Nonlinear Time-varying Evolution Research Articles

Adaptive-Uniform-Experimental-Design-Based Fractional-Order Particle Swarm Optimizer with Non-Linear Time-Varying Evolution

An adaptive-uniform-experimental-design-based fractional particle swarm optimizer (AUFPSO) with non-linear time-varying evolution (NTE) is proposed. A particle swarm optimizer (PSO) is an excellent evolutionary algorithm due to its simple structure and rapid convergence. Nevertheless, PSO has notable drawbacks. Although many proposed methods and strategies have enhanced its effectiveness and performance, PSO is limited by its tendency to fall into local optima and its tendency to obtain different solutions in each search (i.e., its weak robustness). Introducing fractional-order calculus in PSO (FPSO) can correct the order of the velocity derivative for each particle, which enhances the diversity and algorithmic effectiveness. This study used NTE of the order of the velocity derivative, inertia weight, cognitive parameter, and social parameter in an FPSO used to search for a global optimal solution. To obtain the best combination of FPSO and NTE, an adaptive uniform experimental design (AUED) method was used to deal with this essential issue. The AUED method integrates a uniform layout with the best combination phase and a stepwise ratio to assist in selecting the best combination for FPSO-NTE. Experimental applications in 15 global numerical optimization problems confirmed that the AUFPSO-NTE had a better performance and robustness than existing PSO-related algorithms.

Attuned design of demand response program and M-FACTS for relieving congestion in a restructured market environment

This paper addresses the attuned use of multiconverter flexible alternative current transmission systems (M-FACTS) devices and demand response (DR) to perform congestion management (CM) in the deregulated environment. The strong control capability of the M-FACTS offers a great potential in solving many of the problems facing electric utilities. Besides, DR is a novel procedure that can be an effective tool for reduction of congestion. A market clearing procedure is conducted based on maximizing social welfare (SW) and congestion as network constraint is paid by using concurrently the DR and M-FACTS. A multi-objective problem (MOP) based on the sum of the payments received by the generators for changing their output, the total payment received by DR participants to reduce their load and M-FACTS cost is systematized. For the solution of this problem a nonlinear time-varying evolution (NTVE) based multi-objective particle swarm optimization (MOPSO) style is formed. Fuzzy decision-making (FDM) and technique for order preference by similarity to ideal solution (TOPSIS) approaches are employed for finding the best compromise solution from the set of Pareto-solutions obtained through multi-objective particle swarm optimization-nonlinear time-varying evolution (MOPSO-NTVE). In a real power system, Azarbaijan regional power system of Iran, comparative analysis of the results obtained from the application of the DR & unified power flow controller (UPFC) and the DR & M-FACTS are presented.

The minimization of voltage sag effect for specially connected transformers with a sensitive load and distributed generation systems

Abstract This paper improves the voltage sag for a distributed generation (DG) system with three-phase to two-phase connected transformers. The proposed connection system is compared with the systems designed by Scott and Le Blanc. A particle swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is used to determine the design values. This analysis considers sensitive loads and the coordination of over-current relays. The model uses a node admittance matrix for voltage sag calculation, in order to calculate the severity of system sag after single or two-phase faults. The analytical equations are useful in the mathematical optimal planning method. The PSO-NTVE is then used to derive suitable transformer impedances and relay time multiplier parameter values. The search for a global optimal solution for the voltage sag problems involves tests on the Scott and Le Blanc connected transformer power systems, in order to verify the effectiveness of the proposed method. Finally, the results show that the severity of the voltage sag is reduced by tuning the transformer impedances and relay settings.

Optimal harmonic filters design of the Taiwan high speed rail traction system of distributer generation system with specially connected transformers

This paper presents a method for combining a particle swarm optimization with nonlinear time-varying evolution and orthogonal arrays (PSO-NTVEOA) in the planning of harmonic filters for the high speed railway traction system of Distributed Generation (DG) system with specially connected transformers in unbalanced three-phase power systems. The objective is to minimize the cost of the filter, the filters loss, the total harmonic distortion of currents and voltages at each bus simultaneously. An orthogonal array is first conducted to obtain the initial solution set. The set is then treated as the initial training sample. Next, the PSO-NTVEOA method parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. This PSO-NTVEOA method is then applied to design optimal harmonic filters in Taiwan HSR traction system, where both rectifiers and inverters with IGBT are used. From the results of the illustrative examples, the feasibility of the PSO-NTVEOA to design an optimal passive harmonic filter of Taiwan HSR system is verified and the design approach can greatly reduce the harmonic distortion. Three design schemes are compared that V–V connection suppressing the 3rd order harmonic and Scott connection for the harmonic improvement is better than those of V–V and Le Blanc connection.

