Supervised learning approach to online contingency screening and ranking in power systems

1 Abstract: This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PI MVA ) and Voltage-reactive power performance index (PI VQ ). SVM works in two steps. Step I is the estimation of both standard indices (PI MVA and PI VQ ) that is carried out under different operat- ing scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system). The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

https://tandfonline.com/doi/pdf/10.1080/23311916.2015.1137201

ELECTRICAL & ELECTRONIC ENGINEERING | RESEARCH ARTICLE A least square support vector machine-based approach for contingency classification and ranking in a large power system

This paper presents a review of artificial-intelligence methodologies for contingency analysis for power system security. In the planning process, offline artificial intelligence methods can assist the operator with anticipatory information useful to preserve the steady state security of the system. In online operation stages, artificial intelligence techniques have been proposed to speed up and improve the accuracy of voltage-stability analysis and for contingency selection, without requiring a traditional mathematical model of the power grid. Furthermore, when the power grid is stressed, artificial intelligence approaches to dynamic contingency analysis are capable of empowering the operator to control the power grid in a more reliable and rapid fashion.

On Artificial Intelligence Approaches for Contingency Analysis in Power System Security Assessment

This paper proposes an effective supervised learning approach for static security assessment of a large power system. Supervised learning approach employs least square support vector machine (LS-SVM) to rank the contingencies and predict the system severity level. The severity of the contingency is measured by two scalar performance indices (PIs): line MVA performance index (PIMVA) and Voltage-reactive power performance index (PIVQ). SVM works in two steps. Step I is the estimation of both standard indices (PIMVA and PIVQ) that is carried out under different operating scenarios and Step II contingency ranking is carried out based on the values of PIs. The effectiveness of the proposed methodology is demonstrated on IEEE 39-bus (New England system). The approach can be beneficial tool which is less time consuming and accurate security assessment and contingency analysis at energy management center.

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

In modern emerging power system many contingencies and critical operating conditions present a potential threat to system’s stability. An intelligent designer at energy management center requires a paradigm which can not only predict such cases but also suggests an effective strategy for preventive control. This paper presents a least square support vector machine (LS-SVM)-based classifier to identify and rank the critical contingencies in a standard IEEE-39 bus Network (New England). This paradigm works in two stages. In first stage, the identification of two indices, i.e., voltage reactive performance index PIVQ and MVA line loading index PIMVA is carried out and in next stage the classification of contingencies is carried out. The proposed approach shows promising results when compared with recent contemporary techniques.

Supervised Learning Paradigm Based on Least Square Support Vector Machine for Contingency Ranking in a Large Power System

This paper presents a new decision tree (DT) based approach for fast voltage contingency screening and ranking for on-line applications in energy management systems. The hybrid decision tree model is developed to learn all the selected contingencies simultaneously, therefore fewer DTs are required. To reduce the size and improve the accuracy of the decision tree, the K-class problem is converted into the set of K two-class problems, and separate decision tree modules are trained for each of the two class problems. All the selected contingencies are presented to the filter module, which is trained to separate them in critical and non-critical contingency classes, which reduces the burden on ranking modular DT. The critical contingencies screened out by the filter module are presented to the ranking modular decision tree for their further ranking. To measure the severity of contingencies, bus voltage violation based scalar performance index is used. Full AC load flow is performed to generate the training and testing patterns for the proposed hybrid decision tree, under each contingency. The effectiveness of the proposed approach is tested on IEEE test systems. Once trained, a hybrid decision tree method gives fast and accurate screening and ranking of contingencies for unknown load patterns.

Narayan Prasad Patidar

Papers

A Hybrid Decision Tree Model for Fast Voltage Contingency Screening and Ranking