Power System Topological Observability Analysis Using Improved Hopfield Neural Network

Journal of Electrical and Electronics Engineering

SALKUTI Surender Reddy¹ 

[1] Woosong University, Daejeon, Republic of Korea;

关键词: improved hopfield neural networks;    power system;    observability;    state estimation;    evolutionary algorithms;   

DOI  :  

来源: DOAJ

【 摘 要 】

This paper solves the Topological Observability (TO) problem of a power system using the new methodology based on Improved Hopfield Neural Network (IHNN). This IHNN can solve the combinatorial optimization problem with inequality constraints. The proposed approach is computationally much simpler compared to the root based technique and the evolutionary based Particle Swarm Optimization. The proposed approach is useful for a small system to very large system of modern power system with the reduced memory, less execution time and no round-off errors. The proposed IHNN algorithm has been implemented on 5 bus - 10 measurement system and 12 bus - 19 measurement system. Some of the results obtained using the proposedIHNN are also compared with Particle Swarm Optimization.

【 授权许可】

Unknown