High penetration of wind energy requires innovations in different areas of power engineering. Methods for improving wind energy and power system interconnection, control, and operation are proposed in this dissertation. A feed-forward transient compensation control scheme is proposed to enhance the low-voltage ride-through capability of wind turbines equipped with doubly fed induction generators. Stator-voltage transient compensation terms are introduced to suppress rotor-current overshoots and torque ripples during grid faults. A dynamic stochastic optimal power flow control scheme is proposed to optimally reroute real-time active and reactive power flow in the presence of high variability and uncertainty. The performance of the proposed power flow control scheme is demonstrated in test power systems with large wind plants. A combined energy-and-reserve wind market scheme is proposed to reduce wind production uncertainty. Variable wind reserve products are created to absorb part of the wind production variation. These fast wind reserve products can then be used to regulate system frequency and improve system security.

With the increasing complexity of the power system, electromechanical oscillations are becoming one of the major problem. Several blackouts have been reported in the past due to insufficient damping of the oscillatory modes. The starting point to avoid catastrophic behaviors would be to simulate actual power system and study the response of the system under various outages leading to blackouts. Recently, it has been identified that appropriate modeling of the load is necessary to match the actual system behavior with the computer simulated response. This research throws some insight into the detailed load modeling and its impact on the system small signal stability. In particular, Composite load model is proposed and its effect on the system small signal stability is investigated. Modeling all the loads in a large power system would be a cumbersome job and hence the method for identifying the most sensitive load location is also proposed in the thesis. The effect of load modeling on the eigenvalue movement is also investigated. The low damped electromechanical modes are always undesirable in the large inter-connected power systems as they might get excited under some event leading to growing oscillations. Proper damping of these modes is essential for effective and reliable system operation. Power system stabilizers have been proved to be an effective way of damping these electromechanical modes. The optimal number and location of PSS to effectively damp the modes via improved Differential algorithm is proposed. Moreover, the effect of TCSC, series compensated FACTs device, on enhancing the system damping is investigated. A fixed order model matching technique is presented to design a damping controller for the TCSC. With the increasing global pressure for reducing carbon emissions, there is a great amount of interest in the renewable sources of energy, particularly Wind Energy Conversion Systems. Of all the present methods of wind generation systems, Doubly Fed Induction Generation (DFIG) based wind farms are gaining popularity. The comparison of various methods of wind generation techniques is presented. In particular, the impact of DFIG based wind farms on the system small signal stability is investigated in this work. Co-ordinated tuning of the controllers is performed using Bacterial Foraging Technique, which is another member of Evolutionary algorithms. Damping controller for the DFIG system is proposed to enhance the damping of the electromechanical modes. Results have proved the effectiveness of the control methodology. The contributions made in this thesis could be utilized to promote the further development of the damping controllers for large power systems.

With the increasing complexity of the power system, electromechanical oscillations are becoming one of the major problem. Several blackouts have been reported in the past due to insufficient damping of the oscillatory modes. The starting point to avoid catastrophic behaviors would be to simulate actual power system and study the response of the system under various outages leading to blackouts. Recently, it has been identified that appropriate modeling of the load is necessary to match the actual system behavior with the computer simulated response. This research throws some insight into the detailed load modeling and its impact on the system small signal stability. In particular, Composite load model is proposed and its effect on the system small signal stability is investigated. Modeling all the loads in a large power system would be a cumbersome job and hence the method for identifying the most sensitive load location is also proposed in the thesis. The effect of load modeling on the eigenvalue movement is also investigated. The low damped electromechanical modes are always undesirable in the large inter-connected power systems as they might get excited under some event leading to growing oscillations. Proper damping of these modes is essential for effective and reliable system operation. Power system stabilizers have been proved to be an effective way of damping these electromechanical modes. The optimal number and location of PSS to effectively damp the modes via improved Differential algorithm is proposed. Moreover, the effect of TCSC, series compensated FACTs device, on enhancing the system damping is investigated. A fixed order model matching technique is presented to design a damping controller for the TCSC. With the increasing global pressure for reducing carbon emissions, there is a great amount of interest in the renewable sources of energy, particularly Wind Energy Conversion Systems. Of all the present methods of wind generation systems, Doubly Fed Induction Generation (DFIG) based wind farms are gaining popularity. The comparison of various methods of wind generation techniques is presented. In particular, the impact of DFIG based wind farms on the system small signal stability is investigated in this work. Co-ordinated tuning of the controllers is performed using Bacterial Foraging Technique, which is another member of Evolutionary algorithms. Damping controller for the DFIG system is proposed to enhance the damping of the electromechanical modes. Results have proved the effectiveness of the control methodology. The contributions made in this thesis could be utilized to promote the further development of the damping controllers for large power systems.

