Solving optimal reactive power problem by Shark Reek, Amplified Locust Search and White Rhinoceros Algorithms

Abstract

ABSTRACT In this work Shark Reek Algorithm (SRA), Amplified Locust Search (ALS) optimisation algorithm, White Rhinoceros Algorithm (WRA) has been projected to solve optimal reactive power problem. SRA is stimulated by the shark’s capacity to hunt based on its stench sense. Blood flows in the sea when a fish injured into the sea and this treated as search space. Normally the odour particles are high near to the wounded fish. . Secondly Amplified Locust Search (ALS) optimisation algorithm projected to solve the problem. In Locust Search explore agents alter their position at every generation of the algorithm throughout its evolution. Two behavioural phases: solitary phase and social phase are implemented. In Amplified Locust Search (ALS) optimisation algorithm as an alternative of applying both the solitary and social phase operators in the same iteration. Then White Rhinoceros Algorithm (WRA) has been applied to solve the problem. White rhinoceros are one among biggest mammals, which generally live on in leafy materials. Synoptic model and population size modernising model are designed in the projected optimisation algorithm. In this work, White Rhinoceros Ease Index (WREI) is utilised to calculate the righteousness of feasible solution. Proposed SRA, ALS, WRA algorithms is tested in IEEE 30, bus system- real power loss minimisation, voltage deviation minimisation, and voltage stability index enhancement has been attained. Then the Proposed SRA, ALS, WRA algorithms has been tested in standard IEEE 14, 30, 57, 300 bus test systems without considering the voltage stability index. Projected algorithms reduced the power loss effectively and control variables are within the limits.