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Refereed/Peer-reviewed In this article, the analysis of induction machines using five separated subdomains including the rotor slot, rotor slot opening, airgap, stator slot opening, and stator slot regions is considered. The Maxwell equations of each region are solved considering the boundary conditions. Uniform distributions of the current density in the rectangular slots of both the rotor and stator are considered to find the energy of the winding and to calculate the machine inductances using the energy equation. The calculated flux density of each subdomain is used to find the flux distribution. The calculated magnetic flux intensities are required to estimate the saturation level of the motor cores and correct the saturation-related values such as the leakage and magnetizing inductances. The machine core losses are calculated based on the Steinmetz equation at the no-load condition. The calculated inductances and resistances are used in the equivalent circuit of the squirrel-cage induction machine (SCIM) for the performance prediction. The results demonstrate the reliability of the saturation model in the prediction of the saturation influence in a wide range of magnetization levels. The accuracy of the proposed model in the prediction of the performance of the SCIM is validated using two-dimensional (2-D) and 3-D finite-element analysis results. A sensitivity analysis with 75 different geometries was conducted to show the capability of the model for the performance prediction of a wide range of induction machines.

Efficiency maps (EffMs) illustrate the maximum efficiencies of electric machines over a range of torque-speed operating points. This paper reviews the available techniques for efficiency map calculation and gives a general understanding of EffM modelling and interpretation for traction motors. It shows the accuracy versus calculation effort trade-off for different methods. The process of efficiency map calculation for sample induction motor and permanent magnet synchronous machines is explained. Calculation of EffMs using commercial software is also covered. Finally, possible future research opportunities in this field are suggested. Refereed/Peer-reviewed

The bond-graph (BG) enables to model multi-physics systems in the energy domains. This paper shows that BG models are an alternative, more intuitive and simpler to interpret, graphical tool as compared to existing dynamic models for performance prediction of the line-start permanent-magnet synchronous motors (LSPMSMs). It includes electrical, magnetic, and mechanical energy domains where the stator windings, rotor bars, and permanent magnets are the main components of the model. The dynamical equations of the proposed BG are extracted and solved using the MATLAB ordinary differential equation solver for a sample LSPMSM. The dynamics of the studied LSPMSM is investigated under two operating conditions (1) a healthy motor and (2) a motor with broken rotor bars. The simulation results are compared with the finite-element analysis (FEA) as the most accurate method to simulate the electric motor behaviour. It is shown that the proposed BG model accurately predicts the performance parameters of the motor such as current, torque, and speed under various operating conditions. In addition to the modelling validation, the transient behavior of the LSPMSM are discussed to describe the possible reasons for its behaviour during transient and steady-state operating conditions.

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.

