Hsan Hadj Abdallah

Reduced order observer for permanent magnet synchronous generator in wind energy conversion system

This paper presents an approach to develop a simple reduced speed observer for permanent magnet synchronous generator (PMSG) in wind energy conversion system (WECS). The speed and rotor position estimation of the PMSG are obtained by only measuring phase voltage and current. Maximum wind energy extraction is achieved by running the wind turbine generator in variable-speed mode. The rotor speed is allowed to vary in sympathy with the wind speed by maintaining the tip speed ratio to the value that maximizes aerodynamic efficiency. Advantages of this mechanical sensorless control strategy for maximum power estimation are demonstrated by digital simulation of the WECS.

Optimal placement of Phasor Measurement Units by genetic algorithm

Monitoring and supervision of power systems are provided by the control center, whose role is the design, coordination and network management. This paper presents a control technique based on the implantation of measurement units at the network buses. This technique should meet two requirements: ensure the complete system observability and find the optimal locations of PMUs with the minimum cost. The problem was formulated as a mono-objective optimization problem and its resolution was made by implementing a genetic algorithm. The proposed method is tested on three test networks and the results are compared with other resolution techniques. The simulation results ensure the complete system observability and validate the technique presented.

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).

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 ).

Application of multi-objective PSO algorithm for Economic Dispatch (ED) through Unit Commitment Problems (UCP)

This paper presents a new approach via MOPSO algorithm to solve optimal Economic Dispatch (ED) via Unit Commitment Problems (UCP). The unit commitment problem can be defined as the scheduling of production of electric power generating units over a daily to weekly time horizon in order to accomplish some objective. The problem solution must respect both generator constraints (such as ramp rate limits and minimum up or down times) and system constraints (reserve and energy requirements and, potentially, transmission constraints). The first function is the production cost. While the second one is the transition cost. A comparative study is conducted to examine the impact of reliability constraint on the optimal solution obtained. The ten machine thirty nine-bus system is used and the results show the effectiveness of MOPSO and confirm its potential to solve the unit commitment problem.

Modeling of a three-phase underground power cable using the distributed parameters approach

This paper presents a model of the underground shielded cable by using voltage and current electrical measurements available in voltage source. The cable modeling is based on the distributed parameters theory which takes into account both core and sheath. This model is applied to the real case of 150 kV cable posed in trefoil configuration, connecting substations of TYNA, TAPAROURA and SIDIMANSOUR, three urban zones of SFAX city, TUNISIA. In order to validate the developed model, load and unload tests are applied using the software Simulink-SimPowerSystems of Matlab.