imperialist competitive algorithm (ica)

Improving transient voltage stability is one of the most important issues that must be provided by doubly fed induction generator (DFIG)-based wind farms (WFs) according to the grid code requirement. This paper proposes adjusted DC-link chopper based passive voltage compensator and modified transient voltage controller (MTVC) based active voltage compensator for improving transient voltage stability. MTVC is a controller-based approach, in which by following a voltage dip (VD) condition, the voltage stability for the WF can be improved. In this approach, a voltage dip index (VDI) is proposed to activate/deactivate the control strategy, in which, two threshold values are used. In the active mode, the active and reactive power are changed to decrease the rotor current and boost the PCC voltage, respectively. Based on the control strategy, in a faulty grid, DFIG not only will be able to smooth DC-link voltage fluctuations and reduces rotor overcurrents but also it will increase the voltage of point of common coupling (PCC). Therefore, it improves transient voltage stability. The simulation results show the effectiveness of the proposed strategy for improving voltage stability in the DFIG.</span></span></span></div>

Search-based optimization methods have been used for software engineering activities such as software testing. In the field of software testing, search-based test data generation refers to application of meta-heuristic optimization methods to generate test data that cover the code space of a program. Automatic test data generation that can cover all the paths of software is known as a major challenge.
The paper establishes a new cost function for automatic test data generation, which can traverse the non-iterative paths of software control flow graphs. This function is later compared with similar cost functions proposed in other articles. The results indicate the superior performance of the proposed function. Still another innovation in this paper is the application of the Imperialist Competitive Algorithm in automatic test data generation along with the proposed cost function. Automatic test data generation is implemented through the Imperialist Competitive Algorithm as well as the Genetic and Particle Swarm Optimization Algorithms for three software programs with different search space sizes. The algorithms are compared with each other in terms of convergence speed, computational time, and local search. Test data generated by the proposed method has achieved better results than other algorithms in finding the number of non-iterative paths, the convergence speed and computational time with growing the searching space of the software's control flow graph.

In this paper, a novel compensator based on Magnetically Controlled Reactor with Fixed Capacitor banks (FC-MCR) is introduced and then power system stability in presence of this compensator has been studied using intelligent control method. The problem of robust FC-MCR based damping controller design is formulated as a multi-objective optimization problem. The multi-objective problem is concoctedto optimize a composite set of two eigenvalue-based objective functions comprising the desired damping factor, and the desired damping ratio of the lightly damped and undamped electromechanical modes.The controller is automatically tuned with optimization of an eigenvalue based multi-objective function by ICAto simultaneously shift the lightly damped and undamped electromechanical modes to a prescribed zone in the s-plane so that the relative stability is guaranteed and the time domain specifications concurrently secured. The effectiveness of the proposed controller is demonstrated through eigenvalue analysis, nonlinear time simulation studies and some performance indices to damp low frequency oscillations under different operating conditions. The results show that the tuned ICA based FC-MCR controller which is designed by using the proposed multi-objective function has an outstanding capability in damping power system low frequency oscillations and significantly improves the power systems dynamic stability.

In this paper, optimization of the backstepping controller parameters in a grid-connected single-phase inverter is studied using Imperialist competitive algorithm (ICA), Genetic Algorithm (GA) and Particle swarm optimization (PSO) algorithm. The controller is developed for the system based on state-space averaged model. By selection of a suitable Lyapunov function, stability of the proposed controller is proved in a wide range of operation. Considering different optimization algorithms, steady-state and dynamic responses of the developed system are studied. In addition, THD values for different test are compared. Finally, to verify accuracy of the proposed method, designed controller is simulated using MATLAB/Simulink software.

Optimal distribution substation placement is one of the major components of optimal distribution system planning projects. In this paper optimal substation placement problem is solved using Imperialist Competitive Algorithm (ICA) as a new developed heuristic optimization algorithm. This procedure gives the optimal size, site and installation time of medium voltage substation, using their related costs subject to operating and optimization constraints. A multistage and pseudo-dynamic expansion planning is applied to consider dynamic of the system parameters for example, load forecasting uncertainty, asset management and geographical constraints. In order to evaluate the effectiveness of the proposed method a sensitivity analysis of ICA parameters on obtained results is done. A graphical representation of obtained results is used to show the efficiency and capability of the proposed method both from the planning view and graphical aspects. The results show the efficiency and capability of the proposed method which has been tested on a real size distribution network.

Microgrids are small-scale, low voltage (LV) power networks which employ renewable distribution energy resources (DERs) with power electronic interfaces (PEIs). Microgrids as single controlled units and active distribution networks require flexible control systems to ensure reliable and secure operation in different modes. These various operations of microgrid cause variations in voltage and frequency especially in island mode. In this paper, a new control method with two optimization algorithms (genetic algorithm (GA) & imperialist competitive algorithm (ICA)) are proposed to eliminate both voltage and frequency disturbances. Also, a new concept of conventional droop control in format of fast droop controller (FDC) is designed to guaranty the microgrid system reliability with cooperation of a modern frequency controller. Simulation results show the truth behavior of proposed approach in comparison with previous methods

Measurement of magnitude and frequency of the voltage flicker is very important for monitoring and controlling voltage flicker efficiently to improve the network power quality. This paper presents two new methods for measurement of flicker signal parameters using Shuffled Frog Leaping Algorithm (SFLA) and Imperialist Competitive Algorithm (ICA). This paper estimates fundamental voltage and flicker magnitudes and frequencies with proposed methods. The goal is to minimize the error of the estimated magnitudes and frequencies via a designed fitness function. At first, we introduce voltage flicker and its measuring techniques. Then voltage flicker model is analyzed. At the next part, a review of SFLA and ICA is presented. These methods will be applied to a test voltage signal and the results are be analyzed.