Background: Environmental health has become a worldwide debating issue among researchers, scientists, and governments. Although fossil fuels have been the greatest energy resources for the human, their consumption leads to injecting greenhouse gases into the air, which can affect the existence of all living species dramatically. In fact, fossil fuels consumption pollutes the environment because of the injection of greenhouse gases which result in global warming. On the other hand, reductions in fossil fuel and drinking water resources have highlighted the importance of energy management and loss reduction in brand-new management strategies and manufacturing methods. Distribution transformers are one of the most used devices in power distribution networks. Hence, it seems to be logical to consider transformer losses as a definitive factor in design and construction procedures. Furthermore, such design procedures require powerful tools to solve complex non-linear equations and find the best solutions in the shortest time. Researchers and scientists have always had a great challenge regarding finding the best solutions for their analysis. In actuality, it is hard to solve complex non-linear problems by means of traditional calculation methods even if a researcher employs a powerful computer. For this reason, metaheuristic optimization algorithms have found great popularity among scientists. These algorithms seek the best solution through a solution pool at an appropriate pace. Therefore, a researcher can save more time and energy in solving an intricate problem. Objective: In this paper, the authors aim to modify the conventional Teaching-Learning Based Optimization (TLBO) algorithm to design an optimum distribution transformer by consideration of the transformer Total Owning Cost (TOC). The TOC consists of operational and initial manufacturing costs of a distribution transformer. The used raw materials affect the manufacturing cost and it would be decreased if the copper and iron volumes are reduced in the construction instruction. However, the operational cost that includes iron and copper losses must not be forgotten in design analysis. Indeed, loss consideration is of great significance to keep energy from frittering away and as a result, protects environmental resources. Materials and Methods: A novel approach based on an optimization technique for distribution transformer design problems is presented in this paper. The entire expenses of a transformer consist of transformer construction and operating costs. In other words, by reducing copper and iron volumes, the initial cost of a transformer will be decreased. Due to the electromagnetic and electrical losses in transformers, the initial cost of a transformer is not its entire design problem and the operating cost must be considered in the design algorithm. Appropriate limits on efficiency, voltage regulation, temperature rise, no-load current and winding fill factor are the constraints on the transformer design problems in the international standards. With respect to these constraints, the transformer designer can minimize the volume of the core and windings. In this paper, the Conditional Teaching-Learning Based Optimization (CTLBO) algorithm determines the appropriate transformer properties, while transformer construction cost and its operating cost are selected as an objective function for optimization method. In order to attain a suitable voltage regulation, transformer impedance is chosen as an optimization constraint. Results: The result of the paper demonstrates the proposed algorithm ability in reducing the Total Owning Cost (TOC) during transformer lifetime, which could be useful for energy distribution companies. In addition, the result analysis proves that the total losses of the transformer are reduced by the proposed approach in comparison to conventional design techniques. Then, more energy will be saved in the power grid when the proposed transformer is utilized in the power network. Conclusion: In this paper, a suitable method to design an optimum distribution transformer is proposed which enables manufacturers to construct their product based on the proposed method. In this way, it can be claimed that not only more money will be saved during the transformer operation, but also the energy consumption will be decreased drastically. Therefore, world resources will remain for future generations.

The power system operation, stability, and reliability are strongly dependent on the network protection system performance. On the other hand, the penetration of the renewable energy power plants into power grids drastically affects power networks and smart grids’ stability and reliability because of their fast dynamics in comparison to conventional power generators. Hence, power networks are considered as a complex cyber–physical energy system with vast interactions and communications among consumers and generators. In this condition, a reliable monitoring and control system can augment the safety, security, and operation of such an intricate system. Suitable time coordination between the operations of power grids protection relays is one of the most challenging issues to protect the power system properly. In this paper, a reliable supervisor system (RSS) with fast and precise control ability is proposed to improve power systems and smart grids protection system operations. The device utilizes a safe robust communication way which provides a condition for the available relays on two sides of a line to share their data. For this reason, the proposed device is able to diagnose the fault on a short transmission line quickly upon the event occurrence. In other words, fast communication between two sides of a short transmission line helps the protection system to work faster in the presence of a fault. In addition, quick handshaking and data sharing of the network different points provided by the RSS enable grid circuit breakers to disconnect a fault from the system as fast as possible. The proposed system, then, can easily find a defect location and disconnect a faulty line from the grid. According to the experimental results provided in this paper, the RSS can effectively corroborate the network stability.

Commercialization of electric vehicles (EVs) and light electric vehicles (LEVs) play a major role in reduction of greenhouse gases and environmental pollution. In this paper, the main reasons for popularity of the LEVs are explained based on the statistics available in literature. The propulsion system of the LEVs (including the electric motor, transmission system, battery package, and inverter) aims to offer high efficiency over a wide range of torque-speed operating points. Hence, the optimal design and performance improvement of such vehicles have attracted attention in literature. The investigated drivetrain systems in various LEVs are reviewed in this paper to highlight their advantages and disadvantages. The popular electric motors used in the LEVs are discussed and their characteristics are emphasized. The available research gaps in this field are highlighted at the end of the paper to provide a guide for the new researchers in this field.

