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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: Multilevel inverters are widely used in solar energy generation systems consist of various photovoltaic generators. These converters deliver distorted output waveforms. There are trascendental equations characterizing the harmonic content but the degree of the polynomial increases with the number of DC sources or the number of harmonics to be eliminated. Also, the solutions are discontinuous in some points. In this paper, a way is proposed to minimize the output harmonics of a multilevel inverter, especially those of lower order. The Particle Swarm Optimization algorithm used to minimize the Total Harmonic Distortion, subject to restrictions on the harmonic content allowed. The appropriate switching angles can be calculated to eliminate low order harmonics. The proposed method has been succesfully applied to a cascaded multilevel converter of seven levels. Then, this method can be extended to more levels inverters. In case of a real-time application, the method can be useful because the switching angles can be stored. [Copyright &y& Elsevier]

Abstract: In order to improve power quality and reduce network losses, this paper proposes a modified immune particle swarm algorithm for power system reactive power optimization. To overcome the disadvantages in the traditional PSO algorithm about low accuracy and easy to fall into local optimal, MIPSO adopts sinusoidal changing inertia weight strategy to make particles explore the local and global optimization more efficiently. Introduce convergence acceleration factor to improve the convergence rate. Modified immune principle enhances the search capability to avoid premature convergence. Finally, compare the reactive power optimization results of Wuzhong of Ningxia system by MIPSO with other classical PSO algorithms. The experimental results show that the MIPSO algorithm is an efficient and feasible approach for reactive power optimization. [Copyright &y& Elsevier]

Abstract: This paper proposes a regulation algorithm for the power flow regulation of a power system. The proposed method employs nodes unbalanced powers, which serves to assess the unsolvable status of the power system and to guide the corrective control strategy. Particle swarm optimization is applied to find the optimal control action, where the objective functions to be optimized are nodes unbalanced powers and load shedding. The method is applied on the New-England 39-bus test system. The proposed approach results have been compared to those that reported in the literature recently. The results are promising and show the effectiveness and robustness of the proposed approach. [Copyright &y& Elsevier]

Abstract: Tunnel kiln temperature control system is a typical system which has characteristics of complex, dynamic, and uncertainty. Under the conventional PID control, parameters are adjusted by experienced workers. In the steady-state, effect is better. If conditions change, it is difficult to be steady again. According to this characteristic, this paper proposes the use of improved particle swarm algorithm to optimize the PID control. And in the tunnel kiln temperature control system simulation, results of the two control methods are compared. Finally the optimized PID control effect is better. [Copyright &y& Elsevier]

Abstract: In this paper, efficient population utilization strategy for particle swarm optimizer (EPUS-PSO) is introduced to optimize the weight value and threshold value of BP neural network. EPUS-PSO is combined with a population manager to omit redundant particles and create new ones or maintain particle numbers according to the solution searching status to make the process more efficient. The algorithm also has two sharing strategies which can stop the particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted when the number of hidden nodes is different. EPUS-PSO-based BP training mean square error is compared with BP training. The results demonstrate that EPUS-PSO-based BP training is superior to BP training. [Copyright &y& Elsevier]

Abstract: A computation model of fractional-order differentiators based on functional network and the learning Algorithm of functional network using particle swarm optimization was proposed. it provided a new method to compete the numerical calculation of fractional-order differentiators in other fields. the simulation results demonstrate that the method presented in the paper is more efficient and feasible. [Copyright &y& Elsevier]

Abstract: With the shortage of resource and deterioration of environment, more and more enterprises and government have been identifying the importance of reverse logistics. Moreover an enterprise can save a great number of transportation, inventory and warehousing costs associated with product return by optimal handing the product returns. This paper proposes a nonlinear mixed-integer reverse logistics model which focus on the problem of determining the number and location of centralized return centers and where returned products from retailers or end-customers were collected, sorted, and consolidated into a large shipment destined for manufactures’ or distributors’ repair facilities. To solve the model an improved particle swarm optimization be introduced. The result indicates that the improvement PSO algorithm is an effective method of solving the problem. [Copyright &y& Elsevier]

Abstract: A mathematical model of optimal scheduling scheme for gas pipeline network is established, which takes maximum benefits of pipeline network service provider as objective function after comprehensively considered the resources of gas field, downstream gas utilizing conditions, operating parameters of compressor stations and work conditions of pipeline system etc. Evolutionary-based particle swarm algorithm has been investigated, and a new hybrid particle swarm algorithm with embedded chaotic search has been presented to the optimization solution of pipeline network scheduling. Improved algorithm makes use of the ergodicity of chaos to improve the capability of precise search and keep the balance between the global search and the local search, which is able to avoid the premature convergence problem effectively. This paper took a gas network with 17 nodes, 16 pipelines, 2 compressor stations as example, and make the decision on the optimal scheduling scheme for gas pipeline network. The analysis of example demonstrates the effectiveness and applicability of the established model and algorithm. [Copyright &y& Elsevier]

Abstract: In the paper, least squares support vector machine trained by chaos particle swarm optimization (CPSO-LSSVM) is applied in the classification. Chaos particle swarm optimization algorithm is introduced to select the suitable training parameters of least squares support vector machine. That is because chaos particle swarm optimization algorithm can avoid being trapped in local optimum and gaining the global optimum quickly in the process of search by combining particle swarm optimization with chaos queues. The experimental results demonstrate that least squares support vector machine trained by chaos particle swarm optimization is best classification results in the three classification algorithms. [Copyright &y& Elsevier]

Abstract: A new quantum-behaved particle swarm optimization algorithm with inverse proportional inertia weight is presented and proved in this paper. Also a robust design method of optimization is used by optimizing the robustness of both the system objective function and constraint functions in solving the problem of breach of condition to the constraint functions. Comparing the solution of the traditional optimization and the solution of the robust optimal design, the robust design parameters display a significant stability in performance when the value of the parameters changed and all the optimizations are solved by this new efficient algorithm. [Copyright &y& Elsevier]

Abstract: In order to apply cockroach swarm optimization (CSO) algorithm to more function optimization and improve its accuracy, this paper improve CSO by modifying chase-swarming behavior of cockroach, and apply the modified CSO to optimize several Benchmark Problems. The results of simulation experiments illustrate that modified CSO has stronger convergence performance and highly-accuracy optimization results compared with Particle Swarm Optimization (PSO) and Chaotic Particle Swarm Optimization (CPSO). [Copyright &y& Elsevier]