FDM based multi-objective optimal sitting and design of TC-FLSFCL for study of distribution system reliability

An approach for optimal placement of flux-lock type SFCL with tap changer (TC-FLSFCL) and adjustment of tap changer related to it is used in this paper by debating the reduction of fault current and enhancement of reliability in a distribution network connected with distributed generation (DG). Recently, various superconducting fault current limiters (SFCL) have been developed in the field of power system. Especially, through researches, SFCLs have been put to practical use in distribution network as well as in the transmission network. TC-FLSFCL is a flexible SFCL that it has some preference than previous SFCLs. In this type of SFCL the current limiting characteristics are improved and the fault current limiting level during a fault period can be adjusted by controlling the current in the third winding, which also made the magnetic field applied to the high-Tc superconducting (HTSC) element. Three objective functions based on reliability index, reduction of fault current and the number of installed TC-FLSFCL are systematized and a nonlinear time-varying evolution (NTVE) based multi-objective particle swarm optimization (MOPSO) style is then formed in searching for the best location and tuning of tap changer of TC-FLSFCL to meet the fitness requirements. A decision-making procedure based on fuzzy decision making (FDM) is used for finding the best compromise solution from the set of Pareto-solutions obtained through MOPSO-NTVE. In a distribution network, Bus 4 of Roy Billinton test system (RBTS), comparative analysis of the results obtained from application of the resistive SFCL (RSFCL) and TC-FLSFCL is presented. The results show that optimal placement of TC-FLSFCL than RSFCL can improve reliability index and fault current reduction index with less number.

Parameter estimation of chaotic systems by a nonlinear time-varying evolution PSO method

An important issue in nonlinear science is parameter estimation for Lorenz chaotic systems. There has been increasing interest in this issue in various research fields, and it could essentially be formulated as a multidimensional optimization problem. A novel evolutionary computation algorithm, nonlinear time-varying evolution particle swarm optimization (NTVEPSO), is employed to estimate these parameters. In the NTVEPSO method, the nonlinear time-varying evolution functions are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates tothe full factorial experiments. The NTVEPSO method and other PSO methods are then applied to identify the Lorenz chaotic system. Simulation results demonstrate the feasibility and superiority of the proposed NTVEPSO method.

Optimal the tilt angles for photovoltaic modules using PSO method with nonlinear time-varying evolution

A particle-swarm optimization method with nonlinear time-varying evolution (PSO–NTVE) is employed in determining the tilt angle of photovoltaic (PV) modules in Taiwan. The objective is to maximize the output electrical energy of the modules. In this study, seven Taiwanese cities were selected for analysis. First, the sun's position at any time and location was predicted by the mathematical procedure of Julian dating, and then the solar irradiation was obtained at each site under a clear sky. By combining the temperature effect, the PSO–NTVE method is adopted to calculate the optimal tilt angles for fixed south-facing PV modules. In this method, the parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments have an effect that approximates the full factorial experiments. Statistical error analysis was performed to compare the results between the four PSO methods and experimental results. Hengchun city in which the minimum total error value of 6.12% the reasons for the weather more stability and less building shade. A comparison of the measurement results in electrical energy between the four PSO methods and the PV modules set a six tilt angles. Obviously four types of PSO methods simulation of electrical energy value from 231.12 kWh/m2 for Taipei to 233.81 kWh/m2 for Hengchun greater than the measurement values from 224.71 kWh/m2 for Taichung to 228.47 kWh/m2 for Hengchun by PV module which is due to instability caused by climate change. Finally, the results show that the annual optimal angle for the Taipei area is 18.16°; for Taichung, 17.3°; for Tainan, 16.15°; for Kaosiung, 15.79°; for Hengchung, 15.17°; for Hualian, 17.16°; and for Taitung, 15.94°. It is evident that the authorized Industrial Technology Research Institute (ITRI) recommends that tilt angle of 23.5° was not an appropriate use of Taiwan's seven cities. PV modules with the installation of the tilt angle should be adjusted in different locations.

