Aleksei Zhukov

Development of software for modelling decentralized intelligent systems for security monitoring and control in power systems

With rapidly increasing complexity of power grids in Europe, North America and Asia, liberalization of electricity markets and increasing penetration of renewable energy, the risk of large-scale emergencies and blackouts increases. This paper proposes a novel approach for development of software for modelling of decentralized intelligent systems for security monitoring and control in power systems. The innovation here is to joint use the modern computing environments - MATLAB, R and Java Agent Development Framework platform. The proposed intelligent system was tested on the modified 53-bus IEEE power system.

On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm Modification

Voltage instability is among the main factors causing large-scale blackouts. One of the major objectives of the Control centers is a prompt assessment of voltage stability and possibly self-healing control of electric power systems. The standing alone solutions based on classical approximation methods are known to be redundant and suffer with limited efficiency. Therefore, the state-of-the-art machine learning algorithms have been adapted for security assessment problem over the last years. This chapter presents an automatic intelligent system for on-line voltage security control based on the Proximity Driven Streaming Random Forest (PDSRF) model using decision trees. The PDSRF combined with capabilities of L-index as a target vector makes it possible to provide the functions of dispatcher warning and “critical” nodes localization. These functions enable self-healing control as part of the security automation systems. The generic classifier processes the voltage stability indices in order to detect dangerous pre-fault states and predict emergency situations. Proposed approach enjoy high efficiency for various scenarios of modified IEEE 118-Bus Test System enabling robust identification of dangerous states.

Development of automatic intelligent system for on-line voltage security control of power systems

The majority of recent large-scale blackouts have been caused by voltage instability. A prompt on-line assessment of voltage stability for preventive corrective control of electric power systems is one of the key objectives for Control centers. The use of classical approximation methods alone is complicated. Therefore, several modified methods combined with machine learning algorithms enabling security assessment in real time have been proposed over the last years. The paper presents an automatic intelligent system for on-line voltage security control, which is based on the model of decision trees Proximity Driven Streaming Random Forest (PDSRF). In this case, the combination of original properties of PDSRF and capabilities of L-index as a target vector makes it possible to provide the functions of dispatcher warning, localization of critical nodes, and ensure direct interaction with the security automation systems. The efficiency of the proposed system was demonstrated using various test schemes of IEEE.