ENHANCED SEEKER OPTIMIZATION ALGORITHM FOR REDUCTION OF ACTIVE POWER LOSS

This paper projects Enhanced Seeker Optimization (ESO) algorithm for solving optimal reactive power problem. Seeker optimization algorithm (SOA) models the deeds of human search population based on their memory, experience, uncertainty reasoning and communication with each other. In Artificial Bee Colony (ABC) algorithm the colony consists of three groups of bees: employed bees, onlookers and scouts. All bees that are presently exploiting a food source are known as employed bees. The number of the employed bees is equal to the number of food sources and an employed bee is allocated to one of the sources. In this paper hybridization of the seeker optimization algorithm with artificial bee colony (ABC) algorithm has been done to solve the optimal reactive power problem. Enhanced Seeker Optimization (ESO) algorithm combines two different solution exploration equations of the ABC algorithm and solution exploration equation of the SOA in order to progress the performance of SOA and ABC algorithms. At certain period’s seeker’s location are modified by search principles obtained from the ABC algorithm, also it adjust the inter-subpopulation learning phase by using the binomial crossover operator. In order to evaluate the efficiency of proposed Enhanced Seeker Optimization (ESO) algorithm it has been tested in standard IEEE 57,118 bus systems and compared to other specified algorithms. Simulation results clearly indicate the best performance of the proposed Enhanced Seeker Optimization (ESO) algorithm in reducing the real power loss and voltage profiles are within the limits

ANTELOPE ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER DISPATCH PROBLEM

In this paper, Antelope Algorithm (AA) is proposed for solving optimal reactive power dispatch problem. A population of candidate solution move toward as a herd of Antelope out a sequence of jumps through the exploration space in order to find the most outstanding solution. The main idea of this algorithm is fairly different from the population based algorithms, as the individual solutions are stirred collectively in a herd-like approach. Projected Antelope Algorithm (AA) algorithm has been tested in standard IEEE 30 bus test system and simulation results show clearly about the superior performance of the projected algorithm in reducing the real power loss

REDUCTION OF REAL POWER LOSS BY UNIFIED ALGORITHM

In this paper, we propose a new Unified Algorithm (UA) by combination of Variable mesh optimization algorithm (VMO) with Differential Evolution (DE) for solving reactive power problem. VMO has mainly three search operators, one for global exploration and two for local optima exploitation. DE is a simple yet commanding evolutionary algorithm for solving optimization problems. In all iteration VMO serve as the initial population of DE and obtains a population of more quality with this population VMO begins a new cycle. The proposed UA has been tested in standard IEEE 30 bus test system and simulation results show clearly about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits

COALESCED ALGORITHM FOR SOLVING REACTIVE POWER PROBLEM

This paper combines Parallel Chaos Optimization Algorithm with Outlook Algorithm (PO) to solve optimal reactive power problem. The algorithm is organized in dual phases. The first phase uses parallel chaos optimization grounded on tent map for global exploration, while outlook algorithm is involved in the second phase for local exploration. The projected PO algorithm has been tested in standard IEEE 57,118 bus test systems and simulation results show clearly the improved performance of the proposed PO algorithm in declining the real power loss when compared to other reported standard algorithms

ENRICHED BLACK HOLE ALGORITHM FOR DIMINUTION OF REAL POWER LOSS

This paper presents an Enriched Black Hole (EBH) algorithm for solving reactive power flow problem. The Black Hole Algorithm starts with a preliminary population of contestant and for all iteration of the black hole algorithm, the most excellent candidate is favored to be the black hole, which followed by pulling further candidates around it, called stars. If a star move very close to the black hole, it will be consumed by the black hole and is vanished undyingly. In such a case, a new star - candidate solution is arbitrarily created and placed in the exploration space and starts a new search. Black hole algorithm is feeble to carry out global search completely in the large size problem spaces. So the enhancement in the amalgamation process in black hole algorithm has to be done. In this work, black hole algorithm will be enhanced, using stars gravities information. For this aim, a kind of gravitational force between stars is defined and the movement of stars to the black hole is adjusted during the penetration of solution space. In order to evaluate the projected Enriched Black Hole (EBH) algorithm, it has been tested in Standard IEEE 57,118 bus systems and compared to other standard reported algorithms. Simulation results reveal about the Enriched performance of the projected algorithm in plummeting the real power loss

MINIMIZATION OF REAL POWER LOSS BY ENHANCED GRAVITATIONAL SEARCH ALGORITHM

In this paper, Enhanced Gravitational Search (EGS) algorithm is proposed to solve the reactive power problem. Gravitational search algorithm (GSA) results are improved by using artificial bee colony algorithm (ABC). In GSA, solutions are fascinated towards each other by applying gravitational forces, which depending on the masses assigned to the solutions, to each other. The heaviest mass will move slower than other masses and pull others. Due to nature of gravitation, GSA may pass global minimum if some solutions stuck to local minimum. ABC updates the positions of the best solutions that have obtained from GSA, preventing the GSA from sticking to the local minimum by its strong penetrating capability. The proposed algorithm improves the performance of GSA in greater level. In order to evaluate the performance of the proposed EGS algorithm, it has been tested on IEEE 57,118 bus systems and compared to other standard algorithms

DIMINUTION OF REAL POWER LOSS BY VALUE-ADDED BAT ALGORITHM

In this paper, a new Value-added Bat Algorithm (VBA) is proposed to solve reactive power problem. Echolocation is a significant feature of bat behavior and it produce a sound pulse and listens to the echo bouncing back from obstacles whilst flying. Projected VBA algorithm utilizes chaotic behaviour to produce a candidate solution in behaviours analogous to acoustic monophony. Proposed VBA has been tested in Standard IEEE 118 bus system & practical 191 Indian utility system and simulation results show clearly the better performance of the proposed algorithm in decreasing the real power loss