Zwe-Lee Gaing

Particle swarm optimization to solving the economic dispatch considering the generator constraints

This paper proposes a particle swarm optimization (PSO) method for solving the economic dispatch (ED) problem in power systems. Many nonlinear characteristics of the generator, such as ramp rate limits, prohibited operating zone, and nonsmooth cost functions are considered using the proposed method in practical generator operation. The feasibility of the proposed method is demonstrated for three different systems, and it is compared with the GA method in terms of the solution quality and computation efficiency. The experimental results show that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in ED problems.

A particle swarm optimization approach for optimum design of PID controller in AVR system

In this paper, a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an AVR system using the particle swarm optimization (PSO) algorithm is presented. This paper demonstrated in detail how to employ the PSO method to search efficiently the optimal PID controller parameters of an AVR system. The proposed approach had superior features, including easy implementation, stable convergence characteristic, and good computational efficiency. Fast tuning of optimum PID controller parameters yields high-quality solution. In order to assist estimating the performance of the proposed PSO-PID controller, a new time-domain performance criterion function was also defined. Compared with the genetic algorithm (GA), the proposed method was indeed more efficient and robust in improving the step response of an AVR system.

Wavelet-based neural network for power disturbance recognition and classification

In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parsevals theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.

Discrete particle swarm optimization algorithm for unit commitment

This paper proposes integrating a discrete binary particle swarm optimization (BPSO) method with the Lambda-iteration method for solving unit commitment (UC) problems. The LJC problem is considered as two linked optimization sub-problems: the unit-scheduled problem that can be solved by the BPSO method for the minimization of the transition cost, and the economic dispatch (ED) problem that can be solved by the Lambda-iteration method for the minimization of the production cost. The feasibility of the proposed method is demonstrated for 10 and 26 unit systems, respectively, and the test results are compared with those obtained by the GA method in terms of solution quality and convergence characteristic. The simulation results show that the proposed method is indeed capable of obtaining higher quality solutions.

Constrained dynamic economic dispatch solution using particle swarm optimization

This paper proposes using the particle swarm optimization (PSO) to solve the constrained dynamic economic dispatch (DED) problem in power system operation. The constrained DED must not only satisfy the system load demand and the spinning reserve capacity, but some practical operation constraints of generators, such as ramp rate limits and prohibited operating zone, are also considered in practical generator operation. The feasibility of the proposed PSO method is demonstrated for two power systems, and it is compared with the other stochastic methods in terms of solution quality and computation efficiency. The experimental results showed that the proposed PSO method was indeed capable of obtaining higher quality solutions efficiently in constrained DED problems.

Constrained optimal power flow by mixed-integer particle swarm optimization

This paper presents an efficient mixed-integer particle swarm optimization (MIPSO) for solving the constrained optimal power flow (OPF) with a mixture of continuous and discrete control variables and discontinuous fuel cost functions. In the MIPSO-based method, the individual that contains the real-value mixture of continuous and discrete control variables is defined, two mutation schemes are proposed to deal with the continuous and discrete control variables, respectively. Different objective functions with the valve-point loading effects constraints considered were employed to test the robustness of the proposed method. The feasibility of the proposed method is demonstrated for a 9-bus system and a 26-bus system, and it is compared with other stochastic methods in terms of solution quality, convergence property, and computation efficiency. The experimental results show that the MIPSO-based OPF method has suitable mutation schemes, resulting in robustness and effectiveness in solving constrained mixed-integer OPF problems.

Closure to "Discussion of 'Particle swarm optimization to solving the economic dispatch considering the generator constraints'"

For original article by Z. L. Gaing see ibid vol.18, p.1187-95, Aug. 2003 and for discussion by T. Aruldoss Albert Victoire and A. Ebenezer Jeyakumar see ibid., vol.19, no.4, p.2121-2, Nov. 2004.

Security-constrained optimal power flow by mixed-integer genetic algorithm with arithmetic operators

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems with considering transmission security and bus voltage constraints for practical application. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic crossover and mutation schemes are proposed to deal with continuous/discrete control variables, respectively. The objective of OPF is defined that not only to minimize total generation cost but also to enhance transmission security, to reduce transmission loss, to improve the bus voltage profile under normal or contingent states. Moreover, the valve-point loading effect of thermal units should be taken into consideration. The effectiveness of the proposed method is demonstrated for a 26-bus and the IEEE 57-bus systems, and it is compared with the evolutionary programming (EP) in terms of solution quality and evolutionary computing efficiency. The experimental results show that the MIGA-based OPF method is superior to the EP

Real-coded mixed-integer genetic algorithm for constrained optimal power flow

This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic mutation schemes are proposed to deaf with continuous/discrete control variables, respectively. Simultaneously, because the length of the individual is short, it is easy to deal with the operation of control variables, and high computation efficiency can be achieved. The total generation cost of units with the prohibited operating zones is employed to evaluate the individual. The feasibility of the proposed method is demonstrated for a 26-bus system, and it is compared with the simple GA method in terms of solution quality and computation efficiency. The experimental results show that the MIGA method has the suitable mutation schemes, resulting in robustness and efficiency in solving non-convex OPF problems.

Implementation of power disturbance classifier using wavelet-based neural networks

In this paper, a wavelet-based neural network classifier for recognizing power quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural network (PNN) model to construct the classifier. First, the multi-resolution analysis (MRA) technique of DWT and the Parsevals theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Second, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the features of distorted signal without losing its original property, less memory space and computing time are required. Various transient events are tested, the results show that the classifier can detect and classify different power disturbance types efficiently.