An integrated optimization approach to locate the D-STATCOM in power distribution system to reduce the power loss and total cost

Abstract

The optimization problem with a single objective can obtain a single solution, called an optimal solution. It maximizes or minimizes the performance of a particular objective function to a given constraint. But, in the case of the multi-objective optimization, different objectives can be simultaneously optimized. Thus, this paper recommends a multi-objective optimization methodology for simultaneously perform the two objective functions such as resizing and optimal placement of Distributed Static Compensator (DSTATCOM) for reducing the power loss, total cost and enhancing the voltage profile. For these purposes, an integrated approach of two optimization algorithm called Multi-objective Ant Colony Optimization (MACO) and Bacterial Foraging Optimization Algorithm (BFOA) are used. The prime intention of this work is to bring down the power loss, total cost and enhance the voltage profile by placing the DSTATCOM device in an optimal location. Here, IEEE-30 and IEEE-69 bus systems are considered to appraise the recital of the recommended approach. Moreover, the effectiveness of the MACO-BFOA approach is evaluated and compared with other multi-objective algorithms. From this analysis, it is observed that when compared to these techniques, the proposed system provides the minimized power loss and total cost.

Optimal siting and sizing of multiple type DGs for the performance enhancement of Distribution System using Differential Evolution Algorithm

“…The authors had used dragon fly algorithm to place the DGs on IEEE 15, 33 and 69 bus systems. The proposed algorithm results were compared with other evolutionary algorithms from literature and found to be better [12]. Adel et al have proposed water cycle algorithm to place DGs and Capacitor Banks to improve the voltage profile of the distribution system.…”

Bat Algorithm Based Selective Harmonic Elimination PWM for an Eleven Level Inverter

“…SHE technique uses a set of non-linear transcendental equations as the fitness or objective function. There are three methods for solving the SHE problem, resultant theory method, iterative methods like Newton Raphson method and evolutionary algorithms like Genetic Algorithm (GA), Flower Pollination Algorithm (FPA), BFOA, PSO and so on [4], [8][9][10][11][12][13][14][15][16][17][18][19][20]. In this paper, to deal with SHE problem and to find out the finest switching angles for minimizing the THD level, Bat Algorithm (BA) technique is proposed [21].…”