A decision tree C4.5-based voltage security events classifier for electric power systems. (5th September 2022)

• Record Type: Journal Article
• Authors: Jain, Sanjiv Kumar; Agrawal, Shweta; Shukla, Prashant Kumar; Shukla, Piyush Kumar; Jain, Anurag
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 13:Number 4(2022)
• Page Start: 268
• Page End: 276
• Publication Date: 2022-09-05
• Subjects: artificial intelligence -- decision tree -- machine learning -- power systems -- voltage security; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

Static voltage security classification has emerged as a potential field of research, due to large interconnections and more power demand. The paper presents a model to deal with static voltage security assessment problem through machine learning algorithm and decision tree C4.5. Using this algorithm, security classifications of power system operating states is achieved under vast load variations. N – 1 line outages contingencies are considered for the knowledge-base generation using the offline continuation power flow method. Mainly, the credible contingency cases are considered for security classification. The proposed approach is tested on IEEE-30 bus and IEEE-118 bus systems. The work will be useful for system operators in control decisions and prevent the occurrence of grid failure. Percentage classification accuracy of 100% is achieved for line outage nos. 8, 12 and 13 for IEEE-30 bus system and the accuracy is 98% for line outages no. 93 for IEEE-118 bus test system.