Hany M. Hasanien

Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Performance Improvement Using Response Surface Methodology and Genetic Algorithms

Permanent magnet (PM) type transverse flux linear motors (TFLMs) are electromagnetic devices, which can develop directly powerful linear motion. These motors have been developed to apply to high power system, such as railway traction, electrodynamics vibrator, free-piston generator, etc. This paper presents an optimum design of a PM-type TFLM to reduce the weight of motor with constraints of thrust and detent force using response surface methodology (RSM) and genetic algorithms (GAs). RSM is well adapted to make analytical model of motor weight with constraints of thrust and detent forces, and enable objective function to be easily created and a great computational time to be saved. Finite element computations have been used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. GAs are used as a searching tool for design optimization of TFLM to reduce the weight of motor and improve the motor performance.

Particle Swarm Design Optimization of Transverse Flux Linear Motor for Weight Reduction and Improvement of Thrust Force

Particle swarm optimization (PSO) is a computational intelligence-based technique that is not largely affected by the size and nonlinearity of the problem and can converge to the optimal solution in many problems where most analytical methods fail to converge. The PSO algorithm is applied to the design optimization problem of a permanent-magnet type transverse flux linear motor (TFLM). The objective of the optimization is to reduce the motor weight while maximizing the thrust force as well as minimizing the detent force of the motor. The stator pole length, the air gap length, the winding window width, and the stator pole width define the search space for the optimization problem. Response surface methodology (RSM) is well adapted to obtain an analytical model of the motor weight, detent force, and thrust force. The RSM enables objective functions to be easily created and a great computational time to be saved. Finite element computations are used for numerical experiments on geometrical design variables in order to determine the coefficients of a second-order analytical model for the RSM. The finite element analysis based model is verified by experimental results. The effectiveness of the proposed PSO model is then compared with that of the conventional optimization models and genetic algorithms model. With this proposed PSO technique, the weight of the initially designed TFLM and its detent force can be reduced, as well as its thrust force can be increased.

Design Optimization of Controller Parameters Used in Variable Speed Wind Energy Conversion System by Genetic Algorithms

This paper presents an optimum design procedure for the controller used in the frequency converter of a variable speed wind turbine (VSWT) driven permanent magnet synchronous generator (PMSG) by using genetic algorithms (GAs) and response surface methodology (RSM). The cascaded control is frequently used in the control of the frequency converter using the proportional plus integral (PI) controllers. The setting of the parameters of the PI controller used in a large system is cumbersome, especially in an electrical power system, which is difficult to be expressed by a mathematical model or transfer function. This study attempts to optimally design the parameters of the PI controllers used in the frequency converter of a variable speed wind energy conversion system (WECS). The effectiveness of the designed parameters using GAs-RSM is then compared with that obtained using a generalized reduced gradient (GRG) algorithm considering both symmetrical and unsymmetrical faults. The permanent fault condition due to unsuccessful reclosing of circuit breakers is considered as well. It represents another salient feature of this study. It is found that fault-ride-through of VSWT-PMSG can be improved considerably using the parameters of its frequency converter obtained from GAs-RSM.

Design Optimization of PID Controller in Automatic Voltage Regulator System Using Taguchi Combined Genetic Algorithm Method

The optimum design of the proportional-integral-derivative (PID) controller plays an important role in achieving a satisfactory response in the automatic voltage regulator (AVR) system. This paper presents a novel optimal design of the PID controller in the AVR system by using the Taguchi combined genetic algorithm (TCGA) method. A multiobjective design optimization is introduced to minimize the maximum percentage overshoot, the rise time, the settling time, and the steady-state error of the terminal voltage of the synchronous generator. The proportional gain, the integral gain, the derivative gain, and the saturation limit define the search space for the optimization problem. The approximate optimum values of the design variables are determined by the Taguchi method using analysis of means. Analysis of variance is used to select the two most influential design variables. A multiobjective GA is used to obtain the accurate optimum values of these two variables. MATLAB toolboxes are used for this paper. The effectiveness of the proposed method is then compared with that of the earlier GA method and the particle swarm optimization method. With this proposed TCGA method, the step response of the AVR system can be improved.

Single and Multi-objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms

Abstract This article applies the grey wolf optimizer and differential evolution (DE) algorithms to solve the optimal power flow (OPF) problem. Both algorithms are used to optimize single objective functions sequentially under the system constraints. Then, the DE algorithm is utilized to solve multi-objective OPF problems. The indicator of the static line stability index is incorporated into the OPF problem. The fuzzy-based Pareto front method is tested to find the best compromise point of multi-objective functions. The proposed algorithms are used to determine the optimal values of the continuous and discrete control variables. These algorithms are applied to the standard IEEE 30-bus and 118-bus systems with different scenarios. The simulation results are investigated and analyzed. The achieved results show the effectiveness of the proposed algorithms in comparison with the other recent heuristic algorithms in the literature.

