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Background: A large number of nature-based optimization methods have been proposed to use as efficient tools in scientific studies. Genetic Algorithm (GA), which operates based on human genetical evolution, has been an outstanding mostly used solver in a wide range of applications. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Initialization, selection, crossover, and mutation are the main parts of the GA population-based method which enables GA to have a prominent explorative feature. On the other hand, the Teaching Learning Based Optimization algorithm (TLBO) is of great performance during searching for the optimum solution among individuals. Therefore, it is expected that the combination of both algorithms in a certain logical way improves the optimization time. Objective: The study intends to determine ways of improving performance of the TLBO algorithm to solve a complex non-linear problem. Power system studies are one of the most complex problems for analysis. Therefore, a powerful heuristic optimization procedure would have a valuable contribution in solving such problems. In addition, the proposed heuristic algorithm will help scientists to apply the technique to their problems. Materials and Methods: According to the aforementioned explanation, a new efficient optimization approach is proposed which optimizes the parameters of multi-machine power system stabilizers (PSSs). The TLBO algorithm includes two different stages in its main structure, which are aptly called teacher and student stages. The student stage of TLBO is replaced by the genetic algorithm in order to improve the explorative feature of the main TLBO. The PSS parameters are obtained for four PSSs which are connected to four generators. Results: The performance of the proposed stabilizer is compared with other formerly designed stabilizers reported in the literature consisting of multi-band PSSs for two areas four-machine power system. Simulation results demonstrate the effectiveness and robustness of the proposed PSS in damping local and inter-area oscillation modes under various disturbances and confirm its superiority in comparison with the other types of PSSs. Conclusion: : A search heuristic method like the genetic algorithm can dramatically improve the performance of meta-heuristic optimization technique. In actuality, the TLBO as a meta-heuristic optimization technique suffers from a direct search of random solutions in its primary stage. Then, the TLBO relinquishes some parts of search space which may restrict the algorithm to find absolute maximums or minimums. In this condition, the GA with a great ability in searching the whole search space effectively improves the TLBO. According to the obtained results, the proposed algorithm, named Genetic-TLBO, obviates the conventional TLBO flaws successfully.

This paper deals with the optimal design of V-shape interior permanent-magnet motor (V-IPM) for the electric vehicle application. The magnetic equivalent circuit (MEC) is used to obtain the performing parameters of the studied motor. The applied analytical model is verified using finite-element analysis (FEA). Considering the voltage and current limits, the maximum torque per ampere (MTPA) is applied to determine the optimum number of turns per slot to have a wider constant torque region. The consideration of the MTPA for the machine design increases the electric motor volume. The volume minimization is targeted in the objective function of the optimization problem. The optimization is executed over three different driving cycles for a 50-kW V-IPM motor. The efficiency and power factor are the constraints of the optimization problem. The optimal designs are compared against each other in terms of their efficiency and power density. The simulation results depict how the magnet and iron volume vary in different driving cycles.

This paper presents an improved analytical model for axial-flux induction motors (AFIMs) based on solving the magnetic vector potential equations. The advantage of the proposed approach is its fast simulation time (fractions of a second against minutes/hours in FEA) while maintaining over 95 % accuracy compared to FEA that makes it suitable for an optimization purpose. This method applies a uniform distribution of the current density in the rectangular shaped rotor/stator slots. The equivalent circuit parameters of the studied AFIMs are extracted using the magnetic energy analysis. The proposed model allows the saturation effect in calculation of the motor performance parameters (power factor and efficiency). The accuracy of analytical model is validated using FEA results. A comprehensive design optimization is presented to show its capability for an optimization purpose. The objective functions in this study are maximum efficiency, maximum power factor, maximum efficiency times power factor, minimum mass, minimum price, maximum efficiency over slip, and maximum power factor over slip. The proposed analytical model can be easily used for AFIMs with different number of poles and phases. Refereed/Peer-reviewed

