k-means algorithm and mixture distributions for locating faults in power systems

Flórez, Juan José Mora; Cormane, Jorge; Ordóñez-Plata, Gabriel

doi:10.1016/j.epsr.2008.10.011

Cited by 37 publications

(12 citation statements)

References 7 publications

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“…One of the cluster techniques used is the k-means algorithm, as it is easy to apply, simple and efficient (Anil, 2010). It has been documented that the k-means algorithm is applicable in many fields such as engineering, energy, medical, electrical and transport (Chen et al, 2008;Docquier et al, 2009;Mora-Flórez et al, 2009;Pandit et al, 2011;Yiakopoulos et al, 2011). Cluster analysis uses classified data that is divided into meaningful subgroups (Fraley and Raftery, 1998).…”

Section: Methodsmentioning

confidence: 99%

Newcastle Traffic Classification Using Clustering Algorithms

Matar¹,

Almutairi²,

Al-Mutairi³

2020

American Journal of Engineering and Applied Sciences

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The urban road traffic network evolution is complex and varies depend on road type, zoning types and social activities. Typical traffic pattern variation of road network could be examined by considering the daily human travel activities. Thus, factor and cluster analysis is carried out. This paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads based on traffic profile. The analysis was carried out using data available from 45 Automatic Traffic Recorder (ATR) sites in Newcastle, UK. Factor and cluster analysis were applied on the road traffic data so that roads could be classified, allowing diurnal traffic profiles to be assigned a group to roads with similar attributes. These groups could be classify based on road traffic characteristics. Five road classifications were found.

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Section: Methodsmentioning

confidence: 99%

Newcastle Traffic Classification Using Clustering Algorithms

Matar¹,

Almutairi²,

Al-Mutairi³

2020

American Journal of Engineering and Applied Sciences

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“…To simplify (11), let A = (H T R -1 H) and name it the information matrix. Consider E(dd T ) = R (R is the measure-ment error variance matrix).…”

Section: Parameter Estimation Error Analysismentioning

confidence: 99%

“…The above methods are commonly used in end-to-end or simple structured transmission lines. Some new algorithms have been proposed for multi-branch hybrid transmission lines: À improved methods based on traditional methods [9,10];`fault locating methods based on pattern recognition techniques such as Artificial Neural Networks and K-means clustering algorithms [11]; and´a method based on dynamic state estimation is proposed [12,13]. The principle of the first type of methods is first determining the fault branch and then locating the fault point based on the determined fault branch by the traditional method.…”

Section: Introductionmentioning

confidence: 99%

A fault locating method for multi-branch hybrid transmission lines in wind farm based on redundancy parameter estimation

Zhang

Zhu

Liu

2019

J. Mod. Power Syst. Clean Energy

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In order to solve the problem of ''abandoned'' wind caused by short circuit faults in a wind farm, a wind farm fault locating method based on redundancy parameter estimation is proposed. Using the characteristics of the traveling wave, transmission equations containing the position of the fault point are constructed. Parameter estimation from statistical theory is used to solve the redundant transmission equations formed by multiple measuring points to locate the faults. In addition, the bad data error detection capability of the parameter estimation is used to determine bad data and remove them. This improves locating accuracy. A length coefficient is introduced to solve the error enlargement problem caused by a transmission line sag. The proposed fault locating method can solve the fault branch misjudgment problem caused by the short circuit faults near the data measuring nodes of the wind farm based on the proposed fault interval criterion. It also avoids the requirements to the traveling wave speed of traditional methods, thus its fault location is more accurate. Its effectiveness is verified through simulations in PSCAD/ EMTDC, and the results shows that it can be used in the fault locating of hybrid transmission lines.

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“…In the present paper, the authors have simulated IEEE 13-node distribution system using PSCAD which is an unbalanced system and current samples are generated at the substation end. The current samples are subjected to FCM to obtain clusters and fed to expectation maximization algorithm [13]. The paper presents an alternative solution to the problems associated with interruptions by means of a statistical modeling of current sample database applied to determine the fault location in power distribution systems to reduce the system restoration time.…”

Section: Introductionmentioning

confidence: 99%

FCM and Statistical Based Approach for Classification and Location of Faults in Electrical Distribution System

Arvind

Maheswari

2011

IJPSOEM

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Electric Power Distribution System is a complex network of electrical power system. Also, large number of lines on a distribution system experiences regular faults which lead to high value of current. Speedy and precise fault location plays a pivotal role in accelerating system restoration which is a need of modern day. Unlike transmission system which involves a simple connection, distribution system has a very complicated structure thereby making it a herculean task to design the network for computational analysis. In this paper, the authors have simulated IEEE 13- node distribution system using PSCAD which is an unbalanced system and current samples are generated at the substation end. A Fuzzy c-mean (FCM) and statistical based approach has been used. Samples are transformed as clusters by use of FCM and fed to Expectation- Maximization (EM) algorithm for classifying and locating faults in an unbalanced distribution system. Further, it is to be kept in mind that the combination has not been used for the above purpose as per the literature available till date.

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k-means algorithm and mixture distributions for locating faults in power systems

Cited by 37 publications

References 7 publications

Newcastle Traffic Classification Using Clustering Algorithms

Newcastle Traffic Classification Using Clustering Algorithms

A fault locating method for multi-branch hybrid transmission lines in wind farm based on redundancy parameter estimation

FCM and Statistical Based Approach for Classification and Location of Faults in Electrical Distribution System

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