Mohamed Kamal El-Nemr

Switched reluctance motor (SRM) is gaining more interest in both research and industry. Its simple structure without windings or permanent magnets on the rotor makes the motor robust and reliable with reduced manufacturing cost. The SRM also provides high starting torque and high efficiency over a wide range of speeds, which is strongly desired for electric vehicles’ applications. However, these advantages of switched reluctance motors come with some challenges. Torque ripples, low power density, and temperature rise are common questions about SRM. This paper utilizes multi-objective optimization of SRM design to get most of the SRM desired characteristics with minimization of the machine’s common drawbacks. The optimization process has considered twelve variables and five objective functions. These functions include average torque, efficiency, iron weight, torque-ripples, and maximum temperature rise. The electromagnetic analysis of each candidate is performed by the finite elements method (FEA). The performance indices of SRM are calculated based on FEA analysis results via calculations that compensate for accuracy and computation time. The multi-objective genetic algorithm technique (MOGA) combines the objective functions into a single objective function. Verifying the optimal design comprises generating the efficiency map, torque profile, and dynamic simulation of the motor. This paper mainly focuses on the design and optimization of SRM to fulfill the general requirements of electric vehicle applications.

This paper proposes an effective algorithm; this algorithm is updating the phase-locked loop (PLL) gains according to the value of the short circuit ratio that will decrease the weak grid effect in the PLL controllers to reduce problems related to renewable energy sources and connect to the point of common coupling (PCC). Based on system parameters estimation, the proposed algorithm enhances the system stability for the distribution networks. The standard IEEE 33-bus test system is used to investigate the proposed algorithm’s effectiveness and superiority. An extensive mathematical effort is exerted to get the system’s small-signal stability (SSS) model. The tuning of the phase-locked loop and the current control loop are choosing to dynamically change according to the system variables to ensure the same system stability margin, whatever the change in the loading conditions. The MATLAB/Simulink model is used to validate the algorithm and simulate the test system.

This paper studies the sizing of stand-alone renewable energy system applied in rural areas in the north of Egypt. The available renewable energy sources in these areas are investigated to be integrated to supply the different types of electrical loads. The quality and quantity of these sources over various weather and climate changes are studied to construct a robust energy system. The load demand in such areas is determined according to all activities require electrical energy. This study considers the different economic levels and technologies which affect the load demand value. The technique and economical indices required to obtain the optimal are investigated and applied in the various estimated cases. The genetic algorithm (GA) technique is applied to determine the size and number of photovoltaic panels and wind turbines. The obtained solution takes into account the loss of power supply probability and the minimization of system cost. This study presents an essential phase in the sustainable development of such rural areas.

The Gulf of Suez is highly susceptible to economic and energy security due to the heavy need for imported fossil fuels. The energy produced from fossil fuels causes serious environmental pollution. So, it is important to find other clean sources of energy for stabilizing the released amount of carbon dioxide. Gulf of Suez has the potential to develop clean Solar Photovoltaic (PV) and wind systems to improve economic and environmental performance. This paper proposes a combination of floating solar panels (FSP) and offshore wind turbines to solve the main critical issues, such as the land area required for a large number of cells and the space required for wind turbines. In addition to their good economical and environmental benefits. A simplified model is provided to investigate the replacement of traditional generating units (Diesel and Gas operated) supplying offshore oil production field facilities at the Gulf of Suez area with a mix of renewable resources. The load flow analysis is carried out for the model and the cost is calculated and compared for four schemas, the traditional generating, Wind-Gas, PV-Gas, and PV-Wind-Gas hybrid system. From the techno-economic analysis and comparing environmental benefits, floating solar systems and offshore wind turbines can provide a good solution to the existing problems of energy needs with environmental impacts considered.

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

The design of switched reluctance motor (SRM) is considered a complex problem to be solved using conventional design techniques. This is due to the large number of design parameters that should be considered during the design process. Therefore, optimization techniques are necessary to obtain an optimal design of SRM. This paper presents an optimal design methodology for SRM using the non-dominated sorting genetic algorithm (NSGA-II) optimization technique. Several dimensions of SRM are considered in the proposed design procedure including stator diameter, bore diameter, axial length, pole arcs and pole lengths, back iron length, shaft diameter as well as the air gap length. The multi-objective design scheme includes three objective functions to be achieved, that is, maximum average torque, maximum efficiency and minimum iron weight of the machine. Meanwhile, finite element analysis (FEA) is used during the optimization process to calculate the values of the objective functions. In this paper, two designs for SRMs with 8/6 and 6/4 configurations are presented. Simulation results show that the obtained SRM design parameters allow better average torque and efficiency with lower iron weight. Eventually, the integration of NSGA-II and FEA provides an effective approach to obtain the optimal design of SRM.

Linear generators have been proposed as a proper power takeoff system for direct drive wave energy conversion. Coupled directly to a reciprocating wave energy device, it was declared that linear generators could be a respected replacement for hydraulic and pneumatic systems. In this paper, slotless permanent magnet Halbach array linear generators is designed to extract the wave energy. The design of these generators is suitable for direct energy extraction from sea waves at small wave amplitude. The electromagnetic design is performed using finite element method FEM and the genetic algorithm GA as an optimization technique. In this paper, the parameters affect the maximum power extraction from wave is studied according to the hydrodynamics modeling of direct wave energy converters. Equivalent circuit based model is utilized to investigate the dynamic performance of the Halbach array linear generator according to maximum power transfer condition. The control strategy of the converter is explained to justify the maximum power transfer conditions.

Voltage source inverters (VSIs) can be functioned in parallel with the aim of using limited power rating switching devices in high power electric drive systems. However, appropriate modulation strategies should be selected to provide the desired output voltage waveform at low switching frequency. Selective harmonic elimination (SHE) is one of the most adopted pulse width modulation (PWM) strategies that eliminate low-order harmonics from the output voltage. This paper proposes a genetic algorithm (GA) based SHE-PWM strategy for two VSIs operated in parallel and applied for electric drive system. The proposed modulation technique can eliminate extra harmonic contents from the output voltage by using different switching forms simultaneously. The optimum firing angles of each VSI is determined by solving a multi-objective optimization problem using GA. The various functions and configuration parameters that are required for solving the optimization problem are presented and described. Simulation results using different firing angles are presented to verify and validate the advantages of the proposed modulation strategy.

Paralleling of three-phase voltage-source inverters (VSIs) is recommended to allow using low power rating VSI modules in high power applications. This paper presents the operation of two-module three-phase two-level VSIs using a selective harmonic elimination pulse width modulation (SHE-PWM) approach to reducing the output voltage harmonics. Unlike the standard PWM approach, a different switching pattern for each VSI module is proposed to eliminate more harmonics from the output voltage. Moreover, genetic algorithm (GA) optimization technique is used offline to determine the optimum switching angles by solving the nonlinear transcendental equation set. Simulation results show how more output voltage harmonics can be eliminated with the proposed SHE-PWM approach than with the standard approach. Both the mathematical formulations used to generate suitable PWM patterns and simulation results are presented in this paper.