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.
This is the extended version of the manuscript of the First EAI International Conference on Computer Science and Engineering (COMPSE 2016), November 11–12, 2016, Penang, Malaysia. AZ, DS and DP are partly supported by the International science and technology cooperation program of China and Russia, project 2015DFR70850 and by the National Natural Science Foundation of China Grant No. 61673398.
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