Abstract
Dynamic security is an essential requirement for operating a modern power system. Due to the global increase in load demand, modern power systems witness several dramatic changes in terms of size and implementation of new renewable sources. At the same time, the deregulation process in power operation policy is being pushed to operate closer to its security boundary limits. Based on the combined Decision Tree (DT) algorithm, namely Random Forest (RF) and advance attribute selection technique, this paper presents an approach to address these challenges related to dynamic security assessment (DSA) in the modern power system. The performance of study approach is demonstrated on a modified version of IEEE 9 and 14-bus test system models with presence of two wind turbines (WTs) type WTG 3. Results show the superiority of RF compared to other DT algorithms that are used in this study. In addition, the attribute selection technique could significantly affect the number of attributes required for DSA. This makes DT classifier more effectiveness in the online application. Thus, this approach can provide control center with vital information with high accuracy results and less attributes about security state direction that will help operator to take the right and fast steps to remedy problems and prevent a blackout from occurring.
Highlights
The dynamic security is one of a significant issue in power system operation [1]
Based on the combined Decision Tree (DT) algorithm, namely Random Forest (RF) and advance attribute selection technique, this paper presents an approach to address these challenges related to dynamic security assessment (DSA) in the modern power system
The RF shows an outperformance for both IEEE test systems compared to other DTs namely J48, Logistic Model Tree (LMT) and Reduced Error Pruning (REP)
Summary
The dynamic security is one of a significant issue in power system operation [1]. control center aims to assess dynamic security behavior after each disturbance occurred in the power grid via DSA tool [2]. Each of these research in [16,17,18,19,20,21,22,23,24] used various power system or various disturbance scenarios and these algorithms could successfully build an intelligent DSA classifier via training/ test process that used system measurements data as input and assessment system state as output. Each technique from studies above shows different results in term of classified error and computational time depending on the ability of the algorithms to discover the relationship between input feed and out state Despite that these artificial intelligence methods, especially DT, show outperformance compared with the intelligent systems.
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