Refereed/Peer-reviewed Subdomain technique is a computationally efficient approach with an acceptable accuracy for performance evaluation of the electric machines. This paper develops the subdomain model of three types of permanent magnet (PM) machines including surface-mounted PM (SPM), Inset PM, and consequent pole PM (CPPM) machines. The performance parameters of the studied machines are extracted via subdomain technique and validated by 2D finite-element analysis (FEA). The airgap flux density is calculated using the vector potentials obtained from the subdomain model. The flux density of the iron parts, that is required to find the core losses, are found using the magnetic vector potentials in the iron parts. The developed model is then employed for optimal design of various wind generators. The optimization is executed through two different scenarios. Firstly, the machines are optimized for maximization of the generator efficiency for a single operating point. In the second scenario, the optimization is done over annual wind regime of an area to maximize the annual energy efficiency. The airgap length, magnet dimensions, stack length, and the rotor and stator diameters are the variables of the optimization problem. The optimal designs are compared to show the advantageous and disadvantageous of the considered machines for the wind generator application.

Refereed/Peer-reviewed This paper presents the comparative study of two induction machines (IMs). It investigates the conversion of an existing three-phase IM to five-phase IMs and explores the effect of phase number on the performance parameters. The two studied machines in this work have the same number of winding turns per phase and similar rotor but modified stator.2D finite-element analysis and MATLAB are used to extract the performance parameters of the machine. The performance comparison of the machines consists of inductance, resistance, current, torque, power factor, and efficiency. It is found that the five-phase IM is more efficient than three-phase IM considering the same physical characteristics. This study aims to provide useful understanding on the effect of the number of phases on performance of IMs.

Robustness, low cost and high torque density are the main characteristics of switched reluctance machines (SRM) that have made them promising for a variety of applications. In this paper a small diameter, long stack length (small aspect ratio) 6kW SRM is designed. In the first step, the dimensions of the studied SRM are derived based on conventional sizing equations. The significance of the rotor and stator pole arcs and also the effective torque zone as a constraint of the design are discussed. A finite-element study is carried out to validate the analytical model of the proposed SRM. The performance of the machine is comprehensively studied in different operating speeds. Simulation results demonstrate the capability of the machine for operation over a wide speed range.pa Refereed/Peer-reviewed

This paper presents the design and analysis of a novel axial-flux synchronous reluctance motor (AFSynRM). A magnetic equivalent circuit (MEC) model for the proposed axial-flux machine is derived. The feasibility of fabrication is discussed. Finite-element analysis is applied to predict the performance of the proposed motor design. This paper provides a baseline study by comparing the performance of the proposed AFSynRM design with a similar rating radial-flux motor. The motors are designed to have similar volume and both have three rotor barriers. The operating parameters are obtained through static and dynamic FEA. The power, torque, power factor and efficiency of the motors are studied. The saturation and flux distribution in the motor structures are investigated. Refereed/Peer-reviewed

In this paper, the magnetic vector potential (MVP) equations are solved for a 5.5-kW radial-flux induction machine (RFIM) in five subdomains. The proposed modeling technique considers a uniform distribution of the current density in the rectangular slots of both rotor and stator. The RFIM is represented using a 2D model in the cylindrical coordinate system. The magnetic flux distribution in each region which is used to calculate the core losses and the machine inductances for the steady state analysis of the machine. T-equivalent circuit (T-EC) is considered to study the machine performance parameters. The rotor and stator resistances are calculated based on the geometry of the machine. The results demonstrate the accuracy of the model in prediction of the airgap flux density. The comparison of the machine performance parameters with the 2D FEA results demonstrate the reliability of the analytical model in the prediction of the machine behavior at the rated condition. Refereed/Peer-reviewed

The power system stability has always been one of the most significant and controversial debates among researchers and electric power engineers. The synchronous generators producing the most electrical energy in current power systems all around worlds must operate under safe conditions in terms of short circuit and open circuit faults occurrence to prevent the network instability. The Flexible Alternating Current Transmission System (FACTS) devices have been introduced to improve the power quality as well as loss reduction in the power systems. In this paper, a Unified Power Flow Controller (UPFC) is placed in the power system to improve the power quality. In addition, the Ant Colony Optimization (ACO) approach finds suitable proportional-integral coefficients of the Power System Stabilizers (PSSs) and UPFC controller parameters to coordinate them in order to improve power system stability. The simulation results demonstrate the robustness and effectiveness of the coordination and controller design method in damping inter-area oscillations.

