Contingency severity assessment for voltage security using non-parametric regression techniques

Contingency severity assessment for voltage security using non-parametric regression techniques

Abstract — This paper proposes a novel approach to voltage security assessment exploiting non-parametric regression techniques to extract simple and at the same time reliable models of the severity of a contingency, defined as the difference between preand postcontingency load power margins. The regression techniques extract information from large sets of possible operating conditions of a powe...

Artificial neural network model for voltage security based contingency ranking

The continual increase in demand for electrical energy and the tendency towards maximizing economic benefits in power transmission system has made real-time voltage security analysis an important issue in the operation of power system. The most important task in real time security analysis is the problem of identifying the critical contingencies from a large list of credible contingencies and r...

Contingency Constrained Power System Security Assessment using Cascade Neural Network

A unified approach to power system security assessment and contingency analysis suitable for on-line applications is proposed. The severity of the contingency is measured by two scalar Performance Indices (PIs): Voltage-reactive power performance index, PIVQ and line MVA performance index, PIMVA. In this paper, a two stage cascade neural network is developed: Stage I employs Multi-Layer Percept...

Online Non-Parametric Regression

We establish optimal rates for online regression for arbitrary classes of regression functions in terms of thesequential entropy introduced in [14]. The optimal rates are shown to exhibit a phase transition analogous to thei.i.d./statistical learning case, studied in [16]. In the frequently encountered situation when sequential entropyand i.i.d. empirical entropy match, our resu...

Voltage Security Margin Assessment