A Hybrid Multi-objective Integrated JAYA-Evolutionary Programming (MOIJEP) Algorithm for Under Voltage Load Shedding (UVLS) Scheme in Bulk Power System

• Published in: Lecture Notes in Electrical Engineering
• Main Author: Shukor S.F.A.; Musirin I.; Hamid Z.A.; Senthil Kumar A.V.; Mansor M.H.; Salimin R.H.
• Format: Conference paper
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198726804&doi=10.1007%2f978-981-97-3940-0_76&partnerID=40&md5=799298d0bc06df11ea498ff78f4036a9

Progressing demand can lead to voltage decay in a power system which causes under-voltage phenomenon. Load shedding is a reliable last option to secure a power system from possible voltage collapse occurrence when unintended disturbance occurs. Under Voltage Load Shedding (UVLS) is one of the suitable methods to overcome further unsecure operation. This will require a reliable optimization technique to identify the most suitable locations and sizing for UVLS scheme. This paper presents a hybrid multi-objective integrated jaya-evolutionary programming (MOIJEP) algorithm for under voltage load shedding (UVLS) scheme in bulk power system. MOIJEP integrates the features in the original Jaya algorithm into the conventional Evolutionary Programming (EP). A weighted sum multi-objective which considers voltage stability index, loss and minimum voltage in the system is formulated in this study, implemented for multi-load shedding scheme. Results obtained using the proposed MOIJEP are superior to the benchmarked technique, i.e., MOIJEP when validated on the IEEE 57-Bus Reliability Test System (RTS). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.