NEURO-GENETIC OPTIMIZATION OF MAGNETIC HYSTERESIS INTEGRATES IN ELECTROMAGNETIC SYSTEMS

International audienceIn this work we have presented an approach for calculating the hysteresis loop of Jiles-Atherton model using the magnetic inductance as the independent variable is proposed to be used directly in the calculation time step finite volume applied to the numerical analysis of nonlinear magnetic fields. This model is characterized by five parameters that must be identified and optimized for better representation of the measured characteristics. The parameters set of the Jiles–Atherton hysteresis model identified by using a real coded genetic algorithm. The parameters identification performed by minimizing the mean squared error between experimental and simulated magnetic field curves. The method verified by applying it to an axi-symmetrical ferromagnetic system. The calculated results validated by experiences performed in a Single Sheet Tester's frame (SST). In this work, we are interested to develop a model based on feed-forward neural networks of which can describe magnetic hysteresis by taking account the influence of some external sizes

Experimental study of temperature effects on the photovoltaic solar panels performances in Algerian desert

Photovoltaic panels are operated in the Algerian desert areas under high temperatures, especially, in the summer, when the temperature may be reached 70°C on the panel's surface. The high temperature has a significant negative impact on the photovoltaic panels performance. In this paper, an experimental study to track the effects of temperature on the photovoltaic panels performances in different situations has been realized. The obtained results approve the importance of the temperature effects on the electrical power of the photovoltaic panel. The temperature increases lead to decreases in the performance of the panel, where an output power that does not exceed 52% of the nominal power at a high temperature

Optimal coordination of directional overcurrent relays using PSO-TVAC considering series compensation

The integration of system compensation such as Series Compensator (SC) into the transmission line makes the coordination of directional overcurrent in a practical power system important and complex. This article presents an efficient variant of Particle Swarm Optimization (PSO) algorithm based on Time-Varying Acceleration Coefficients (PSO-TVAC) for optimal coordination of directional overcurrent relays (DOCRs) considering the integration of series compensation. Simulation results are compared to other methods to confirm the efficiency of the proposed variant PSO in solving the optimal coordination of directional overcurrent relay in the presence of series compensation

Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow

International audienceUnder critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the optimal power flow (OPF) with non-smooth cost function and under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning designed based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the proposed algorithm tested on IEEE 30-Bus, 26- Bus and IEEE 118-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, PSO, MTS, MDE and ACO) and with two robust simulation packages: PSAT and MATPOWER. The results show that the proposed approach can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time