Parameter Estimation for Composite Load Model Using a Hybrid Searching Method

Lotka-Volterra competition model has been applied in life sciences including competition between species, predicting the Aeromonas hydrophila growth on fish surface. The competition model also has been used in social sciences, including competition in the Korean stock market and competition between two types of bank, namely commercial bank and rural bank in Indonesia. It is well-known that the analytical solution of the Lotka-Volterra is unknown. Here gradient-base methods such as Newton method and Levenberg-Marquardt are difficult to be implemented to estimate paramaters of the model. In order to estimate parameters in the model, one need to use heuristic method such as genetic algorithm, particle swarm optimization, firefly algorithm or other heuristic methods. In this paper, we compared performance of particle swarm optimization and firefly algorithm in parameter estimation of Lotka-Volterra type competition model. Here we used the profit data of commercial bank and rural bank, where the data cited from literature. We found the mean absolute percentage error (MAPE) of firefly algorithm is a little bit smaller than the error of particle swarm optimization method. We also found variance of the error of firefly algorithm is lower than the particle swarm optimization method. Hence, for parameter estimation of Lotka-Volterra competition model, firefly algorithm is more beneficial than the particle swarm optimization method.

A new combination of particle-swarm optimization (PSO) and the least-squares support vector machine (LS-SVM) technique for FDTD time-series forecasting is presented. In this paper, the PSO is extended to optimize the hyperparameter used in the LS-SVM algorithm. The numerical simulations demonstrate that the PSO method can efficiently obtain the optimal value of the hyperparameter used in the LS-SVM algorithm. And the PSO_LS-SVM method can improve the computational efficiency of the FDTD algorithm, as compared to the direct FDTD method. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 48: 141–144, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.21288

This paper presents an accurate and easy-to-implement algorithm for estimating the parameters of the asymmetric Gaussian chirplet model (AGCM) used for modeling echoes measured in ultrasonic nondestructive testing (NDT) of materials. The proposed algorithm is a combination of particle swarm optimization (PSO) and Levenberg–Marquardt (LM) algorithms. PSO does not need an accurate initial guess and quickly converges to a reasonable output while LM needs a good initial guess in order to provide an accurate output. In the combined algorithm, PSO is run first to provide a rough estimate of the output and this result is consequently inputted to the LM algorithm for more accurate estimation of parameters. To apply the algorithm to signals with multiple echoes, the space alternating generalized expectation maximization (SAGE) is used. The proposed combined algorithm is robust and accurate. To examine the performance of the proposed algorithm, it is applied to a number of simulated echoes having various signal to noise ratios. The combined algorithm is also applied to a number of experimental ultrasonic signals. The results corroborate the accuracy and reliability of the proposed combined algorithm.

Beetle Antennae Search (BAS) is an intelligent algorithm that mimics the beetle preying process. In BAS, there is a beetle which has a left and a right antennae sensing the intensity of the odor emitted by the food. The beetle learns the different sense of two antennas to determine the flight and finally finds the food. The BAS algorithm has been proved to have good optimizing speed and precision when it is applied for low-dimensional optimization problems. However, when solving high-dimensional problems, the algorithm is easily trapped into local optimum. In order to improve the optimization ability of BAS, we have combined particle swarm optimization (PSO) algorithm and proposed a new hybrid BAS and PSO algorithm (BAS-PSO). In BAS-PSO, firstly, the standard PSO is used for particle velocity and position updating. Particles learn ontology information and group optimal information for evolution. Then each particle in the swarm is taken as a beetle independently applying a local search with BAS algorithm. To verify the performance of BAS-PSO, four benchmark functions have been selected in our simulation experiments. The results show that the performance of BAS-PSO is better than BAS and PSO.

This paper proposes an advanced control strategy for DFIG based Wind Turbine. The designed method is based on a combination of Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN). PSO combined ANN is suggested to track the available maximum power point (MPPT) at varying wind speeds. Thus, PSO is used to improve the dynamic performance of Doubly Fed Induction Generator (DFIG) by optimizing its Proportional Integral (PI) controller gains. This work highlights the performance of the PSO optimized PI controller against classical one. The proposed control strategy is verified via 2 MW DFIG-WT using the MATLAB/Simulink environment. The obtained results validate the proposed PSO-PI as an effective tool for improving the dynamic behavior of DFIGWT, it reveals that the overshoots are reduced by 50 % compared to the classical PI. In addition, a faster transient response is achieved using the proposed control strategy.

Economic dispatch (ED) is one of the optimization problems in power system operation and planning. The practical ED problems have non-smooth cost functions with equality and inequality constraints. It becomes more complicated problem when valve point effects of generator are considered and results multiple local minima. This makes the ED problems are difficult to find global optimum solution when using any mathematical approach due to non-convex cost function. The Classical particle swarm optimization (PSO) has ability to reach near global optimum solution by tuning some parameters but always stuck at local minima. In this paper, an iteration particle swarm optimization (IPSO) is proposed to solve ED problems with valve point effects. The proposed algorithm is introduced a new parameter in the original velocity equation of PSO in order to enrich searching behaviour, solution quality and avoid being trapped at local minima. The proposed IPSO algorithm has been implemented on two test power systems (consisting 3 and 13 generating units) to validate its effectiveness. The simulation results confirmed that IPSO algorithm has better convergence characteristic and more robust compared to PSO and some published results.

