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Multi-Objective Optimization incorporating TCSC with ramp-rate limits and prohibited operating zones using NSHCSA


Affiliations
1 Associate Professor, EEE department, CBIT, Hyderabad, 500075, India
     

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This paper mainly concentrates in finding enhanced optimal solution for Multi-Objective Problem (MOP) with Thyristor Controller Series Compensator (TCSC) formulated using generation fuel cost, emission, and loss objectives with practical and operating constraints. Here, the optimal location is selected to enhance the system security in terms of minimizing line overloads and bus voltage violations under severe contingency. Cuckoo Search Algorithm (CSA) along with genetic algorithm cross over operation treated as Hybrid Cuckoo Search Algorithm (HCSA) is proposed to select best value as compared with existing evaluation algorithms. Optimizing multiple objectives simultaneously and selecting a best compromised solution as per the requirements of decision maker needs an application of MOP along with fuzzy decision making tool. The proposed Non-dominated Sorting Hybrid Cuckoo Search Algorithm (NSHCSA) with TCSC is tested on IEEE test system and corresponding results are analyzed. The OPF results obtained using proposed method is compared with the existing methods.

Keywords

Optimal location, Non-dominated Hybrid Cuckoo Search Algorithm, Installation cost of device, Multi objective optimization.
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  • Multi-Objective Optimization incorporating TCSC with ramp-rate limits and prohibited operating zones using NSHCSA

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Authors

M. Balasubbareddy
Associate Professor, EEE department, CBIT, Hyderabad, 500075, India

Abstract


This paper mainly concentrates in finding enhanced optimal solution for Multi-Objective Problem (MOP) with Thyristor Controller Series Compensator (TCSC) formulated using generation fuel cost, emission, and loss objectives with practical and operating constraints. Here, the optimal location is selected to enhance the system security in terms of minimizing line overloads and bus voltage violations under severe contingency. Cuckoo Search Algorithm (CSA) along with genetic algorithm cross over operation treated as Hybrid Cuckoo Search Algorithm (HCSA) is proposed to select best value as compared with existing evaluation algorithms. Optimizing multiple objectives simultaneously and selecting a best compromised solution as per the requirements of decision maker needs an application of MOP along with fuzzy decision making tool. The proposed Non-dominated Sorting Hybrid Cuckoo Search Algorithm (NSHCSA) with TCSC is tested on IEEE test system and corresponding results are analyzed. The OPF results obtained using proposed method is compared with the existing methods.

Keywords


Optimal location, Non-dominated Hybrid Cuckoo Search Algorithm, Installation cost of device, Multi objective optimization.



DOI: https://doi.org/10.33686/prj.v13i2.189198