Hybridization of Self Adaptive Differential Evolution Algorithm with Biogeography-Based Algorithm for Solving Reactive Power Problem

In this paper, Self-adaptive Differential Evolution Algorithm (SaDE) Hybridized with Biogeography-Based Algorithm (BBO) to solve reactive power problem. In this proposed algorithm Iteration-level hybridization is done, in which Self-adaptive Differential Evolution Algorithm and Biogeography-Based Algorithm (DEBA) is executed in sequence. Self-adaptive Differential Evolution Algorithm acts independently and then exchanges information from Biogeography-Based algorithm. The proposed DEBA has been tested in IEEE 30,118 bus test systems and simulation results show clearly the better performance of the proposed algorithm in reducing the real power loss. Keywords: Evolutionary computation, Self-Adaptive Differential Evolution Algorithm, Biogeography-Based optimization, optimal reactive power, Transmission loss

Reduction of Real Power Loss and Safeguarding of Voltage Constancy by Artificial Immune System Algorithm

In this paper, Artificial Immune System (AIS) algorithm is used for solving reactive power problem. Artificial Immune System Algorithm, also termed as the machine learning approach to Artificial Intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Artificial immune system algorithmic approach to power system optimization these ideas are embedded into proposed algorithm for solving reactive dispatch problem. In order to evaluate the proposed algorithm, it has been tested in standard IEEE 30,118 bus systems and compared to other specified algorithms. Simulation results show better performance of the proposed AIS algorithm in reducing the real power loss and preservation of voltage stability

Optimal Power Flow with Static VAR Compensator Using Galaxy Based Search Algorithm to Minimize Real Power Losses

AbstractIn this paper, Galaxy based search algorithm (GbSA) is used to solve multi-objective problem of optimization in power systems. The proposed GbSA resembles the spiral arms of some galaxies to search for the optimal solutions. The GbSA also uses a modified Hill Climbing algorithm as a local search. Simulation results show that the GbSA finds the optimal or very near optimal values in all runs of the algorithm. The weighted sum technique with equal weights has been chosen to solve the multi-objective function. The functions considered are to minimize the power losses in transmission line, cost of the real power generation and voltage deviation. Static VAR Compensator (SVC) is used for the purpose of optimal power flow. L-index is used to identify the optimal location to place SVC. The results have been compared with Genetic algorithm (GA) for IEEE-14 System

Performance Analysis of Symmetrical and Asymmetrical Configuration of Open-End Winding Induction Motor Drive Using Decoupled SVPWM Techniques

In this article, space vector based decoupled PWM techniques are proposed for both symmetrical and asymmetrical configuration of open-end winding induction motor drive. These decoupled PWM techniques are generated using instantaneous reference voltages of the two inverters. That means, one inverter reference voltages are phase shifted with respect to other inverter reference phase voltages, So that each inverter operates independently. It does not require any complex computations as in case of conventional PWM techniques. A common-mode voltage is identified in the dual inverter configuration. These decoupled PWM techniques also reduce the common mode voltage by better extent. All the proposed PWM techniques for symmetrical and asymmetrical configurations of open-end winding induction motor are implemented using MATLAB/SIMULINK and the corresponding results are reported and compared

Reduction of Real Power Loss and Safeguarding of Voltage Constancy by Artificial Immune System Algorithm

In this paper, Artificial Immune System (AIS) algorithm is used for solving reactive power problem.  Artificial Immune System Algorithm, also termed as the machine learning approach to Artificial Intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Artificial immune system algorithmic approach to power system optimization these ideas are embedded into proposed algorithm for solving reactive dispatch problem. In order to evaluate the proposed algorithm, it has been tested in standard IEEE 30,118 bus systems and compared to other specified algorithms. Simulation results show better performance of the proposed AIS algorithm in reducing the real power loss and preservation of voltage stability

Optimizing real power loss and voltage stability limit of a large transmission network using firefly algorithm

This paper proposes a Firefly algorithm based technique to optimize the control variables for simultaneous optimization of real power loss and voltage stability limit of the transmission system. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained optimization problem with an objective function integrating both real power loss and voltage stability limit. Transformers taps, unified power flow controller and its parameters have been included as control variables in the problem formulation. The effectiveness of the proposed algorithm has been tested on New England 39-bus system. Simulation results obtained with the proposed algorithm are compared with the real coded genetic algorithm for single objective of real power loss minimization and multi-objective of real power loss minimization and voltage stability limit maximization. Also, a classical optimization method known as interior point successive linear programming technique is considered here to compare the results of firefly algorithm for single objective of real power loss minimization. Simulation results confirm the potentiality of the proposed algorithm in solving optimization problems

Firefly algorithm based solution to minimize the real power loss in a power system

This paper proposes a method to minimize the real power loss (RPL) of a power system transmission network using a new meta-heuristic algorithm known as firefly algorithm (FA) by optimizing the control variables such as transformer taps, UPFC location and UPFC series injected voltage magnitude and phase angle. A software program is developed in MATLAB environment for FA to minimize the RPL by optimizing (i) only the transformer tap values, (ii) only UPFC location and its variables with optimized tap values and (iii) UPFC location and its variables along with transformer tap setting values simultaneously. Interior point successive linear programming (IPSLP) technique and real coded genetic algorithm (RCGA) are considered here to compare the results and to show the efficiency and superiority of the proposed FA towards the optimization of RPL. Also in this paper, bacteria foraging algorithm (BFA) is adopted to validate the results of the proposed algorithm

OPTIMAL LOCATION OF SVC FOR REAL POWER LOSS MINIMIZATION AND VOLTAGE STABILITY ENHANCEMENT USING HARMONY SEARCH ALGORITHM

ABSTRACT Everyday increasing in demand in the electric power system network has been caused the system to be overloaded and leading to voltage instability. It is one of the major phenomena which has resulted a major obstruct to power system network. It is possible that voltage stability status in a stressed Power system could be improved with effective reactive power compensation and it can be achieved by significant use of FACTS controllers. This paper aimed at multi-objective optimization problem such as minimization of real power losses, load voltage deviation and L-index. Here Lindex is used to find the critical buses in the system for optimal location of shunt connected FACTS controller known as Static Var Compensator (SVC).A Meta-Heuristic Algorithm Known as Harmony Search Algorithm (HSA) is applied to find the optimal sizes of SVC for solving Multiobjective optimization problem. Simulations are performed on IEEE 14-bus test system. The results are shown that optimal location and sizing of SVC minimizes real power losses, load voltage deviation, L-index and also Voltage profiles are improved at different loading conditions. In this present paper 125%, 150%, 175% and 200% overloading cases are considered