Shuffled Frog Leaping Algorithm for Photovoltaic Model Identification

In simulation studies of photovoltaic (PV) systems with power electronic converters, the simulation results are affected by the accuracy of the PV model. The maximum power point tracking, transient and dynamic analysis of the PV systems, and operation of microgrids systems are examples of these simulation studies. The mathematical model of the PV module is a nonlinear I-V characteristic that includes several unknown parameters because of the limited information provided by the PV manufacturers. This paper presents a novel approach using the shuffled frog leaping algorithm (SFLA) to determine the unknown parameters of the single diode PV model. The validity of the proposed PV model is verified by the simulation results which are performed under different environmental conditions. The simulation results are compared with the experimental results of different PV modules such as Kyocera KC200GT and Solarex MSX-60. The effectiveness of the proposed PV model is evaluated by comparing the absolute error of the model with respect to the experimental results with that of other PV models. With the application of the SFLA technology, an accurate PV model can be achieved.

A Taguchi Approach for Optimum Design of Proportional-Integral Controllers in Cascaded Control Scheme

This study aims at resolving a specific problem in the area of renewable energy, energy storage systems, variable-speed drives, and flexible AC transmission system (FACTS) devices used in the electric power systems. The use of power conversion system (PCS) is very common in the aforementioned areas. A cascaded control scheme based on four proportional-integral (PI) controllers is widely used in the control of the PCS unit. Setting the parameters of the four cascaded PI controllers simultaneously is cumbersome, especially when the application is in the area of electrical power system which is difficult to express using a mathematical model or transfer function due to its nonlinear properties. This paper presents an optimum design procedure for the cascaded controller of the PCS unit using Taguchi method. To apply the design parameters obtained from Taguchi method, a renewable energy application is chosen where a variable-speed wind turbine generator system is connected to the power grid through two back-to-back PCS units. The effectiveness of the designed parameters using Taguchi method is then compared with that obtained using response surface methodology and genetic algorithm (RSM-GA) method under the grid fault condition.

A Fuzzy Logic Controller for Autonomous Operation of a Voltage Source Converter-Based Distributed Generation System

This paper presents a fuzzy logic controller (FLC) for autonomous (islanded) operation of an electronically interfaced distributed generation unit and its load. In the grid-connected mode, the voltage-sourced converter is operated in the active and reactive power (PQ) control mode, where a conventional control scheme is used to control the active and reactive power exchange with the grid. In the islanded mode, the proposed FLC is used to control the voltage of the islanded system despite the load variability and uncertainties. In addition, this paper also presents the use of the black-box nonlinear optimization technique to tune the parameters of the membership functions of the FLC in order to achieve optimal performance. The salient features of the proposed FLC are: 1) efficient to deal with the nonlinear systems; 2) design does not depend on the mathematical model of the system; and 3) less sensitive to the parameters variation than the conventional controllers. The frequency of the islanded system is dictated through the use of an internal oscillator. The effectiveness of the proposed FLC in controlling the voltage of the islanded system, irrespective of the load variability, is extensively validated based on simulation studies in the PSCAD/EMTDC environment. Moreover, the paper highlights the superiority of the proposed FLC over the conventional proportional-integral controllers through comparing the transient responses of the system based on both controllers.

An Adaptive Control Strategy for Low Voltage Ride Through Capability Enhancement of Grid-Connected Photovoltaic Power Plants

This paper presents a novel application of continuous mixed p-norm (CMPN) algorithm-based adaptive control strategy with the purpose of enhancing the low voltage ride through (LVRT) capability of grid-connected photovoltaic (PV) power plants. The PV arrays are connected to the point of common coupling (PCC) through a DC-DC boost converter, a DC-link capacitor, a grid-side inverter, and a three-phase step up transformer. The DC-DC converter is used for a maximum power point tracking operation based on the fractional open circuit voltage method. The grid-side inverter is utilized to control the DC-link voltage and terminal voltage at the PCC through a vector control scheme. The CMPN algorithm-based adaptive proportional-integral (PI) controller is used to control the power electronic circuits due to its very fast convergence. The proposed algorithm updates the PI controller gains online without the need to fine tune or optimize. For realistic responses, the PV power plant is connected to the IEEE 39-bus New England test system. The effectiveness of the proposed control strategy is compared with that obtained using Taguchi approach-based an optimal PI controller taking into account subjecting the system to symmetrical, unsymmetrical faults, and unsuccessful reclosing of circuit breakers due to the existence of permanent fault. The validity of adaptive control strategy is extensively verified by the simulation results, which are carried out using PSCAD/EMTDC software. With the proposed adaptive-controlled PV power plants, the LVRT capability of such system can be improved.

A new control strategy for smoothing of wind farm output using short-term ahead wind speed prediction and Flywheel energy storage system

With the increased integration of wind energy into power networks it has become more important to have a reliable system for mitigating the fluctuations of output power supplied to the grid. This paper proposes a new control scheme to smooth the output power fluctuations of an aggregated wind firm using Flywheel energy storage system (FESS) and short-term ahead prediction of wind speed. In the proposed system, the kinetic energy of the FEES is utilized to smooth the output power fluctuations of wind farm. In addition, the stored energy of FESS is utilized in most efficient way by correcting the power reference using prediction base control. This helps to reduce the overall system cost by keeping the size of the energy storage system at minimum. The effectiveness of the proposed control is verified by using PSCAD/EMTDC.