Tawfik Guesmi

Application of a multiobjective evolutionary algorithm for optimal location and parameters of FACTS devices considering the real power loss in transmission lines and voltage deviation buses

In this paper, a multiobjective evolutionary algorithm (MOEA) to solve optimal reactive power (VAR) dispatch problem with flexible AC transmission system (FACTS) devices is presented. This nonlinear multiobjective problem (MOP) consists to minimize simultaneously real power loss in transmission lines and voltage deviation at load buses, by tuning parameters and location of FACTS. The constraints of this MOP are divided to equality constraints represented by load flow equations and inequality constraints such as, generation VAR sources and security limits at load buses. an unified power flow controller (UPFC) is considered. The design problem is tested on the IEEE 6-bus system.

Optimal design of multimachine power system stabilizers using evolutionary algorithms

In the past two decades, conventional power system stabilizer (PSS) is widely used to damp power systems oscillations. Nowadays, several approaches based on intelligent techniques have been applied to PSS design problem. This paper deals with optimal design of multimachine power system stabilizers to improve the dynamic stability of power system. The design problem is formulated as an optimization problem. Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed to search the optimal PSS parameters.. Based on a three-machine nine-bus test system, nonlinear simulation results under disturbance, show the effectiveness of the proposed approach in power oscillation damping.

Optimal location of multi type FACTS devices for multiple contingencies using genetic algorithms

With the electricity market deregulation, the number of unplanned power exchanges increases. If these exchanges are not controlled, some lines may become overloaded. Network contingencies often contribute to overloading of branches, violation of voltages and also leading to problem security. This paper presents a procedure based on the contingency severity index (CSI) described by a real power flow performance index (PI) to place multi type FACTS devices (Flexible AC Transmission System) in order to eliminate or alleviate the line over loads. TCSC(Thyristor Controlled Series Compensator) and UPFC(Unified Power Flow Controller) are considered and modeled for steady state analysis. Once the location is determined, their type, their optimal settings and cost of installation can be obtained by solving the optimization problem using genetic algorithms (GA). The proposed approach is tested on 9-bus test system.

Application of multi-objective PSO algorithm for static reactive power dispatch

This paper presents the application of multiple-objective particle swarm optimization (MOPSO) algorithm for solving the static reactive power dispatch (RPD) problem. The optimal RPD problem is a nonlinear multi-objective optimization problem which involves the simultaneous minimization of two objective functions. The first function is the total real power losses in transmission lines. While the second one is the voltage deviation at load buses. This optimization is done by considering functional equality and inequality constraints. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among the different trade-off solutions provided in the pareto-optimal front. The three-machine nine-bus system is used and the results show the effectiveness of MOPSO and confirm its potential to solve the multi-objective RPD problem.

Power flow solution for power systems including FACTS devices and wind farms

In this work, the power flow (PF) calculation method using Newton-Raphson algorithm including simultaneously wind farms and FACTS devices is studied. The power flow model for a stall regulated fixed speed wind generator (SR-FSWG) system is discussed to assess the steady-state condition of power systems with wind farms. A general steady-state modeling approach of power systems having SR-FSWG and the thyristor controlled series compensator (TCSC) is considered. To demonstrate the effectiveness of the proposed approach, the IEEE-5bus system is used. The TCSC is used to control the power flow of the line where it is installed.

0ptimal location and parameter setting of TCSC based on Sensitivity analysis

The FACTS devices, such as, thyristor controlled series compensators (TCSC) may be used to enhance system performance by controlling the power flows in the network. TCSC can be used to effectively control the load flow distribution and the power transfer capability, to reduce the active power losses and decrease the cost of power production. It is important to locate these devices optimally in the power system because of their considerable costs. Firstly, we perform a sensitivity analysis and ranking process to determine the optimal placement of TCSC. Thus, a method has been suggested in this paper based on real power flow performance index sensitivity and reduction of total system reactive power losses. Secondly, we have applied optimization techniques to find the best optimal location in order to reduce generation rescheduling cost. Evolutionary algorithms have been used to solve this nonlinear optimization problem, such as, the second version of non-dominated sorting genetic algorithm (NSGAII).

A genetic algorithm PSS and AVR controller for electrical power system stability

In this paper genetic algorithm (GA) is applied in order to design a PSS/AVR controllers with the goal of improving both transient stability and voltage regulation of power system under a symmetrical three-phase short circuit fault. A comparison based on simulation is then performed between the proposed approach and the classical controllers (manual control, AVR with no PSS, AVR with PSS). The simulation results with a Single Machine Infinite Bus (SMIB) demonstrate the effectiveness of the proposed approach.

Eigenvalue Assignments in Multimachine Power Systems using Multi-Objective PSO Algorithm

Applying multi-objective particle swarm optimization (MOPSO) algorithm to multi-objective design of multimachine power system stabilizers (PSSs) is presented in this paper. The proposed approach is based on MOPSO algorithm to search for optimal parameter settings of PSS for a wide range of operating conditions. Moreover, a fuzzy set theory is developed to extract the best compromise solution. The stabilizers are selected using MOPSO to shift the lightly damped and undamped electromechanical modes to a prescribed zone in the s-plane. The problem of tuning the stabilizer parameters is converted to an optimization problem with eigenvalue-based multi-objective function. The performance of the proposed approach is investigated for a three-machine nine-bus system under different operating conditions. The effectiveness of the proposed approach in damping the electromechanical modes and enhancing greatly the dynamic stability is confirmed through eigenvalue analysis, nonlinear simulation results and some performance indices over a wide range of loading conditions.

Robust design of multimachine power system stabilizers using multi-objective PSO algorithm

In this paper, robust design of multimachine power system stabilizers (PSSs) using multi-objective particle swarm optimization (MOPSO) is presented. The problem of selecting the stabilizer parameters is converted to an optimization problem with integral square error (ISE) and integral of time multiplied absolute value of the error (ITAE)-based objective functions. The MOPSO is employed to search for optimal PSS parameters for a wide range of operating conditions. The performance of the proposed MOPSO based PSSs is investigated for a three-machine nine-bus system under different configurations. The effectiveness of the proposed approach in enhancing the dynamic stability of power systems is confirmed through eigenvalue analysis and nonlinear simulation results.

A new method for the coordinated design of power system damping controllers

Abstract This paper proposes a new Teaching–Learning Algorithm ( TLA ) that uses the chaotic map to prevent the conventional TLA from getting stuck on local optima and enhancing the convergence characteristics, due to non-repetitions nature and ergodicity of chaotic functions. Some shortcomings are encountered in the original TLA , for instance, it can be trapped in local optima. This work tries to improve it by substituting the random in the initial algorithm with chaotic sequences. At this level, the initial population is chaotically generated and chaotic values are used in both phases. The global solutions are further enhanced by adding a new third chaotic phase. To demonstrate the effectiveness of the improved Teaching–Learning algorithm ( ITLA ), a fifteen of well-known benchmark functions are used. Experimental results demonstrate that ITLA outperforms significantly the conventional TLA , in terms of the accuracy of the final solution and the speed of convergence. The enhanced optimization algorithm is employed to solve the coordinated design problem of power system stabilizers ( PSS ) and thyristor-controlled series capacitor ( TCSC ), in order to investigate the feasibility and effectiveness of the proposed method in power systems. The performance of the proposed controllers is evaluated on a multi-machine power system under large disturbance and for different operating conditions through a nonlinear time-domain simulation. At the end, the results confirm the robustness of the proposed controllers in comparison to PSS designed by ITLA ( ITLAPSS ) and TCSC designed by ITLA ( ITLATCSC ).