04272nam a22003737i 4500 0 0 lcc 0 0 REF 20000 20000 REF 2020-02-27 0 FKEE .Z35 2019 r Thesis T000000341 2020-08-13 00:00:00 2020-02-27 THESIS 0 0 lcc 0 1 REF 20000 20000 2020-02-27 0 CD12340 T000000342 2021-02-16 00:00:00 2020-02-27 THESIS 92104 92110 MY-KuUP 20251125105525.0 a||||fr|||| 001 0 ta 200227t20192019my ||||f ma|| 001 0 eng d THE0008535(Local) UMP eng UMP rda FKEE .Z35 2019 r Thesis Noor Zaihan Jamal, author. Omega grey wolf optimizer (ωGWO) for optimization of overcurrent relays coordination with distributed generation / Noor Zaihan Jamal Kuantan, Pahang : UMP, 2019 © 2019 xv, 155 pages : illustrations (some color) ; 30 cm. + 1 CD-ROM text rdacontent text rdacontent unmediated rdamedia computer rdamedia volume rdacarrier computer disc rdacarrier text file PDF rda Faculty of Electrical & Electronics Engineering Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2019 Includes bibliographical references Inverse definite minimum time (IDMT) overcurrent relays (OCRs) are among protective devices installed in electrical power distribution networks. The devices are used to detect and isolate the faulty area from the system in order to maintain the reliability and availability of the electrical supply during contingency condition. The overall protection coordination is thus very complicated and could not be satisfied using the conventional method moreover for the modern distribution system. This thesis apply a meta-heuristic algorithm called Grey Wolf Optimizer (GWO) to minimize the overcurrent relays operating time while fulfilling the inequality constraints. GWO is inspired by the hunting behavior of the grey wolf which have firm social dominant hierarchy. Comparative studies have been performed in between GWO and the other well-known methods such as Differential Evolution (DE), Particle Swarm Optimizer (PSO) and Biogeographybased Optimizer (BBO), to demonstrate the efficiency of the GWO. The study is resumed with an improvement to the original GWO’s exploration formula named as Omega-GWO (ωGWO) to enhance the hunting ability. The ωGWO is then implemented to the realdistribution network with the distributed generation (DG) in order to investigate the drawbacks of the DG insertion towards the original overcurrent relays configuration setting. The GWO algorithm is tested to four different test cases which are IEEE 3 bus (consists of six OCRs), IEEE 8 bus (consists of 14 OCRs), 9 bus (consists of 24 OCRs) and IEEE 15 bus (consists of 42 OCRs) test systems with normal inverse (NI) characteristic curve for all test cases and very inverse (VI) curve for selected cases to test the flexibility of the GWO algorithm. The real-distribution network in Malaysia which originally without DG is chosen, to investigate and recommend the optimal DG placement that have least negative impact towards the original overcurrent coordination setting. The simulation results from this study has established that GWO is able to produce promising solutions by generating the lowest operating time among other reviewed algorithms. The superiority of the GWO algorithm is proven with relays’ operational time are reduced for about 0.09 seconds and 0.46 seconds as compared to DE and PSO respectively. In addition, the computational time of the GWO algorithm is faster than DE and PSO with the respective reduced time is 23 seconds and 37 seconds. In Moreover, the robustness of GWO algorithm is establish with low standard deviation of 1.7142 seconds as compared to BBO. The ωGWO has shown an improvement for about 55% and 19% compared to other improved and hybrid method of GA-NLP and PSO-LP respectively and 0.7% reduction in relays operating time compared to the original GWO. The investigation to the DG integration has disclosed that the scheme is robust and appropriate to be implemented for future system operational and topology revolutions. Faculty of Electrical & Electronics Engineering Dissertations Universities and colleges Dissertations Theses lcc THESIS