Wind turbine systems need a suitable control system to achieve the best performance and efficiency during its operation, which is dependent on many unpredictable factors. Grid side and rotor side converters play a vital role in the system, hence they require suitable control algorithms and appropriate control parameters. In this paper control parameters is determined by smart line search enhanced by teaching-learning based optimization (SLS-TLBO) algorithm, which improves the reliability of the wind turbine system. To acquire the accurate input power control in a wide range of wind speed, drive train control system needs an appropriate pitch angle controller. In order to reach this goal fuzzy logic controller, which is well known for its capability in dealing with nonlinear systems is applied to pitch angle controller. A 30MW Doubly Fed Induction Generator (DFIG) wind turbine in connection with the IEEE 6 bus power system has been analyzed to validate the proposed control algorithms. The simulation result proves the accuracy of the controllers.

Syringe pumps have found various applications in the oil, gas, medical, and pharmaceutical industries. A high-precision pressure control approach is required in order to have accurate results in laboratories experiments. In conventional control systems, users need to regulate system controllers because of the various operating points. Adaptive control consists of several techniques which enable engineers to adjust control system parameters in real time automatically. In this paper, an appropriate adaptive controller is designed and implemented to provide meticulous operation of the syringe pump's motor in a wide range of operating points. The drive system of the permanent magnet synchronous motor is designed to control the fluid pressure through the syringe and needle. The experimental results demonstrate the accuracy and effectiveness of the proposed control strategy in comparison to the conventional controllers.

Superiority of Switched Reluctance Machine (SRM) in many aspects has been proven for years and recently because of its simple construction, low cost, ruggedness and high torque/mass ratio becomes one of the favorable machines in applications like hybrid vehicles and wind turbine generators. Despite all mentioned advantages because of its non-linear nature, control of Switched Reluctance Generator (SRG) challenged researchers and became a barrier in their way. In this paper a novel control strategy for Switched Reluctance Machine (SRM) in battery charging application based on Fuzzy Logic (FL) has been presented. Proposed control system do not contain any look-up tables and just by using FL controllers and conventional PI, desirable control has been achieved. In battery charging operations the quality of DC link current is very crucial and should has low ripple, in this case FL control loops provide smooth DC current with fast dynamic response. By combination of two FL controllers with a PI control loop, to some extent practicality of control method has been increased. Simulation results of a 4-pole, 4000W SRM which is coupled with prime mover and operate under battery charging mode verifies effectiveness of Recommended control strategy.

In this paper indirect rotor flux oriented control (IRFOC) approach has been implemented to torque control of induction machine (IM). The drive's control system has two proportional integral (PI) controllers, which have an essential role to control the transient and steady state conditions. So to make a desirable control, teaching learning based optimization (TLBO) method has been used to find the optimum coefficients. In order to enhance operation of the controller and achieve more accurate results the fuzzy logic control method is combined with TLBO. In fact on-line control will be achieved by applying fuzzy system to control system. In other word knowledge of expert human is used to locate fuzzy logic system in optimum location of control system. Also TLBO method by limiting boundaries of membership functions around optimal point lead system toward less complexity and practicability. Simulation results show the effectiveness of the proposed control approach in damping transient oscillations and reduction of steady state error.

In this paper a new effectual optimization approach is proposed which optimizes the power system stabilizers (PSSs) parameters in a multi-machine power system. The PSS parameters are established for four PSSs which are linked to four synchronous generators. There is an increased demand for development of such algorithm, so that made researchers look for algorithms not only being metaheuristic, but also inherit desired properties of so-called deterministic approaches. To accomplish this objective, a novel method called smart line search (SLS) has been proposed in this paper and has been combined with Teaching Learning based optimization (TLBO). Being so new-found, SLS tries to take benefits of gradient methods using weighted stochastic selection approach which not only leads to unraveling of new local optimums, but also with a performance rather more speedy than conventional algorithms so far used. The performance of the proposed approach is also compared with other method. By observing the simulation results in two-area four-machine power system and compare with each of old algorithm, have been seen that new method is positively effective on damping time and also power frequency and oscillation when the fault occurs.