Reliability issues in today's electric power utility environment

Billinton, R.; Salvaderi, L.; McCalley, James D.; Chao, Henry; Seitz, T.; Allan, R.N.; Odom, J.; Fallon, Corey K.

doi:10.1109/59.627880

Cited by 110 publications

(41 citation statements)

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“…But the drawback with deterministic criteria is that they do not reflect the stochastic or probabilistic nature of the system in terms of load profiles, component availability, failures etc. (Billinton et al 1997). Therefore the need to incorporate probabilistic or stochastic techniques to assess power system reliability and obtain suitable indices or guidelines for planning has been recognized by the power system planners and operators; and several such techniques have been developed (Beshir 1999;Chowdhury and Koval 2006;Li and Choudhury 2007;Wan et al 2000;Xiao and McCalley 2007).…”

Section: Introductionmentioning

confidence: 98%

Enhanced Power System Security Assessment Through Intelligent Decision Trees

Krishnan

2014

Complex System Modelling and Control Through Intelligent Soft Computations

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Power system security assessment involves ascertaining the post-contingency security status based on the pre-contingency operating conditions. A system operator accomplishes this by the knowledge of critical system attributes which are closely tied to the system security limits. For instance, voltage levels, reactive power reserves, reactive power flows are some of the attributes that drive the voltage stability phenomena, and hence provide easy guidelines for the operators to monitor and maneuver the highly stressed power system to a secure state. With tremendous advancements in computational power and machine learning techniques, there is increased ability to produce security guidelines that are highly accurate and robust under a wide variety of system conditions. Particularly, the decision trees, a data mining tool, has lend itself well in extracting highly useful and succinct knowledge from a very large repository of historical information. The most vital and sensitive part of such a decision tree based security assessment is the stage of training database generation, a computationally intensive process which involves sampling many system operating conditions and performing power system contingency assessment simulations on them. The classification performance of operating guidelines under realistic testing scenarios depend heavily on the quality of the training database used to generate the decision trees. So the primary objective of this chapter is to develop an improvised database generation process that creates a satisfactory training database by sampling the most influential operating conditions from the input operating parameter state space prior to the stage of power system contingency simulation. Embedding such intelligence to the system scenario sampling process enhances the information content in the training database, while minimizing the computing requirements to generate it. This chapter will clearly explain and demonstrate the process of identifying such high information contained sampling space and the advantage of deriving security guidelines from decision trees that exclusively use such an enhanced training database.

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Section: Introductionmentioning

confidence: 98%

Enhanced Power System Security Assessment Through Intelligent Decision Trees

Krishnan

2014

Complex System Modelling and Control Through Intelligent Soft Computations

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“…In a competitive energy market, TEP is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operation and interruption costs. This practice is the only rational response to conflicting customer and regulatory demands [1,2].…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System Applied to the Multi-stage Transmission Expansion Planning

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Silva

Honório

2009

Lecture Notes in Computer Science

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Abstract. Transmission expansion planning (TEP) is a complex optimization task to ensure that the power system will meet the forecasted demand and the reliability criterion, along the planning horizon, while minimizing investment, operational, and interruption costs. Metaheuristic methods have demonstrated the potential to find good feasible solutions, but not necessarily optimal. These methods can provide high quality solutions, within an acceptable CPU time, even for large-scale problems. This paper presents an optimization tool based on the Artificial Immune System used to solve the TEP problem. The proposed methodology includes the search for the least cost solution, bearing in mind investments and ohmic transmission losses. The multi-stage nature of the TEP will be also taken into consideration. Case studies on a small test system and on a real sub-transmission network are presented and discussed.

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“…Reliability issues in restructured power systems have been discussed in Refs. [18,19]. Some techniques have been developed in Refs.…”

Section: Introductionmentioning

confidence: 99%