Mohammad Rasoul Narimani

A new multi objective optimization approach based on TLBO for location of automatic voltage regulators in distribution systems

This paper proposes a new multi-objective optimization algorithm based on modified teaching-learning-based optimization (MTLBO) algorithm in order to solve the optimal location of automatic voltage regulators (AVRs) in distribution systems at presence of distributed generators (DGs). The objective functions including energy generation costs, electrical energy losses and the voltage deviation are considered in this paper. In the proposed MTLBO algorithm, teacher and learner phases are modified. The considered objective functions are energy generation costs, electrical energy losses and the voltage deviations. The proposed algorithm uses an external repository to save founded Pareto optimal solutions during the search process. Since the objective functions are not the same, a fuzzy clustering method is used to control the size of the repository. The proposed technique allows the decision maker to select one of the Pareto optimal solutions (by compromising) for different applications. The performance of the suggested algorithm on a 70-bus distribution network in comparison with other evolutionary methods such as genetic algorithm (GA), particle swarm optimization (PSO) and TLBO is extraordinary.

Multiobjective Optimal Reactive Power Dispatch and Voltage Control: A New Opposition-Based Self-Adaptive Modified Gravitational Search Algorithm

This paper presents a novel opposition-based self-adaptive modified gravitational search algorithm (OSAMGSA) for optimal reactive power dispatch and voltage control in power-system operation. The problem is formulated as a mixed integer, nonlinear optimization problem, which has both continuous and discrete control variables. In order to achieve the optimal value of loss, voltage deviation, and voltage stability index, it is necessary to find the optimal value of control variables such as the tap positions of tap changing transformers, generator voltages, and compensation capacitor. Therefore, this complicated problem needs to be solved by an accurate optimization algorithm. This paper solves the aforementioned problem by using the gravitational search algorithm (GSA), which is one of the novel optimization algorithms based on the gravity law and mass interactions. To improve the efficiency of this algorithm, the tuning of its parameters is accomplished using random generation, and by applying the self-adaptive parameter tuning scheme. Also, the proposed OSAMGSA of this paper employs the opposition-based population initialization and self-adaptive probabilistic learning approach for generation jumping and escaping from local optima. Since the proposed problem is a multiobjective optimization problem incorporating several solutions instead of one, we applied the Pareto optimal solution method in order to find all Pareto optimal solutions. Moreover, the fuzzy decision method is used for obtaining the best compromise solution between them.

Design of sliding mode controller for UPFC to improve power oscillation damping

In this paper, a new indirect method of sliding mode control (SMC) for series converter of unified power flow controller (UPFC) is proposed. In this method, the dynamic model of controller is obtained through special concepts of state variable transform, feedback linearization and differentially flat output. This new and simple control strategy has been developed base on the modified SMC principles which utilizes only the active and reactive output powers as the control inputs. Since these measurable control inputs are known as differentially flat outputs, they have a direct influence on the state variables of series converter to control them with the capability of power oscillation damping. The absence of discontinuous component input to UPFC controller avoids the chattering effect on state variables, especially on the dc capacitor voltage of inverters. For the validation of the proposed new controller, the obtained results were compared with results obtained from the PI controller.

Modified shuffled frog leaping algorithm for multi-objective optimal power flow with FACTS devices

This paper presents a novel approach to depict Flexible AC Transmission Systems FACTS devices effects in power system using multi-objective optimization function. The FACTS devices can play very important roles in power system such as improve power system security, reduce generation cost, decrease transmission loss and improve the voltage stability index. Two more common FACTS devices are the Thyristor Controlled Series Capacitor TCSC and Static VAR Compensator SVC which can smoothly and rapidly change their apparent reactance and injection power respectively according to the system requirements. Determining the FACTS devices parameters in power system is too complicate and has a lot of local optima in its search space. In order to overcome above problems a new method, based on SFLA algorithm combined with a new mutation is proposed to increase the efficiency of the SFLA algorithm. Since the proposed problem is a multi-objective problem it is usual to obtain a set of solution instead one solution therefore Pareto method that uses concept of non-dominate solutions is applied to find best compromise solutions. An external repository is considered for saving all non-dominated solution, and also they are sorted by fuzzy set rule to obtain best solutions. For more validation the simulation results are compared with those in other literatures.

Application of Modified Shuffled Frog Leaping Algorithm on Optimal Power Flow Incorporating Unified Power Flow Controller

(UPFC). In a power system, installing the UPFC can improve power transfer capability, transient stability, and system reliability, reduce loss in the transmission network and the fuel cost of generators. In order to apply UPFC in OPF problem, a mathematical model needs to be set for it. In this paper a new model based on the Injection Power Model (IPM) is presented. Due to the nonlinearity of OPF problem, it is essential to use an exact and strong method to solve it. In recent years, evolutionary and heuristic advantages of algorithms in terms of the modeling capability and search power lead to their higher application in the complicate problem like OPF. This paper presents a modified shuffle frog-leaping algorithm (MSLFA) to solve the OPF problem. The MSFLA has a flexible and well balanced mechanism in order to enhance and adapt to global and local exploration abilities. Simulation results on the modified IEEE 30-bus and 5-bus test systems indicate that the proposed MSLFA algorithm approach can obtain better solutions than other optimization algorithms.

Design of Adaptive-Sliding Mode Controller for Positioning Control of Underwater Robotics

This paper presents a new robust controller for positioning control of underwater robotics. One of the most important underwater robotics that is used in navy and marine industries is underwater remotely operated vehicle (ROV). Control of ROV is not easy task, because of its dynamic. Various controllers have been proposed, such as sliding control, nonlinear control, adaptive control, and etc. Some nonlinear control schemes require an accurate system model of the ROV system. And some of control schemes require bound of parametric uncertainties. This paper is presented an adaptive-sliding mode controller that is focused on self-tuning when the control performance degrades during the operation due to model uncertainties and change in the vehicle system and its environment. The adaptive-sliding mode controller combined advantages of sliding mode controllers and adaptive estimators. In the adaptive-sliding mode controller, robust controller is applied for tracking and rejecting disturbances and adaptive estimator is applied for estimating uncertain parameters. This controller is compared with other control methods such as sliding mode controller. To show the validity of the proposed algorithm, computer simulation results are presented