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Abstract: A new design of decentralized load-frequency controller for interconnected power systems with ac-dc parallel using Particle Swarm Optimization (PSO) algorithm is proposed in this paper. A HVDC link is connected in parallel with an existing ac tie-line to stabilize the frequency oscillations of the ac system. Any optimum controller selected for load frequency control of interconnected power systems should not only stabilize the power system but also reduce the system frequency and tie line power oscillations and settling time of the output responses. In practice Load Frequency Control (LFC) systems use simple Proportional Integral (PI) or Integral (I) controller parameters are usually tuned based on classical or trial-and-error approaches, they are incapable of obtaining good dynamic performance for various load change scenarios in multi-area power system. For this reason, in this paper the PI and I control parameters are tuned based on PSO algorithm method for the LFC control in the two-area power system. A two area interconnected thermal power system is considered to demonstrate the validity of the proposed controller. The simulation results show that the proposed controller provides better dynamic responses with minimal frequency and tie-line power deviations, quick settling time and guarantees closed-loop stability margin. [Copyright &y& Elsevier]

Abstract: The higher power microwave heating source, in the industrial and engineering needs, is obtained through inputting microwave energy of multi-open feed into cavity via near-field radiation, resulting in the coupling of each feed. Meanwhile, the initial situation, the ratio of fluid and the permittivity are highly nonlinear in the process of microwave heating during technical operation process by microwave heating process cycling fluid medium. This paper is presented an improved Anti-predatory Particle Swarm Optimization (APSO) algorithm to optimize the three parameters of PID controller, and is applied to control the complicated and coupling of microwave heating. It is shown through intelligent calculation and simulation experiments that the improved APSO has good performance. And this paper provides an alternative for control of multi-couple and uncertain objects in industry. [Copyright &y& Elsevier]

Abstract: There are many methods available for Load frequency control in an interconnected power system. This paper deals with tuning of PID controller for Load frequency control. A two area thermal power system is considered for study. Both the areas are equipped with PID controller. Parameters of these PID controllers are obtained using Particle Swarm Optimization. A comparative study on tuned values has been presented to verify effectiveness. Ziegler-Nichols method is used for tuning of parameters for comparative study. The simulation results demonstrate the effectiveness of the designed system in terms of reduced settling time and oscillations. MATLAB/SIMULINK was used as simulation tool. [Copyright &y& Elsevier]

Abstract: The traditional MS tracker has two main drawbacks: (1) The template model can only be built from a single image; (2) It is difficult to adaptively update the template over the tracking. In this paper we attempt to use an online SVM based kernels model updating method to overcome these above-mentioned problems. Simultaneously, in contrast with the template matching method which one usually achieves tracking through maximizing the likelihood between model and candidate region. Here, we treat the tracking as a foreground/background classification problem. Initialized with a small number of data to train using SVM, we could get primitive models (include the object models and the background models) and a decision function. In the process of tracking, we update the previous models using the tracked data, and then maximize the value of decision function with the help of the particle swarm optimization. The optimal solution predicts the object''s location. Experiments show that this method performs better than MS tracker on some challenging sequences. [Copyright &y& Elsevier]

Abstract: Particle swarm optimization (PSO) is an intelligent optimization technique, which has shown a good performance in many real world and benchmark problems. In this paper, we propose an improved PSO variant, called PSO with modified velocity strategy (MVPSO), in which each particle is attracted by the global best particle and a random particle chosen from a set of good particles. In order to verify the performance of MVPSO, ten well-known benchmark problems are used in the experimentas. Simulation results show that MVPSO obtains better performance than standard PSO and two other improved PSO variants. [Copyright &y& Elsevier]