Arup Ratan Bhowmik

Placement of UPFC for minimizing active power loss and total cost function by PSO algorithm

In this paper Particle Swarm Optimization (PSO) technique has been used to minimize the active power loss and the total cost function, which includes the generation costs and the UPFC investment cost, through optimally placing the Unified Power Flow Controller (UPFC) in the power system network. Three objective functions are taken as the indexes of the system performance; identification of most suitable buses using line loss sensitivity index, minimization of active power loss and total generation cost by employing PSO. For the validation and comparison purposes of the proposed techniques, simulations are carried out on modified IEEE 14-bus power system. Results validate that the static performances of the power system can be successfully improved due to the optimal allocation of single UPFC controller.

Non dominated sorting gravitational search algorithm based optimal power flow with emission and non smooth cost functions

This article presents application of non dominated sorting based gravitational search algorithm (NSGSA) for solving optimal power flow (OPF) problems with economic and environment objectives. Fast elitist non-dominated sorting and crowding distance have been used to locate and manage the Pareto optimal front. The proposed method is tested and implemented on standard IEEE 30-bus test power system, with different objectives, that reflect the minimization of total quadratic fuel cost, piecewise quadratic fuel cost, fuel cost with valve point loading effect of generators and emission cost. The numerical results have been compared with other recently developed algorithms to reveal the tangible superiority of the proposed method for solving the OPF problems.

Multi objective optimal power flow using NSMOGSA

Application of non dominated sorting multi objective gravitational search algorithm (NSMOGSA) for solution of different optimal power flow (OPF) problem is presented in this article. NSMOGSA is an extension of the gravitational search algorithm (GSA), where the gravitational acceleration is updated using non-dominated sorting concept. A mixture of sign and reordering mutations are also proposed in to reduce the chance of trapping in local optima. The proposed method is employed for optimal adjustments of the power system control variables which involve both continuous and discrete variables of the OPF problem. To show the effectiveness of the proposed algorithm, it has been tested on a standard IEEE 30-bus system with two different individual objectives that reflect total emission cost minimization and fuel cost minimization. The performance of NSMOGSA is compared with the results found by other meta-heuristic techniques reported in the recent literature to demonstrate the tangible superiority of the proposed method.

Three-Dimensional SVPWM using α-β-γ Frame for Solving 4-leg Active Shunt Filter

This paper establishes that a three dimensional (3D) space vector pulse width modulation method (SVPWM), using α-β-γ frame, produces better results in four leg (4L) bridge circuit converters used in applications for three phase four wire (4W) low voltage (LT) distribution systems. The algorithm developed has been tested in a 4L active shunt filter (ASF) which helped to reduce harmonics from source currents of a three phase 4W LT distribution system having non-linear and unbalanced load. The ASF also reduces the unwanted component of neutral current. All unwanted components of line currents and the neutral current have been used to generate the 3D reference signal vector to modulate the ASF. Neutral current has been divided in two components, the undesirable and the normal, acknowledging the unbalanced nature of 4W LT distribution systems as a normal phenomenon. The proposed ASF has a unique feature that its bridge circuit capacitor has no need of prior charging to a voltage and to maintain that constant during operations. The MATLAB/simulation of the proposed ASF, working with the developed program, has become simpler in design without phase locked loop or feedback loop and has satisfactory performance.

NSMOOGSA for solving combined economic and emission dispatch problem

Combined economic and emission dispatch (CEED) is a key challenge for the system operator in the open access regime and is used to operate generators that produce energy in a power plant with least costs as well as least emission simultaneously. Stability issues must, therefore, be considered during CEED. This paper presents a multi-objective formulation of CEED in a power system, with the competing objective functions of minimizing fuel cost and emission. Non-dominated Sorting Multi Objective Opposition based Gravitational Search Algorithm (NSMOOGSA) is used as an optimization tool. Fuzzy decision maker is used to extract the best compromise non-dominated solution. The proposed method has been tested on the IEEE 30-bus system and results are compared with those of the other reported in the recent literature. The quality of the results establishes the efficacy of the proposed approach in solving different CEED problem.