Online Static Security Assessment of Power Systems Based on Lasso Algorithm

TL;DR

This paper introduces a novel online static security assessment method for power systems using a Lasso-based regression approach, improving speed and accuracy in contingency screening under various load conditions.

Contribution

It develops a multi-step adaptive Lasso regression model for real-time security assessment, integrating contingency ranking and security index prediction in power systems.

Findings

Demonstrates high accuracy in IEEE test systems

Shows rapid assessment suitable for real-time applications

Validates effectiveness across different system sizes

Abstract

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.