A least square support vector machine-based approach for contingency classification and ranking in a large power system

Soni, Bhanu Pratap; Saxena, Akash; Gupta, Vikas

doi:10.1080/23311916.2015.1137201

Cited by 18 publications

(9 citation statements)

References 17 publications

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“…In addition, larger value of exponent gives rise to nonlinearity. Accordingly, former research investigations disclosed that optimum integer selection for exponent will highly influence the PI calculation and further affects the contingency ranking [18].…”

Section: State Space Enumeration Methodsmentioning

confidence: 99%

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

K¹,

Madhusudan

2018

IJECE

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Composite power system reliability involves assessing the adequacy of generation and transmission system to meet the demand at major system load points. Contingency selection was being the most tedious step in reliability evaluation of large electric systems. Contingency in power system might be a possible event in future which was not predicted with certainty in earlier research. Therefore, uncertainty may be inevitable in power system operation. Deterministic indices may not guarantee the randomness in reliability assessment. In order to account for volatility in contingencies, a new performance index proposed in the current research. Proposed method assimilates the uncertainty in computational procedure. Reliability test systems like Roy Billinton Test System-6 bus system and IEEE-24 bus reliability test systems were used to test the effectiveness of a proposed method.

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Section: State Space Enumeration Methodsmentioning

confidence: 99%

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

K¹,

Madhusudan

2018

IJECE

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“…In recent years application of SVMs in classification problems has increased due to its capability of segregation of datasets by the best hyperplane. SVMs are applied for multidimensional data classification [ 14 ], classification of microarrays [ 15 ], wind speed prediction [ 16 ], voltage stability monitoring [ 17 ], classification of power quality events [ 18 ], and contingency ranking [ 19 ]. The main reason behind this popularity of the SVMs as a classifier is that SVM can handle large feature space.…”

Section: Design Of Supervised Learning Modelmentioning

confidence: 99%

Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

Saxena

Shekhawat

2017

Journal of Environmental and Public Health

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With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

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“…Stability assessment may be broadly classified as static stability assessment (SSA) and transient stability assessment (TSA). The SSA methods are based on the operating conditions like loading and line outage disturbances [2], while TSA methods depend on the nature of the generators under different contingencies [3].…”

Section: Introductionmentioning

confidence: 99%

Transient stability‐oriented assessment and application of preventive control action for power system

Soni

Saxena

Gupta

et al. 2019

J. eng.

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A least square support vector machine-based approach for contingency classification and ranking in a large power system

Cited by 18 publications

References 17 publications

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

Probabilistic Performance Index based Contingency Screening for Composite Power System Reliability Evaluation

Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

Transient stability‐oriented assessment and application of preventive control action for power system

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