The growing demand for power and increased environmental concerns gave an impetus to the growth of clean and renewable energy sources like wind, solar etc. There is a remarkable increase in the penetration of wind energy systems in the last decade and this trend is bound to increase at a much faster pace in future. This ever increasing penetration of wind power generating systems pose multi-fold challenges related to operational and stability aspects of the grid. Present day wind energy systems mostly comprise of variable speed wind generators. A large fraction of present day variable speed wind turbine generators use doubly fed induction machine (DFIM). This thesis deals with modeling and grid coordination aspects of variable speed wind gener- ators. In particular, the short coming of the existing steady state equivalent circuit of a DFIM is identified and subsequently, an accurate equivalent circuit of a DFIM is proposed. Relevant mathematical basis for the proposed model is presented. The proposed steady state equivalent circuit of a doubly fed induction machine is further validated using dynamic simulations of a standalone machine. Based on the proposed equivalent circuit, two approaches for computing the initial values of state variables of a DFIM is proposed. The first approach is a linear formulation where the losses due to resistance of the stator and rotor windings are neglected. The second approach is a non-linear formulation which takes the losses into consideration. Further, analysis is carried out on grid connected doubly fed induction generators (DFIG). A framework to incorporate DFIG based variable speed wind farms in the steady state power flow analysis is proposed. The proposed framework takes into consideration important aspects such as voltage dependent reactive power limits and mode of reactive power control of associated converters. Some of the challenges in a grid connected DFIG especially during su- persynchronous mode of operation are identified. The advantages of a non-Maximum Power Point Tracking (MPPT) mode of operation under certain operating conditions is highlighted. Finally, aspects pertaining to coordination of grid connected variable speed wind generators are studied. A trust region framework to determine the reference values to the control loops of converters in a variable speed wind generator is proposed. The proposed framework identifies the reference values considering other reactive power controllers in the grid. Moreover, the proposed framework ensures that the steady state voltage stability margin is maximized. On the computational front, trust region algorithms ensure global convergence. The mathematical models and initialization algorithms proposed in this thesis are tested on standalone systems under various control scenarios. The algorithms proposed to incorporate a grid connected DFIG in steady state analysis tools have been tested on a sample 6-bus system and a practical 418-bus equivalent system of Indian southern grid.

Indiana University-Purdue University Indianapolis (IUPUI) / The harvested energy of wind can be transferred to the generators either through a gearbox or through an intermediate medium such as hydraulic fluids. In this method, high-pressure hydraulic fluids are utilized to collect the energy of single or multiple wind turbines and transfer it to a central generation unit. In this unit, the mechanical energy of the hydraulic fluid is transformed into electric energy. The prime mover of hydraulic energy transfer unit, the wind turbine, experiences the intermittent characteristics of wind. This energy variation imposes fluctuations on generator outputs and drifts their angular velocity from desired frequencies. Nonlinearities exist in hydraulic wind power transfer and are originated from discrete elements such as check valves, proportional and directional valves, and leakage factors of hydraulic pumps and motors. A thorough understanding of hydraulic wind energy transfer system requires mathematical expression of the system. This can also be used to analyze, design, and predict the behavior of large-scale hydraulic-interconnected wind power plants. This thesis introduces the mathematical modeling and controls of the hydraulic wind energy transfer system. The obtained models of hydraulic energy transfer system are experimentally validated with the results from a prototype. This research is classified into three categories. 1) A complete mathematical model of the hydraulic energy transfer system is illustrated in both ordinary differential equations and state-space representation. 2) An experimental prototype of the energy transfer system is built and used to study the behavior of the system in different operating configurations, and 3) Controllers are designed to address the problems associated with the wind speed fluctuation and reference angular velocity tracking. The mathematical models of hydraulic energy transfer system are also validated with the simulation results from a SimHydraulics Toolbox of MATLAB/Simulink®. The models are also compared with the experimental data from the system prototype. The models provided in this thesis do consider the improved assessment of the hydraulic system operation and efficiency analysis for industrial level wind power application.

MSc (Statistics) / Department of Statistics / Wind offers an environmentally sustainable energy resource that has seen increasing global adoption in recent years. However, its intermittent, unstable and stochastic nature hampers its representation among other renewable energy sources. This work addresses the forecasting of wind speed, a primary input needed for wind energy generation, using data obtained from the South African Wind Atlas Project. Forecasting is carried out on a two days ahead time horizon. We investigate the predictive performance of artificial neural networks (ANN) trained with Bayesian regularisation, decision trees based stochastic gradient boosting (SGB) and generalised additive models (GAMs). The results of the comparative analysis suggest that ANN displays superior predictive performance based on root mean square error (RMSE). In contrast, SGB shows outperformance in terms of mean average error (MAE) and the related mean average percentage error (MAPE). A further comparison of two forecast combination methods involving the linear and additive quantile regression averaging show the latter forecast combination method as yielding lower prediction accuracy. The additive quantile regression averaging based prediction intervals also show outperformance in terms of validity, reliability, quality and accuracy. Interval combination methods show the median method as better than its pure average counterpart. Point forecasts combination and interval forecasting methods are found to improve forecast performance. / NRF