Voltage sags pattern recognition technique for fault section identification in distribution networks

Mokhlis, Hazlie; Khalid, Abdul; Li, H.Y.

doi:10.1109/ptc.2009.5282161

Cited by 21 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…">Frequency component or traveling wave method [6,7]; Knowledge‐based method such as voltage‐drop‐based matching, fault current matching, and reactance to fault matching [8–13]. …”

Section: Overview Of Automated Fault Location Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

Novel wide‐area fault location and predictive maintenance technique for distribution system

Tan

Ramachandaramurthy

2011

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Locating faults in a power network has been a common chore for utility personnel. However, with the increasing demand in consumer expectations nowadays, fault location time needs to be shortened so that supply can be restored as soon as possible. Considerable amount of time can be saved if an automated mechanism is available to direct the repair crew to the region of fault. Transmission system has already adopted the automated fault location algorithm in their relay. However, implementing such an approach in wide‐area distribution network would require huge capital expenditure and resources. This paper presents a reliable, automated fault location algorithm within a wide‐area distribution network using a limited number of monitoring devices. In addition, the proposed algorithm is also capable of offering a means of predictive maintenance strategy for the utilities. The algorithm is able to identify the faulted branches in a radial distribution system with high accuracy. In most cases, the calculated fault location is very close to the actual one. This algorithm has also overcome the drawbacks of existing techniques for automated fault location in wide‐area distribution network. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

show abstract

“…">Frequency component or traveling wave method [6,7]; Knowledge‐based method such as voltage‐drop‐based matching, fault current matching, and reactance to fault matching [8–13]. …”

Section: Overview Of Automated Fault Location Techniquesmentioning

confidence: 99%

“…">The reported accuracy of fault location using knowledge‐based methods is relatively low at the moment. The authors in [10,11] reported only around 70% accuracy in identifying the correct section in the first run.…”

Section: Overview Of Automated Fault Location Techniquesmentioning

confidence: 99%

Novel wide‐area fault location and predictive maintenance technique for distribution system

Tan

Ramachandaramurthy

2011

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

show abstract

“…At the same time, different interferences can propagate and overlap between the power supply, the network and the load. All of these make the analysis and recognition of voltage sag interference more difficult [3][4][5][6][7][8]. Therefore, it is necessary to design a more efficient method to recognize the voltage sag causes, thereby solving many new problems in the power grid.…”

Section: Introductionmentioning

confidence: 99%

Recognition method of voltage sag causes based on Bi‐LSTM

Zheng

Wang

et al. 2019

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

In recent years, the power quality problem has become more complicated in power grids because of the extensive usage of power electronics and multisource multitransformation features. The method, based on physical characteristics such as time domain, frequency domain and transform domain, is facing challenges in terms of adaptability, algorithm efficiency and accuracy for the recognition of complex disturbance recognition. The bidirectional long short-term memory network is an algorithm in deep learning. It is based on data for characterization learning, which can effectively overcome the problem of information loss and generalization ability of physical methods. Moreover, it has the characteristics of memory, which can simultaneously consider historical information and future information and can better learn data features with time series characteristics. Aiming at the transient voltage sag time series data, this paper proposes a recognition method of the voltage sag causes based on the bidirectional long short-term memory network's extraction eigenvalue, the full-connection layer's high-dimensional feature extraction and the Softmax network layer's classification. The experiment uses simulation data and measured data to prove that the model has good recognition ability and good antinoise performance in the recognition of voltage sag causes and can be reliably applied in practical engineering.

show abstract

“…In [7]- [8], the technique works by matching during fault voltage sag with simulated data to eliminate multiple estimations. The location method in [7] uses the voltage sag magnitude and phase shift measured at the monitored node to compare with the simulated data and get all possible fault sections. For each section, the voltage sag function is considered to be linear.…”

Section: Introductionmentioning

confidence: 99%

“…For each section, the voltage sag function is considered to be linear. Different from [7], the nonlinearity of the function is considered in [8]. There is still deficiency in this method that the function between two adjacent node is derived using the curve fitting method according to several simulated data.…”

Section: Introductionmentioning

confidence: 99%

Fault Location for Distribution Networks Based on Node Voltage Equation

Tan

Hong-geng

2012

2012 Asia-Pacific Power and Energy Engineering Conference

View full text Add to dashboard Cite

This paper proposed a method to estimate shortcircuit fault location in electrical distribution networks using voltage sags data and current measured at the local substation. With the knowledge of bus impedance matrix, the bus voltage during the fault can be obtained for any fault in the network. Based on the non-liner profile characteristic of voltage sag measured at the bus, the paper proposed a new method. Firstly, voltage quantity for each section is expressed as a function of fault location and resistance base on the bus impedance matrix. The possible fault is estimated by incorporating the real and imaginary part of voltage sags into the node-voltage equation. Then, the method applies rank reasoning analysis to prioritize the possible fault locations due to the same electrical distance. In this method, the uncertainty of fault resistance is also considered. Through simulating for an 11kv typical test distribution network, the method's validity and accuracy are proved.

show abstract

Voltage sags pattern recognition technique for fault section identification in distribution networks

Cited by 21 publications

References 11 publications

Novel wide‐area fault location and predictive maintenance technique for distribution system

Novel wide‐area fault location and predictive maintenance technique for distribution system

Recognition method of voltage sag causes based on Bi‐LSTM

Fault Location for Distribution Networks Based on Node Voltage Equation

Contact Info

Product

Resources

About