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

Selection of electrical machine is a key issue in electrical and hybrid electrical vehicles (HEVs and EVs). As far as HEVs and EVs are concerned, mechanical ruggedness of electrical machine is of utmost importance. For these reasons, the application of switched reluctance (SR) machine in EVs and HEVs is a viable option. In this study, an SR generator (SRG) has been utilised to work as a battery charger in EVs. The current ripple of the SRG is the greatest issue when it works as a battery charger. Therefore, a power converter and a smart search control (SSC) approach are proposed in this study to decrease the current ripple when the output power has a maximum quantity. During the system operation, the SSC method determines the value of excitation angle, turn-on and turn-off angles for each phase of SRG. The simulation and experimental results demonstrate the eligibility of the SSC system and power converter to obtain the acceptable charging current in maximum generated power during battery charging.

The ability of artificial intelligence and machine learning techniques in classification and detection of the types of data in large datasets lead to their popularity among scientists and researchers. Because of the presence of different load at different times in power systems, it is hard to provide an accurate mathematical model for such systems. On the other hand, most of the available protection devices in power grids work based on the estimated mathematical models of the grid. For this reason, power system utilizers usually suffer from the low accuracy of the available protection systems in fault detection and diagnosis. In this paper, a reliable machine learning technique is proposed to detect and classify different faults of smart grids. The proposed technique benefits from the principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA is used to reduce the size of the dataset matrixes. The applied PCA reduces the dataset sizes and eliminates the possible singularity of the datasets. The LDA method is applied to the outputs data of the PCA to minimize the with-in class distance of the dataset and maximize the distance between classes. Finally, the well-known K-nearest neighbor technique is applied to detect the fault and determine its classes. The paper results demonstrate the effectiveness and robustness of the proposed algorithm in the determination of the fault class in smart grids.

Step-up DC-DC converters play a determinative role in traction vehicles by providing the required DC- link voltage for traction drives supplied by battery packages or fuel cells. Bi-directional converters are preferred to be used in the construction of battery-based traction drives to charge batteries when drivetrains system work in the generation mode. However, due to the fact that fuel-cells cannot be charged electrically, the bi-directional structure is not required for fuel cell-based traction drives. The proposed step up converter benefits from the resonance between its elements to provide the soft- switching condition for the converter switches. The resonance assists to transmit high power from input and to output while there is no high-frequency transformer or coupled inductor in the converter topology. In brief, the generated resonance not only leads to a high power density converter but also result in a soft-switching which means higher efficiency in comparison to previously introduced high-step up converters working in hard switching condition. So, the proposed high-efficiency high power density high step-up (HHH) converter could be employed as a reliable DC-DC converter for traction vehicles supplied by fuel cells. Refereed/Peer-reviewed

The piezoelectric elements are the most important component of the acoustic transmitters in underwater communication systems. A special DC/AC power supply which produces an AC high voltage level with the frequency of the 30–50 kHz is required to energize the piezoelectric senders. The transmitters must send data in a few milliseconds and stop working about a second to receive data. For this reason, the designed converter must have acceptable power density to energize the piezoelectric actuators during data sending. On the other hand, the small size of the power supplies is another prominent characteristic of a converter because the communication system engineers prefer to have a device that occupies minimum space. In this paper, a DC-DC boost converter increases the input DC voltage level up to 5 times. After that, a full-bridge converter (FBC) is used to step up the boost converter output and provide the required AC voltage for the piezoelectric transmitters. A robust controller is designed to control the power converter switches. With respect to the proposed controller operation and construction, the converter can successfully drive the piezoelectric in noisy and harsh environments. Finally, the simulation and experimental results demonstrate the effectiveness of the designed combined converter and robustness of the proposed controller.

