Particle swarm optimization based K-means clustering approach for security assessment in power systems

Kalyani, Sheetal; Swarup, K. Shanti

doi:10.1016/j.eswa.2011.02.086

Cited by 102 publications

(46 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It begins by fixing the number of clusters K and their corresponding centroids [38]. Then, each data is classified into a cluster when its distance to the centroid defining that class is a minimum.…”

Section: K-means Clusteringmentioning

confidence: 99%

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Vega

Jaramillo-Morán

2018

Water

View full text Add to dashboard Cite

Abstract:The oxidation-reduction potential (ORP) and the dissolved oxygen (DO) have been monitored in a municipal wastewater treatment plant (WWTP). Three thousand two hundred aeration-non-aeration cycles were recorded. They were analyzed by defining 16 parameters to characterize each one of them. The vectors so obtained were treated with the box-plot tool to reject those with outliers (abnormally high or low values). The remaining data were processed by a neural network (self-organizing map: SOM) in order to classify them into classes and to obtain relations between parameters to identify those more representative of the system dynamics. They were: the oxygen uptake rate (OUR), the oxygen rise average slope (ORAS), and the oxidation-reduction potential "arrow" (ORP arrow , the maximum distance between the ORP curve and its linearization). Finally, the classes obtained from SOM were grouped into four macro-classes by means of the K-means algorithm in order to define four operation states related to seasonal and load characteristics, which may be taken into account, along with the key parameters, in the WWTP management with the aim of improving the nutrient removal performance by adapting their controllers to seasonal and load variations.

show abstract

Section: K-means Clusteringmentioning

confidence: 99%

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Vega

Jaramillo-Morán

2018

Water

View full text Add to dashboard Cite

show abstract

“…K-means is one of the most widely used partition-based clustering algorith ms in practice. It is simp le, easy, understandable, scalable, and can be adapted to deal with streaming data and very large datasets [20]. Kmeans algorith m d ivides a dataset X into k disjoint clusters based on the dissimilarities between data objects and cluster centroids.…”

Section: K-means Algorithmmentioning

confidence: 99%

DIMK-means “Distance-based Initialization Method for K-means Clustering Algorithm”

Aldahdooh¹,

Ashour²

2013

IJISA

View full text Add to dashboard Cite

Abstract-Partition-based clustering technique is one of several clustering techniques that attempt to directly decompose the dataset into a set of disjoint clusters. Kmeans algorith m dependence on partition-based clustering technique is popular and widely used and applied to a variety of domains. K-means clustering results are extremely sensitive to the initial centroid; this is one of the major drawbacks of k-means algorithm. Due to such sensitivity; several d ifferent init ialization approaches were proposed for the K-means algorith m in the last decades. This paper proposes a selection method for init ial cluster centroid in K-means clustering instead of the random selection method. Research provides a detailed performance assessment of the proposed initialization method over many datasets with different dimensions, numbers of observations, groups and clustering complexit ies. Ability to identify the true clusters is the performance evaluation standard in this research. The experimental results show that the proposed initialization method is more effective and converges to mo re accurate clustering results than those of the random initialization method.

show abstract

“…Clustering is a process of partitioning or grouping a set of data objects into a number of clusters such that similar patterns are assigned to one cluster [24]. K-means clustering is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem.…”

Section: Multi-state Probabilistic Model Based On Clusteringmentioning

confidence: 99%

A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on Clustering Algorithm

Liu¹,

Chen²,

Zhang³

et al. 2014

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

-A reliability model of wind farm located in mountainous land with complex terrain, which considers the cable and wind turbine (WT) failures, is proposed in this paper. Simple wake effect has been developed to be applied to the wind farm in mountainous land. The component failures in the wind farm like the cable and WT failures which contribute to the wind farm power output (WFPO) and reliability is investigated. Combing the wind speed distribution and the characteristic of wind turbine power output (WTPO), Monte Carlo simulation (MCS) is used to obtain the WFPO. Based on clustering algorithm the multi-state model of a wind farm is proposed. The accuracy of the model is analyzed and then applied to IEEE-RTS 79 for adequacy assessment.

show abstract

Particle swarm optimization based K-means clustering approach for security assessment in power systems

Cited by 102 publications

References 14 publications

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

Obtaining Key Parameters and Working Conditions of Wastewater Biological Nutrient Removal by Means of Artificial Intelligence Tools

DIMK-means “Distance-based Initialization Method for K-means Clustering Algorithm”

A Reliability Model of Wind Farm Considering the Complex Terrain and Cable Failure Based on Clustering Algorithm

Contact Info

Product

Resources

About