Optimal the tilt angles for photovoltaic modules in Taiwan

A particle-swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in determining the tilt angle of photovoltaic (PV) modules in Taiwan. The objective is to maximize the output electrical energy of the modules. In this study, seven Taiwanese cities were selected for analysis. First, the position of the Sun at any time and location was predicted by the mathematical procedure of Julian dating; then, the solar irradiation was obtained at each site under a clear sky. By combining the temperature effect, the PSO-NTVE method is adopted to calculate the optimal tilt angles for fixed south-facing PV modules. In this method, the parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments have an effect that approximates the full factorial experiments. A comparison of the measurement results in electrical energy between the four PSO methods and the PV modules set a six tilt angles. The results indicated that the annual solar radiation has a maximum value of 2658.69 kW h/m 2 in Hengchun, and the conversion efficiency of the modules ranges from 13.14% for Taitung to 13.39% for Hengchun. Also, it should be noticed that the annual efficiency for Hengchun is higher than that for Kaohsiung because the average monthly PV-module temperature of Kaohsiung is higher than of Hengchun at this time. The results show that the annual optimal angle for the Taipei area is 18.16 ° ; for Taichung, 17.3 ° ; for Tainan, 16.15 ° ; for Kaosiung, 15.79 ° ; for Hengchung, 15.17 ° ; for Hualian, 17.16 ° ; and for Taitung, 15.94 ° .

A PSO method with nonlinear time-varying evolution for optimal design of PID controllers in a pendubot system

A particle swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in designing an optimal PID controller for asymptotic stabilization of a pendubot system. In the PSO-NTVE method, parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. The PSO-NTVE method and other PSO methods are then applied to design an optimal PID controller in a pendubot system. Comparing the simulation results, the feasibility and the superiority of the PSO-NTVE method are verified.

Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm

The time series prediction of a practical power system is investigated in this paper. The radial basis function neural network (RBFNN) with a nonlinear time-varying evolution particle swarm optimization (NTVE-PSO) algorithm is developed. When training RBFNNs, the NTVE-PSO method is adopted to determine the optimal structure of the RBFNN to predict time series, in which the NTVE-PSO algorithm is a dynamically adaptive optimization approach using the nonlinear time-varying evolutionary functions for adjusting inertia and acceleration coefficients. The proposed PSO method will expedite convergence toward the global optimum during the iterations. To compare the performance of the proposed NTVE-PSO method with existing PSO methods, the different practical load types of Taiwan power system (Taipower) are utilized for time series prediction of one-day ahead and five-days ahead. Simulation results illustrate that the proposed NTVE-PSO-RBFNN has better forecasting accuracy and computational efficiency for different electricity demands than the other PSO-RBFNNs.

A PSO Method With Nonlinear Time-Varying Evolution for Optimal Design of Harmonic Filters

A particle swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in the planning of large-scale passive harmonic filters for a multibus system under abundant harmonic current sources. The objective is to minimize the cost of the filter, the filters loss, the total harmonic distortion of currents and voltages at each bus simultaneously. In the PSO-NTVE method, parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. To compare the performance of the proposed PSO-NTVE method with existing ones, five well-known benchmarks are used and the results reveal its superiority over the others. This PSO-NTVE method is then applied to design optimal harmonic filters in a steel plant, where both ac and dc arc furnaces are used and a static var compensator (SVC) is installed. From the results of the illustrative examples, the feasibility of the PSO-NTVE to design an optimal passive harmonic filter of a multibus system is verified.

A PSO method with nonlinear time-varying evolution based on neural network for design of optimal harmonic filters

A particle swarm optimization method with nonlinear time-varying evolution based on neural network (PSO-NTVENN) is proposed to design large-scale passive harmonic filters (PHF) under abundant harmonic current sources. The goal is to minimize the cost of the filters, the filters loss, and the total harmonic distortion of currents and voltages at each bus, simultaneously. In the PSO-NTVENN method, parameters are determined by using a sequential neural network approximation. Meanwhile, based on the concept of multi-objective optimization, how to define the fitness function of the PSO to include different performance criteria is also discussed. To show the feasibility of the proposed method, illustrative examples of designing optimal passive harmonic filters for a chemical plant are presented.

An orthogonal-array-based particle swarm optimizer with nonlinear time-varying evolution

Particle swarm optimization (PSO) is a population-based heuristic optimization technique. It has been developed to be a prominent evolution algorithm due to its simplicity of implementation and ability to quickly converge to a reasonable solution. However, it has also been reported that the algorithm has a tendency to get stuck in a near-optimal solution in multi-dimensional spaces. To overcome the stagnation in searching a globally optimal solution, a PSO method with nonlinear time-varying evolution (PSO-NTVE) is proposed to approach the optimal solution closely. When determining the parameters in the proposed method, matrix experiments with an orthogonal array are utilized, in which a minimal number of experiments would have an effect that approximates the full factorial experiments. To demonstrate the performance of the proposed PSO-NTVE method, five well-known benchmarks are used for illustration. The results will show the feasibility and validity of the proposed method and its superiority over several previous PSO algorithms.