Rapid grows of solar energy systems in recent years makes the application of photovoltaic panels as energy source a prominent option. This is due to its sustainability, decreasing cost, very low pollution of the atmosphere. Pioneering research in the field that makes it the fastest growing energy source in the world. In Iran there is a lot of potential places for use of solar energy. On the other hand, a lot of these places are far from power grid and for their power they depend on diesel generators. In this paper we evaluated the implementation of solar energy with proposing a photovoltaic (PV) system. A case study was done on a small village in Bushehr province, Iran to assess alternative energy sources in comparison with solar energy. The two other options for power source beside solar panels which was considered in this paper is connecting the village to the power grid with a distribution line or installing diesel generators to power the village. It has been shown that the solar power can compete with its old rivals.

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.

In this paper a novel soft switching method for an interleaved boost converter has been introduced and implemented in a PV system. Having low switching losses, high step up ratio, low voltage and current ripples in PV application is something desirable. Robustness and cost are usually neglected in other research related to this subject. Proposed soft switching method in this article does not contain any auxiliary switches however it provide full zero voltage switching condition in turn-off mode and zero current switching in turn-on mode. This simple and effective method in addition to soft switching provide the interleaved boost converter with more stability, because it operates under discrete conduction mode(DCM). At the other hand interleaving three boost converter lead DC-DC converter toward lower current and voltage ripples at both input and output. It is noteworthy that proposed IBC has higher power rate in comparison with single phase boost converters so it can be used in PV power generation system with higher generated power. In order to show the effectiveness of the proposed method a 0.5kW DC-DC converter has been simulated and the results verify the claims which have been made.

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.

The fast industrial developments result in newly created devices in a wide range of applications. Sensors families are one of the most important instruments in industries which provide various useful facilities in controlling and monitoring applications. Precise displacement sensors are needed in any moving applications. The measurement of the linear displacement in the electromechanical systems is a special issue in industries. Linear displacement measuring not only is needed to provide a suitable control system in various systems but also it is necessary to protect some devices in industrial applications such as turbine and generator shafts. In this paper, a low-cost, robust, non-contact, and high precision linear displacement sensor is proposed. The mechanical structure and electrical circuit are two special parts of the sensor. A load cell works as a transducer which converts the mechanical tension to voltage data set. An industrial setup of the sensor is provided and tested to demonstrate the system accuracy and effectiveness in the linear displacement measurement.

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.

A dynamic model for a solar power plant is very vital to be obtained for power and control engineers. In order to achieve a suitable dynamic model that best describes the solar plant characteristics, we have utilized an effective parameter estimation method called prediction error method (PEM). Afterward the estimated parameters have been identified by the iterative and recursive algorithms. The results for each method have been performed numerically, and finally a comparison and analysis of the results have been discussed.

Nowadays, the technology of manufacturing electrical vehicles (EVs) and hybrid electrical vehicles (HEVs) is the most important issue for manufacturers and consumers thereof. The main parts of these cars are their motors and battery packages. Low cost, ruggedness and suitable torque/mass ratio are some of the switched reluctance (SR) machine features that make it superior to permanent magnet and induction machines. The current ripple is the most important characteristic to insure the maximum battery life time. When SR machine works as a generator, the minimum ripple of output current and the level of generated power are considered to be of utmost importance. The suitable control strategy with a combined converter is proposed, in order to achieve these objectives. In this paper, a switched reluctance generator (SRG) operates in battery charging mode while working in HEV. Simulation results demonstrated the eligibility of the proposed control system and power converter to attain the acceptable charging current ripple in appropriate generated power.

With the advancements of photovoltaic panels Solar power plants extend their grip further in the power system every day. It is necessary that these new power plants work together with classic power plants. In this paper the simultaneous operation of a solar power plant with a steam power plant in a power system has been considered. The solar power plant operates with incremental conductance maximum power point tracking (IncCon MPPT) with direct control which is implemented through a boost converter. The inverter of solar power plant is manipulated to perform active power filtering in addition to converting DC power to AC. A d-q reference frame controller is used to perform the active power filtering. The steam power plant is also uses a PSS to stabilize the power system and damp power transients. A metaheuristic method called smart line search teaching learning based optimization (SLS-TLBO) is used to optimize the PSS parameter. In this paper, it is shown that the simultaneous operation of solar power plant can enhance the performance of the power system.

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.