Distribution network fault section identification and fault location using wavelet entropy and neural networks

Adewole, Adeyemi Charles; Tzoneva, Raynitchka; Behardien, Shaheen

doi:10.1016/j.asoc.2016.05.013

Cited by 81 publications

(45 citation statements)

References 26 publications

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“…Some techniques using hybrid methods for fault location in the distribution network have been proposed till now. [96][97][98][99][100][101][102][103][104][105] These methods generally use artificial intelligence techniques along with advanced signal processing techniques to extract fault information from the recorded signals and locate fault. Work done in a previous study [96] proposes a method combining wavelet theory and fuzzy logic for islanding detection and fault location in distribution systems with DGs, and the method claims to be very fast and detects fault within few microseconds from its inception.…”

Section: Hybrid Methods: Proposed Schemes and Their Challengesmentioning

confidence: 99%

“…A faulted section identification followed by fault location for the distribution network with multiple branches based on WT and ANN is proposed in a previous study. [103] In this article, WT is used for faulted section identification and ANN is used for fault location. The fault location method using the impedance-based method and voltage sag matching algorithm in a previous study [104] is applicable for single-phase faults only and the method proposed in a previous study [105] locates fault in an active distribution network using polarities of current transients.…”

Section: Hybrid Methods: Proposed Schemes and Their Challengesmentioning

confidence: 99%

“…Smart meters are used for providing data for the training of SVM that is used for fault location purpose. A faulted section identiﬁcation followed by fault location for the distribution network with multiple branches based on WT and ANN is proposed in a previous study . In this article, WT is used for faulted section identiﬁcation and ANN is used for fault location.…”

Section: Existing Fault Location Techniquesmentioning

confidence: 99%

See 2 more Smart Citations

A Literature Review on Methodologies of Fault Location in the Distribution System with Distributed Generation

Kumar

Saxena

2020

Energy Tech

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In the last decade, with increasing power supply from distributed generation (DG) to the distribution system, the traditional passive radial distribution systems transformed into an active multisource system. The nature of fault location schemes designed for the distribution system has traditionally been based on unidirectional power flow assumptions, making them ineffective for a distribution system with a higher level of DG penetration. New grid regulations require DGs to remain connected to the system in the event of a fault to provide support to the grid and improve system reliability. This makes the fault location process important as the timely location of a fault speeds up the restoration process, which improves the system reliability. Herein, providing a comprehensive review of the available techniques for fault location in the distribution system with DG penetration is focused on. An analysis is given on the shortcomings of traditional protection methods when applied to DG‐integrated distribution systems. A comparative analysis is carried out in which the merit and demerit of each technique is assessed. A discussion on some other important technical issues arising due to DG integration, such as effects on the stability of the distribution system, is also given.

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Section: Hybrid Methods: Proposed Schemes and Their Challengesmentioning

confidence: 99%

Section: Hybrid Methods: Proposed Schemes and Their Challengesmentioning

confidence: 99%

Section: Existing Fault Location Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

A Literature Review on Methodologies of Fault Location in the Distribution System with Distributed Generation

Kumar

Saxena

2020

Energy Tech

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“…Concerning the application of ANN in fault diagnosis, some approaches to fault identification should be mentioned [5][6][7][8][9]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [5], both the extracted discrete wavelet transform (DWT) and wavelet energy entropy (WEE) are used as input of the ANN for fault diagnosis and location. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis model based on Bayesian network considering information uncertainty and its application in traction power supply system

Lin

Chen

Wang

2018

IEEJ Transactions Elec Engng

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Fault diagnosis is of great significance in maintaining the safe and stable operation of a system. Fast and precise location of faults is important for restoring power supply systems. In this paper, considering the interference of information uncertainty to fault diagnosis under different weather conditions, a fault diagnosis model based on Bayesian network is proposed. The model inherits the capability of processing uncertain information of a Bayesian network and divides the information into historical information and evidence information. The uncertainty of historical information is reduced by modifying the parameters through improving parameter learning formulas, while that of evidence information can be reduced by logical judgment. A fault simulation model based on Monte Carlo method is used to generate simulation data for parameter learning, and a case study confirms the correctness of the proposed fault diagnosis and fault simulation models.

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Research on line overload identification of power system based on improved neural network algorithm

Yang

Luo

Lin

et al. 2020

Concurrency and Computation

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Due to the continuous appearance of safety fault accidents in the practice process, operation safety has become the central task of various operation and management tasks of the power grid. Therefore, to establish a line overload identification and data control model for the power system, we first defined the vulnerability of complex power systems based on the analysis of each line and node. For finding the optimal parameters of this model, we proposed an improved optimization strategy by combining the genetic algorithm and BP neural network. To verified the effectiveness of our proposed method, we conducted experiments on a simulation on the IEEE 30-node power system environment. Experimental results demonstrate that the proposed algorithms can establish an optimized overload identification model with better performance. This study can help to conduct reasonable adjustment when overload happens to the power system, and then reduce similar failure as well as enhance the operation safety. K E Y W O R D S BP neural network, genetic algorithm, overload, power system 1 INTRODUCTION The power system is a combination of network space, physical space, and social space. Large-scale grid fault accidents will cause severe safety loopholes. Therefore, the operation safety of the power grid is an essential issue in the development of modern society. In practice, most power grid accidents originate from line overload of the power system, component failure, and similar problems. 1 To reduce such events, power grid enterprises should not only enhance daily maintenance but also need to improve the identification and survival ability of the power grid in emergencies. 2 With the development of information and communication technology, the Internet of Things has been extensively applied to the power grid, and the differential privacy protection policy can effectively protect the data security of power grid transfer. 3 Meanwhile, it would be better to detect corresponding important nodes and vulnerable lines and to reinforce the maintenance in advance before safety accidents happen, which can reduce economic losses and social disruption. 4 Many power safety management problems in power grid system can be converted into optimization problems through mathematical modeling. Some are multi-objective optimization problems, and high-performance computing at a faster speed is needed to solve these problems. 5 Line overload identification is still a difficulty for the safety of the power system. Therefore, we proposed an improved back propagation (BP) neural network based on the genetic algorithm (GA) to tackle the line overload problem in the power grid system. To establish a line overload identification and data control model for the power system, we first defined the vulnerability of complex power systems based on the analysis of each line and node. For optimizing this model, we take the advantages of combining the genetic algorithm and BP neural network. It is worth noting that the original BP neural network usually has the problem of s...

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Distribution network fault section identification and fault location using wavelet entropy and neural networks

Cited by 81 publications

References 26 publications

A Literature Review on Methodologies of Fault Location in the Distribution System with Distributed Generation

A Literature Review on Methodologies of Fault Location in the Distribution System with Distributed Generation

Fault diagnosis model based on Bayesian network considering information uncertainty and its application in traction power supply system

Research on line overload identification of power system based on improved neural network algorithm

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