International Journal of Innovative Computing and Applications (8 papers in press)
Optimal Placement of Unified Power Flow Controller using Differential Search Algorithm
by Dhiman Banerjee, Sriparna Bhattacharya, Provas Kumar Roy
Abstract: Optimal power flow (OPF) problem plays a crucial role to run an economically efficient and well-planned power system. It is a strenuous and challenging task for the power system researchers to cope with the ever-increasing load-demand while getting the minimum system loss. The development of flexible ac transmission system (FACTS) has added a new dimension both to the system operation and research. Unified power flow controller (UPFC) is the most reliable FACTS controller, having its operational capability as series and shunt compensator. As a matter of fact, UPFC can reliably control the different power system parameters. In this article, UPFC is incorporated into the modified IEEE 5-bus and modified IEEE 30-bus test system. Differential search algorithm (DSA) is proposed and implemented to run the OPF with and without UPFC , and the results are listed, analyzed and compared with the same that is obtained by genetic algorithm (GA) and BAT search algorithm.
Keywords: Differential search algorithm; Optimal power flow; FACTS devices; UPFC.
Development of Fuzzy Logic Controller for Improved Interline Unified Power Quality Conditioner
by Ravindranath Tagore Yadlapalli, Rajanand Patnaik Narasipuram, Anusha Dodda
Abstract: This paper presents the improved interline Unified Power Quality Conditioner (iUPQC) and its controlling aspects for nonlinear loads. The nonlinear loads are the major sources of harmonics and raise the power quality issues. However, the iUPQC compensates the harmonics that are generated by a nonlinear load besides reactive power support. This in turn minimizes the harmonic distortion both in the source current as well as voltage. Furthermore, it also provides the current and voltage imbalance compensations, reactive power, frequency and voltage support at grid. The simulation of entire power system is fulfilled using proposed FUZZY controller and compared with the conventional PI controller. The performance of both the controllers is sifted in terms of %Total Harmonic Distortion (THD) by considering different case studies having the 3-ϕ diode rectifier connected to R, RL & RLE loads. The MATLAB/Simulink version R2012b is used for accomplishing the in depth simulation studies.
Keywords: interline dynamic voltage restorer,IDVR; improved interline Unified Power Quality Conditioner; iUPQC; interline Voltage Controller; IVOLCON; Fuzzy logic Controller (FLC); Proportional Integral (PI) controller; Total Harmonic Distortion; THD%.
Special Issue on: Recent Advances in Memetic Computing
Hand Motions Recognition Based on sEMG Nonlinear Feature and Time Domain Feature fusion
by Jiahan Li, Gongfa Li, Ying Sun, Guozhang Jiang, Bo Tao, Shuang Xu
Abstract: In recent years, the development of many rehabilitation robots, bionic prostheses and other sports rehabilitation equipment, which are used to assist the body to restore body movement function, has been paid more and more attention. In the development and design of the current sports rehabilitation equipment or biomimetic prostheses. The classification framework of this paper is a pattern recognition framework. The feature extraction of sEMG is to extract the physical quantity or a set of physical features that fully represent the characteristics of the action class from the electromyogram corresponding to the action of the human hand, in order to distinguish the other types of motion. It's very important step in hand movement recognition. In this paper, the newly developed sEMG nonlinear features AMR are fused with the traditional sEMG time-domain features WL. Feature fusion using SVM-DS fusion algorithm. Hand motions recognition based on feature fusion is improved in accuracy and stability. The accuracy of recognition can be stabilized over 95%.
Keywords: pattern recognition; feature extraction; SVM; D-S evidence theory; Feature fusion; sEMG.
An enhanced cuckoo search using dimension selection
by Lijin Wang
Abstract: This paper proposes an enhanced cuckoo search algorithm using dimension selection. In the proposed strategy, the dimensional distance measure is used to selects a part of dimensions of each solution to search for the new solution in two search components. The dimensions of each solution are selected when those dimensional distances are larger than the average distance of all dimensional distance. A suit of 20 benchmark functions are employed to verify the performance of the proposed algorithm, and the results show the improvement in effectiveness and efficiency of dimension selection.
Keywords: cuckoo search algorithm; dimension selection; dimensional distance; average distance; crossover operator.
A Novel Firefly Algorithm with Self-Adaptive Step Strategy
by Wang Jing
Abstract: In the standard firefly algorithm, the random moving step is very important to the direction of the firefly movement, and the parameter alpha plays an important role in the random moving step. In this paper, we proposed a self-adaptive Step Strategy based on distance control in this paper, and we called it SASFA. Thirteen well-known benchmark functions are used to verify the performance of our proposed method, the computational results show that SASFA is more efficient than many other FA algorithms.
Keywords: firefly algorithm; random moving step; meta-heuristic algorithm; Global optimization;.
A new artificial bee colony based on neighborhood selection
by Xiaoyan Xiong, Jun Tang
Abstract: In this paper, we present a new artificial bee colony (ABC) for solving numerical optimization problems. In the original ABC, a new candidate solution is generated based on the current solution and a randomly selected one. However, the random selection method is unstable. To accelerate the search, a new neighborhood selection is proposed. For each current solution, we firstly randomly select some solutions from the current population. Then, we choose the best one among those solutions as the neighborhood solution to generate new solutions. To verify the performance, we test several classical numerical optimization problems. Simulation results show that our approach outperforms the original ABC and some improved ABC versions.
Keywords: artificial bee colony; neighborhood selection; opposition; global optimization.
One-dimensional deep learning firefly algorithm guided by the best particle
by Zhifeng Xie, Jia Zhao, Hui Sun, Jun Ye, Jiajia Wang, Huasheng Zhu
Abstract: We propose the one-dimensional deep learning firefly algorithm guided by the best particle in order to increase the convergence speed and optimization precision of the firefly algorithm. In each generation of optimization process, the optimal particle is first updated in a fixed number of times according to the newly designed update formula. The update mode is defined as single-dimensional deep learning. After the optimal particle completes single-dimensional deep learning, other fireflies in the population keep the original evolutionary way to update the location and iteratively complete the optimization task. Experiments with 12 benchmark functions show that the proposed algorithm has a higher optimization capacity than the other six modified firefly algorithms.
Keywords: one-dimensional deep learning; firefly algorithm; the optimal particle.
Gaussian bare-bones firefly algorithm
by Hu Peng, Shunxu Peng
Abstract: Firefly algorithm (FA), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimization. However, the premature convergence has greatly affected the performance of original FA. To overcome this problem, we proposed a Gaussian bare-bones FA, named GBFA, in which each firefly moves to a Gaussian bare-bones method generated learning object rather than its better neighbors. The experiments are conducted on a set of widely used benchmark functions. Experimental results and comparison with the state-of-the-art FA variants have proved that the proposed algorithm is promising.
Keywords: Firefly algorithm; Swarm intelligence; Gaussian bare-bones; Global optimization.