gradient Based Neural Network

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