OPTIMIZATION OF DESIGN PARAMETERS FOR A VARIABLE FREQUENCY 3-PHASE INDUCTION MOTOR

This report describes the method of optimizing a variable frequency 3-phase Induction Motor. The objective of this project is to come up with a final design of a 3-phase induction motor that is optimum in weight and cost and maximum in efficiency (weight and cost) without violating the constraints provided. The design of induction motor chosen for this purpose is a squirrel cage design motor as it is more robust and is widely used in the industry. C-language is used to describe the design of the machine. The machine design mainly involves non linear equations such as the magnetizing current flows and motor losses . The objective function achieved from the design are cost, efficiency and weight. The optimization technique used to optimize this objective functions is Genetic Algorithm (GA) which is a Non -Linear Programming technique. Twelve (12) design variables are identified and used in the design process. The objective of this project is to come up with the optimized variable which produces the highest motor efficiency, minimum weight and cost. The results of the optimized values will be compared with the values obtained before optimizing. The comparison is shown in scatter line graph. The final design obtained is the improved the improved version of the design parameters compared with the one used as the input to the optimizing program

Model simplification and optimization of a passive wind turbine generator

In this paper, the design of a "low cost full passive structure" of wind turbine system without active electronic part (power and control) is investigated. The efficiency of such device can be obtained only if the design parameters are mutually adapted through an optimization design approach. For this purpose, sizing and simulating models are developed to characterize the behavior and the efficiency of the wind turbine system. A model simplification approach is presented, allowing the reduction of computational times and the investigation of multiple Pareto-optimal solutions with a multiobjective genetic algorithm. Results show that the optimized wind turbine configurations are capable of matching very closely the behavior of active wind turbine systems which operate at optimal wind powers by using a MPPT control device

Urban and extra-urban hybrid vehicles: a technological review

Pollution derived from transportation systems is a worldwide, timelier issue than ever. The abatement actions of harmful substances in the air are on the agenda and they are necessary today to safeguard our welfare and that of the planet. Environmental pollution in large cities is approximately 20% due to the transportation system. In addition, private traffic contributes greatly to city pollution. Further, “vehicle operating life” is most often exceeded and vehicle emissions do not comply with European antipollution standards. It becomes mandatory to find a solution that respects the environment and, realize an appropriate transportation service to the customers. New technologies related to hybrid –electric engines are making great strides in reducing emissions, and the funds allocated by public authorities should be addressed. In addition, the use (implementation) of new technologies is also convenient from an economic point of view. In fact, by implementing the use of hybrid vehicles, fuel consumption can be reduced. The different hybrid configurations presented refer to such a series architecture, developed by the researchers and Research and Development groups. Regarding energy flows, different strategy logic or vehicle management units have been illustrated. Various configurations and vehicles were studied by simulating different driving cycles, both European approval and homologation and customer ones (typically municipal and university). The simulations have provided guidance on the optimal proposed configuration and information on the component to be used

Computationally Efficient Optimization of a Five-Phase Flux-Switching PM Machine Under Different Operating Conditions

This paper investigates the comparative design optimizations of a five-phase outer-rotor flux-switching permanent magnet (FSPM) machine for in-wheel traction applications. To improve the comprehensive performance of the motor, two kinds of large-scale design optimizations under different operating conditions are performed and compared, including the traditional optimization performed at the rated operating point and the optimization targeting the whole driving cycles. Three driving cycles are taken into account, namely, the urban dynamometer driving schedule (UDDS), the highway fuel economy driving schedule (HWFET), and the combined UDDS/HWFET, representing the city, highway, and combined city/highway driving, respectively. Meanwhile, the computationally efficient finite-element analysis (CE-FEA) method, the cyclic representative operating points extraction technique, as well as the response surface methodology (in order to minimize the number of experiments when establishing the inverse machine model), are presented to reduce the computational effort and cost. From the results and discussion, it will be found that the optimization results against different operating conditions exhibit distinct characteristics in terms of geometry, efficiency, and energy loss distributions. For the traditional optimization performed at the rated operating point, the optimal design tends to reduce copper losses but suffer from high core losses; for UDDS, the optimal design tends to minimize both copper losses and PM eddy-current losses in the low-speed region; for HWFET, the optimal design tends to minimize core losses in the high-speed region; for the combined UDDS/HWFET, the optimal design tends to balance/compromise the loss components in both the low-speed and high-speed regions. Furthermore, the advantages of the adopted optimization methodologies versus the traditional procedure are highlighted

Losses Optimization of Induction Motor Using Genetic Algorithm

In this work, Genetic Algorithm (GA) has been used as an optimization technique to minimise the losses in an induction machine. It was been observed that the GAs locate the global optimum region faster than the conventional direct search optimization techniques. In this paper, the concept of GA was used as an optimization technique to minimize the losses in an electric machine thereby improving on the efficiency. The result shows an improvement in the machine’s efficiency from 90.3% to 94.3%

Design Optimization of Electric Motor (Induction Motor) Using Genetic Algorithm

Various three phase Induction Motors are extensively used in domestic, commercial and industrial applications. One such induction motor is the Squirrel Cage type which is characterized by its simplicity, robustness and low cost. Hence, squirrel cage motors are extensively used in the industrial sector. However these motors consume large quantities of power. The reduction in electric energy consumption in squirrel cage motors through a better motor design is an attractive option. Optimization of the electromagnetic devices requires consideration of discrete and continuous variables and discontinuities in the search space

Integrated Optimal Design of a Passive Wind Turbine System: An Experimental Validation

This work presents design and experimentation of a full passive wind turbine system without active electronic part(power and control). The efficiency of such device can be obtained only if the system design parameters are mutually adapted through an Integrated Optimal Design (IOD) method. This approach based on multiobjective optimization, aims at concurrently optimizing the wind power extraction and the global system losses for a given wind speed profile while reducing the weight of the wind turbine generator. It allows us to obtain the main characteristics (geometric and energetic features) of the optimal Permanent Magnet Synchronous Generator (PMSG) for the passive wind turbine. Finally, experiments on the PMSG prototype built from this work show a good agreement with theoretical predictions. This validates the design approach and confirms the effectiveness of such passive device

African vulture optimizer algorithm based vector control induction motor drive system

This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response

Comparison and Design Optimization of a Five-Phase Flux-Switching PM Machine for In-Wheel Traction Applications

A comparative study of five-phase outer-rotor flux-switching permanent magnet (FSPM) machines with different topologies for in-wheel traction applications is presented in this paper. Those topologies include double-layer winding, single-layer winding, C-core, and E-core configurations. The electromagnetic performance in the low-speed region, the flux-weakening capability in the high-speed region, and the fault-tolerance capability are all investigated in detail. The results indicate that the E-core FSPM machine has performance advantages. Furthermore, two kinds of E-core FSPM machines with different stator and rotor pole combinations are optimized, respectively. In order to reduce the computational burden during the large-scale optimization process, a mathematical technique is developed based on the concept of computationally efficient finite-element analysis. While a differential evolution algorithm serves as a global search engine to target optimized designs. Subsequently, multiobjective tradeoffs are presented based on a Pareto-set for 20 000 candidate designs. Finally, an optimal design is prototyped, and some experimental results are given to confirm the validity of the simulation results in this paper