This thesis focuses on using power electronic techniques to addresses grid integration for wind energy conversion systems. Different approaches to mitigate the low frequency generator torque ripple caused by diode bridge rectifiers are proposed. The advantages and disadvantages of the methods are discussed. A relationship for maximum power point tracking is theoretically analysed. Based on this relationship, two new maximum power point tracking techniques are proposed, which show benefits over conventional tracking methods. Then a pulsewidth modulated current source converter based parallel connected wind energy conversion system is investigated. A new inverter controller for this system is proposed, which is able to maintain a constant average DC network voltage to give satisfactory system performance whilst controlling output reactive power. Practical results support the presented simulations. Furthermore, a fault ride through scheme is proposed for the current source converter based system. The protection scheme uses a shunt resistive chopper to dissipate the active power during faults, and allows the inverter to supply reactive power to support the grid. The space vector modulation for the current source inverter is modified for this application and the design of the dumping resistor is discussed. The system shows riding through capability to both AC and DC network disturbances. This aspect is substantiated by simulation.

The technique of damping torque analysis (DTA) is proposed on the basis of linearized Philips-Heffron model of a single-machine infinite-bus power system. Analysis is to examine the electric torque contribution from a particular source to the electromechanical oscillation loop of generator in the power system. An effective engineering method - phase compensation method - for the design of PSS was developed on the basis of DTA. In study of real large power systems, modeling of the whole power system is very complicated. The generators in the large power system and other equipments result in a very high order of system model. Therefore a proper simplification method has to be applied to reduce the high order of real large power system. The aim of system simplification is to reduce the order of the linearized model of the large power systems, maintaining the characteristics of the under studied low frequency. Power system stabilizers (PSSs) have been adopted to mitigate low-frequency oscillations in the grid. However, in concerning of efficiency of damping controller in the power systems, decomposed analysis of PSS is studied to be further evidence for selecting installing location of PSS in the power systems. With greater integration of wind turbine generators in the introduced realistic large-scale power systems, impact of doubly fed induction generators (DFIGs) has been investigate for further study of renewable energy connected power systems. In terms of coordinated design of PMU and PSS in real large-scale power systems, developments in communication systems have made it feasible to also use remote signals as PSS inputs provided by PMU units. So as to the selection of signals from multiple locations can be further improved..

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Pulse width modulation (PWM) inverters convert a direct current (DC) power supply to an alternating current (AC) supply by means of high frequency switching between two DC sources. Undesirable high-frequency components are generated in the frequency spectra of the voltages and currents of PWM inverters. The high-frequency components are ultimately removed from the input and output waveforms by filters. PWM inverters are used in a wide variety of electrical devices, ranging from microwave ovens to the electrical parts of aircraft. In many of these devices, minimising the size and weight of the electrical parts is important, and, consequently, it is desirable to design efficient filters for PWM inverters. Identification of the unwanted high-frequency components allows for optimal filter design. In this thesis we use alternative methods to calculate the voltages and currents of PWM inverters. Mathematical models are developed for several PWM inverter designs, and Fourier analysis of the mathematical expressions for the currents and voltages allow us to determine frequency spectra. The methods used in this thesis are shown to be more suitable to the calculation of spectra for complex inverter designs, compared to conventional techniques. In particular, input current spectra are calculated for PWM inverters that incorporate dead time and space vector modulation (SVM) inverters for the first time here.

The planning of distribution systems consists in determining the optimum site and size of new substations and feeders in order to satisfy the future power demand with minimum investment and operational costs and an acceptable level of reliability. This problem is a combinatorial, non-linear and constrained optimization problem. Several solution methods based on genetic algorithms have been reported in the literature; however, some of these methods have been reported with applications to small systems while others have long solution time. In addition, the vast majority of the developed methods handle planning problems simplifying them as single-objective problems but, there are some planning aspects that can not be combined into a single scalar objective; therefore, they require to be treated separately. The cause of these shortcomings is the poor representation of the potential solutions and their genetic operators This thesis presents the design of a genetic algorithm using a direct representation technique and specialized genetic operators for power distribution system expansion planning problems. These operators effectively preserve and exploit critical configurations that contribute to the optimization of the objective function. The constraints of the problems are efficiently handle with new strategies. The genetic algorithm was tested on several theoretical and real large-scale power distribution systems. Problems of network reconfiguration for loss reduction were also included in order to show the potential of the algorithm to resolve operational problems. Both single-objective and multi-objective formulations were considered in the tests. The results were compared with results from other heuristic methods such as ant colony system algorithms, evolutionary programming, differential evolution and other genetic algorithms reported in the literature. From these comparisons it was concluded that the proposed genetic algorithm is suitable to resolve problems of largescale power distribution system planning. Moreover, the algorithm proved to be effective, efficient and robust with better performance than other previous methods.

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)