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

New designs systems for induction cooking devices for heating performances improving

In order to give a temperature distribution at the bottom of the induction cooking, and moderate reduction the temperature outside the useless areas of these systems. This paper is dedicated to the study of the induction heating systems, which involves coupled electromagnetic and thermal phenomena and where new topologies are proposed. The modelling of the problem is based on the Maxwell's equations and the heat diffusion equation. We present a numerical simulation method based on parameterization of thermal electromagnetic coupling phenomena taking into account the changing of the physical characteristics of the body during the induction heating process. The purpose of this new optimum perforation topology is based on improving the thermal performances of the system, which allows improved dissipation by heat exchange. The results are obtained from a two-dimensional calculation code developed and implemented on Matlab software where CVM the finite volume method was adopted as a method of solving partial differential equations with partial derivatives characteristics of physical phenomena

Fuzzy Controlled Parallel PSO to Solving Large Practical Economic Dispatch

International audienceThis paper proposes a version of fuzzy controlled parallel particle swarm optimization approach based decomposed network (FCP-PSO) to solve large nonconvex economic dispatch problems. The proposed approach combines practical experience extracted from global database formulated in fuzzy rules to adjust dynamically the three parameters associated to PSO mechanism search. The adaptive PSO executed in parallel based in decomposed network procedure as a local search to explore the search space very effectively. The robustness of the proposed modified PSO tested on 40 generating units with prohibited zones and compared with recent hybrid global optimization methods. The results show that the proposed approach can converge to the near solution and obtain a competitive solution with a reasonable time compared with recent previous approaches

Comparative Analysis of Estimation Techniques of SFOC Induction Motor for Electric Vehicles

International audienceThis paper presents system analysis, modeling and simulation of an electric vehicle with different sensorless control techniques. Indeed, sensorless control is considered to be a lower cost alternative than the position or speed encoder-based control of induction motors for an electric vehicle. Two popular sensorless control methods, namely, the Luenberger observer and the Kalman filter methods are compared regarding speed and torque control characteristics. They are also compared against the well-known model reference adaptive system. Simulations on a test vehicle propelled by 37-kW induction motor lead to very interesting comparison results

SDTC Neural Network Traction Control of an Electric Vehicle without Differential Gears

International audienceThis paper proposes a Sensorless Direct Torque Control (SDTC) neural network traction control approach of an Electric vehicle (EV) without differential gears (electrical differential system). The EV is in this case propelled by two induction motor (one for each wheel). Indeed, using two electric in-wheel motors give the possibility to have a torque and speed control in each wheel. This control level improves the EV stability and the safety. The proposed traction control system uses the vehicle speed that is different from wheels speed characterized by slip in the driving mode, as an input. In terms of the analysis and the simulations carried out, the conclusion can be drawn that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed SDTC neural network approach operates satisfactorily

Modeling, Analysis, and Neural Network Control of an EV Electrical Differential

International audienceThis paper presents system modeling, analysis, and simulation of an electric vehicle (EV) with two independent rear wheel drives. The traction control system is designed to guarantee the EV dynamics and stability when there are no differential gears. Using two in-wheel electricmotorsmakes it possible to have torque and speed control in each wheel. This control level improves EV stability and safety. The proposed traction control system uses the vehicle speed, which is different from wheel speed characterized by a slip in the driving mode, as an input. In this case, a generalized neural network algorithm is proposed to estimate the vehicle speed. The analysis and simulations lead to the conclusion that the proposed system is feasible. Simulation results on a test vehicle propelled by two 37-kW induction motors showed that the proposed control approach operates satisfactorily