Open AccessKrill herd algorithm for automatic generation control with flexible AC transmission system controller including superconducting magnetic energy storage units
Author(s) -
Guha Dipayan,
Roy Provas Kumar,
Banerjee Subrata
Publication year - 2016
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2016.0053
Subject(s) - automatic generation control , electric power system , superconducting magnetic energy storage , control theory (sociology) , computer science , controller (irrigation) , genetic algorithm , thyristor , particle swarm optimization , algorithm , control engineering , power (physics) , engineering , electrical engineering , superconducting magnet , control (management) , agronomy , physics , quantum mechanics , artificial intelligence , biology , voltage , machine learning , magnet
This study presents an optimisation technique, called krill herd algorithm (KHA), for the effectiveness and performance analysis of an interconnected automatic generation control (AGC) system. A two‐area multi‐unit hydro–hydro (HH–HH) power system equipped with classical I‐controller and two other test systems: namely, thermal–thermal, thermal–hydro which are widely available in literature are considered for design and analysis purpose. Eigenvalues analysis assesses that HH–HH power system is highly unstable under small load perturbation. To stabilise this power system, different frequency stabilisers such as superconducting magnetic energy storage, thyristor control phase shifter, static synchronous series compensator etc. are proposed in AGC system. Optimum gains of the controller and frequency stabiliser are evaluated using KHA. Integral square error criterion is used to minimise the area control error, which is considered as an objective function. The superiority of the proposed algorithm is checked by means of an extensive comparative analysis with the results published in recent research algorithms such as craziness‐based‐particle swarm optimisation and real coded genetic algorithm etc. for the same test system.