Kamel Srairi

A Loss-Minimization DTC Scheme for EV Induction Motors

This paper proposes a strategy to minimize the losses of an induction motor propelling an electric vehicle (EV). The proposed control strategy, which is based on a direct flux and torque control scheme, utilizes the stator flux as a control variable, and the flux level is selected in accordance with the torque demand of the EV to achieve the efficiency-optimized drive performance. Moreover, among EVs motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach

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

This 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 electric motors makes 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.

A loss-minimization DTC scheme for EV induction motors

This paper proposes a strategy to minimize the losses of an induction motor propelling and electric vehicle (EV). The proposed control strategy, based on a direct flux and torque control (DTC) scheme, utilizes the stator flux as control variable and the flux level is selected in accordance with torque demand of the EV to achieve the efficiency optimized drive performance. Moreover, among EVs motor electric propulsion features; the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics is taken into-account. Simulations tests have been carried out on a 1.1-kW EV induction motor drive to evaluate the consistency and the performance of the proposed control approach.

Optimal power flow of the algerian network using genetic algorithm/fuzzy rules

This paper presents a combined genetic algorithm and fuzzy logic rules to enhance the optimal power flow (OPF) with consideration of multi shunt flexible AC transmission systems (FACTS). The OPF problem is formulated as a nonlinear constrained objective optimization problem with both equality and inequality constraints. The problem is decomposed into, the optimal power generation subproblem that is searched by a flexible genetic algorithm which crossover and mutation adjusted by fuzzy expert rules and a simple practical reasoning fuzzy rules designed as a second subproblem to control the reactive power exchanged with the network. The proposed method guarantees the near optimal solution and remarkably reduces the computation time. This proposed approach is implemented with Matlab program and applied to the medium 59 bus of the Algerian network. The optimization results are compared to the solution given by the standard GA and ant colony method (ACO). This comparison confirms the efficiency of the proposed approach which makes it promising to solve the OPF with consideration of FACTS devices.

OPF with envirenmental constraints with SVC controller using decomposed parallel GA: Application to the Algerian network

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria up to 90% of the electricity demand produced by thermal generators (vapor, gas), in order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi objective environmental/economic dispatch problem. 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: the first sub problem related to the active power planning to minimize the total fuel cost, and the second sub problem is a reactive power planning 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 algorithm proposed tested on the Algerian 59-bus network test and compared with conventional method and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time.

Differential evolution based dynamic decomposed strategy for solution of large practical economic dispatch

This paper presents an Improved Parallel Differential Evolution (IPDE) optimization algorithm based dynamic decomposed strategy to solving large economic dispatch (ED) with consideration of practical generators constraints. The migration operation inspired from Biogeography-based Optimization algorithm (BBO) is newly introduced in the parallel DE approach, thereby can effectively explore and exploit promising regions in a search space by creating dynamically new efficient partitions. This new mechanism based migration between individuals from different subsystems makes to react more by exchanging experiences. The algorithm is applied to large electrical network test system, 40 thermal units with non-smooth cost function. The simulation results compared with the other recent techniques. From the different case studies, it is observed that the proposed approach improves the performances of the standard Differential Evolution algorithm (DE) and gives results with qualitative solution and less computational time.

OPF with Environmental Constraints with Multi Shunt Dynamic Controllers using Decomposed Parallel GA: Application to the Algerian Network

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria, up to 90% of electricity is produced by thermal generators (vapor, gas). In order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods have been proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed Parallel GA to solve the multi-objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two subproblems are proposed: the first subproblem is related to the active power planning to minimize the total fuel cost, and the second subproblem is a reactive power planning design 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 algorithm proposed was tested on the Algerian 59-bus network test and compared with conventional methods and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near solution and obtain a competitive solution at a critical situation and within a reasonable time.

Implementation of adaptive fuzzy logic and PI controllers to regulate the DC bus voltage of shunt active power filter

The output gain of the adaptive fuzzy controller (AFC), it is considerate as a fuzzy variable.This gain is adapted as a function of the voltage error and its variation.The comparative at PI controller study, shows that the adaptive fuzzy controller can be enhance the performances the DC voltage control at various operating.Experimental results validate the design methodology. Active power filter (APF) performance is entirely dependent on capacitor voltage. DC voltage optimization is one of the key aspects in harmonics compensation. This paper presents an experimental comparative study of new adaptive fuzzy controller (AFC) and proportional integral (PI) regulator, applied to regulate the DC bus voltage of three phase shunt APF. The proposed AFC for APF, consist to adapt the output gain of this controller at every situation of the system as a function of the voltage error and its variation. The algorithm used to identify the reference currents is based on the Self Tuning Filter (STF). The firing pulses of the insulated-gate bipolar transistors (IGBTs) inverter are generated using a hysteresis current controller; which is implemented on an analog card. Finally, the above study, under steady state and transient conditions, is illustrated with signal-flow graphs and corresponding analysis. This study was verified by experimental tests on hardware prototype based on dSPACE-1104.

Optimal power flow for large-scale power system with shunt FACTS using fast parallel GA

The main disadvantage of GAs is the high CPU time execution and the quality of the solution deteriorate with practical large-scale optimal power flow (OPF) problems. This paper presents an efficient parallel GA (EPGA) combined with fuzzy practical rules for the solution of the large-scale OPF. The length of the original chromosome is reduced successively based on the decomposition level and adapted with the topology of the new partition. Partial decomposed active power demand added as a new variable and searched within the active power generation variables of the new decomposed chromosome. In the second step a simple fuzzy rules designed based in practical expertise rules to control the reactive power of a multi dynamic shunt FACTS Compensators (SVC) to improve the system performances. Numerical results on two test systems IEEE 30-bus and IEEE 118-bus are presented and compared with results of others competitive global approach. The results show that the approach proposed can converge to optimum solution faster than other methods, and obtains the solution with high accuracy.

Strategy based PSO for Dynamic Control of UPFC to Enhance Power System Security

Penetration and installation of a new dynamic technology known as Flexible AC Transmission Systems (FACTS) in a practical and dynamic network requires and force expert engineer to develop robust and flexible strategy for planning and control. Unified Power Flow Controller (UPFC) is one of the recent and effective FACTS devices designed for multi control operation to enhance the power system security. This paper presents a dynamic strategy based on Particle Swarm Optimization (PSO) for optimal parameters setting of UPFC to enhance the system loadability. Firstly, we perform a multi power flow analysis with load incrementation to construct a global database to determine the initial efficient bounds associated to active power and reactive power target vector. Secondly a PSO technique applied to search the new parameters setting of the UPFC within the initial new active power and reactive power target bounds. The proposed approach is implemented with Matlab program and verified with IEEE 30-Bus test network. The results show that the proposed approach can converge to the near optimum solution with accuracy, and confirm that flexible multi-control of this device coordinated with efficient location enhance the system security of power system by eliminating the overloaded lines and the bus voltage violation.