In order to reduce the adverse effects of uncertainty on optimal dispatch in active distribution network, an optimal dispatch model based on chance-constrained programming is proposed in this paper. In this model, the active and reactive power of DG can be dispatched at the aim of reducing the operating cost. The effect of operation strategy on the cost can be reflected in the objective which contains the cost of network loss, DG curtailment, DG reactive power ancillary service, and power quality compensation. At the same time, the probabilistic constraints can reflect the operation risk degree. Then the optimal dispatch model is simplified as a series of single stage model which can avoid large variable dimension and improve the convergence speed. And the single stage model is solved using a combination of particle swarm optimization (PSO) and point estimate method (PEM). Finally, the proposed optimal dispatch model and method is verified by the IEEE33 test system.

For a swarm of mobile robots in an unknown environment, the most challenging task is to plan the optimal path and also to learn the environmental parameters. Machine learning is one of the solutions to this problem. This paper proposes a combination of particle swarm optimization (PSO) and Q-value based reinforcement learning (Q-Learning) for a swarm of mobile robots to find the optimal path in an unknown environment and to learn the environment. Q-learning combined with PSO enable the robots to learn the unknown environment with reward and action selection policy while reducing the time to find the optimal path in the environment using the iterative improvement method of PSO. The proposed algorithm is simulated and found performing faster than Q-learning and PSO performing alone. We also compared with some other established algorithm where the proposed algorithm outperforms each of them in accuracy and speed.

Networked synchronization control has very high technology level, which includes network technology and synchronization control technology, etc. Fault diagnosis of the devices in networked synchronization control system has a great importance for ensuring the normal operation. The radial basis function neural network with particle swarm optimization algorithm is developed. The combination method of RBF neural network and particle swarm optimization is applied to fault diagnosis of networked synchronization control system. The test results indicate that the combination model of RBF neural network and particle swarm optimization can almost entirely recognize each state of the device in networked synchronization control system. The diagnostic accuracy of the combination model of RBF neural network and particle swarm optimization is greater than that of normal RBF neural network.

The particle swarm optimization (PSO) method was originally designed by Kennedy and Eberhart in 1995 and has been applied successfully to various optimization problems. The PSO idea is inspired by natural concepts such as fish schooling, bird flocking and human social relations. Some experimental results show that PSO has greater "global search" ability, but the "local search" ability around the optimum is not very good. This paper analyses the PSO method and presents the improved method, which is PSO with division of work (PSOwDOW). In order to enhance the 'local search" ability of PSO we divide the particle swarm into three sub swarms and each sub swarm has a different job in PSOwDOW. Experimental results show that PSOwDOW can overcome the deficiencies in the traditional PSO and reinforce the optimizing ability of the particle swarm.

The particle swarm optimization (PSO) method is a population-based optimization technique which searches for solutions by updating simultaneously a number of candidate solutions called particles. Since, in the PSO, the exploration ability is important to find a desirable solution, various kinds of methods have been investigated to improve it. In this paper, we propose a new chaotic dynamical system for PSO having sinusoidal perturbations, in which chaotic particles can be expected to search for solutions around the two tentative solutions by using chaotic sequences. Through computational experiments, by applying them to some global optimization problems, we compare the proposed chaotic PSO with the standard one

In this paper, a hybrid method is proposed for reducing the amount of load shedding and voltage collapse. The hybrid method is the combination of particle swarm optimisation (PSO) algorithm and artificial bee colony (ABC) algorithm. Both algorithms equally contribute in the paper to determine the optimal load shed values for improving the voltage stability of the power system. Here, PSO is used to determine the weak buses in the system whereas ABC computes the optimal amount of load shed value to be cut for voltage stability purpose. They work on different fitness functions as per the requirements of their purpose. The appropriate buses for load shedding are selected based on the sensitivity of minimum eigenvalue of load flow Jacobian with respect to the load shed. The proposed method based on PSO and ABC is implemented in MATLAB working platform and its performance is tested with six bus and IEEE 14 bus bench mark test systems. From the simulations, the performance of the proposed method is analysed and compared with various techniques in terms of sensitivity of minimum eigenvalues, voltage profile and load power. Through comparison performance, it is showed that the proposed method ensures voltage stability with minimum load shedding.

The design optimization of two-stage cylindrical helical gear reducer is discussed in this paper. The volume and the center gear distance are adopted as objective functions separately, and the particle swarm optimization(PSO) method is used to improve the design quality. The design variables contain discrete variables, which are converted to discrete variables by the nearby principle in order to improve the running efficiency. Inertia weight coefficient is used in the PSO algorithm to adjust the coverage speed. The optimization result shows that our method is better than other optimization method, and it proves that PSO method is meaningful to the reducer’s design.

This paper investigates the use of nonresonant LC loads in the design of multi-band antennas. In this approach, the loads are made to have inductive reactances at some operating frequencies and capacitive reactances at other frequencies; the loads are never resonant. This has the effect of increasing or decreasing the effective electrical length of the antenna. Also, the entire structure supports significant radiating currents at all operating frequencies. Although this approach is not new, the design process has historically been relatively complicated or has relied on trial-and-error. A much more straightforward and automatic approach based on the particle swarm optimization (PSO) method is described here. The PSO method was chosen because of its speed and relatively simple implementation. The focus is on achieving an input impedance with zero reactance and not on obtaining a specific radiation pattern shape, since the patterns of short monopoles and dipoles are essentially invariant with respect to length

To determine propagation characteristics in buildings for fifth-generation (5G) wireless communication, it is necessary to obtain the complex permittivity of the construction materials at millimeter-wave band. Backward reconstruction of complex permittivity of construction materials in free space at millimeter-waves remains a challenging and up to date complex problem. A procedure for the estimation of the complex permittivity of construction materials based on Particle Swarm Optimization (PSO) method is presented in this paper. The complex permittivity is extracted from the transmission and reflection coefficients for perpendicular polarization over 40-50 GHz, and verified by reflection coefficients for parallel polarization. This method is relatively precise and fast which gives a good estimation of complex permittivity.