In a multiple area power system power can be transferred from one area to other to improve the load factor, reliability, security and economics of the power system. The generation cost of each unit in an area and the cost of power transfer from other area are given. For a given load in an area the problem is how much power to be generated internally by all the generating units in the area (unit wise) and how power has to be transferred (pooled) from other area for a given total load demand in the area (for economic operation of the power system). This problem has been solved using Lagrange multiplier method recently. The limitation of this method is that it is applicable only if the generation cost of each generating unit is quadratic. Sometimes the power generation cost of each unit is not quadratic but is other type of nonlinear function e.g. the valve-point effect problem it is addition of quadratic and sinusoidal function. The main objective of this paper is to overcome this limitation by solving this general type of nonlinear optimal problem using a Meta heuristic method, PSO (Particle Swarm Optimization) and its variants. It is to point out that for implementation of PSO it is required to select a population size. Generally the population size is selected on adhoc basis. This effects the computation time as well as number of iteration for solution. A method has been suggested to select population size on the basis of optimal computation time as second objective. The method is explained by an example. The results obtained by PSO and its variants are compared among themselves and with the results obtained by analytical method. MATLAB 7 software is used for computation.
The initiative to manage congestion has gained interest in the current deregulated scenario. The principle commitment of the work in this article is to extend the gravitational search algorithm (GSA) as an efficient metaheuristic optimizing algorithm to diminish the rescheduling cost and efficiently attenuate the overloading of the line with the minimal deviation in the active power generation. The congestion management drive is accomplished by prioritizing the generators based on their sensitivity values. Thereafter, the GSA is introduced to optimally minimize the rescheduling cost along with the minimization of the total amount of active power output and system losses. The potency of the proposed method is tested on the 39-bus New England System and the IEEE 30-bus system and 118-bus system, and the outcomes achieved with the GSA outperform the results reported with other algorithms.
<p>In this paper, a novel design method for determining the optimal PID controller parameters for non-linear power system using the particle swarm optimization (PSO) algorithm is presented. The direct feedback linearization (DFL) technique is used to linearize the nonlinear system for computing the PID (DFL-PID) controller parameters. By taking an example of single machine infinite bus (SMIB) power system it has been shown that PSO based PID controller stabilizes the system and restores the pre-fault system performance after fault is cleared and line is restored. The performance of this controlled system is compared with the performance of DFL-state feedback controlled power system. It has been shown that the performance of DFL-PID controlled system is superior compared to DFL-state feedback controlled system. For simulation MATLAB 7 software is used. </p>
In this paper, a novel design method for determining the optimal PID controller parameters for non-linear power system using the particle swarm optimization (PSO) algorithm is presented. The direct feedback linearization (DFL) technique is used to linearize the nonlinear system for computing the PID (DFL-PID) controller parameters. By taking an example of single machine infinite bus (SMIB) power system it has been shown that PSO based PID controller stabilizes the system and restores the pre-fault system performance after fault is cleared and line is restored. The performance of this controlled system is compared with the performance of DFL-state feedback controlled power system. It has been shown that the performance of DFL-PID controlled system is superior as compared to DFL-state feedback controlled system. For simulation MATLAB 7 software is used.
This paper introduces a GSA for implemented to economic operation of a interconnected area power system and computes how much power has to be generated internally in an area and how much power has to be borrowed from other area through tie-line for a specified load so that generation cost is minimized in most economical sense. This method is explained with an example and the result obtained by the proposed method is compared with by particle swarm optimization (PSO) as reported in literature. It has been shown that this method is more efficient and takes less computation time than PSO.
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