Thesis (Master of Engineering in Electrical Engineering)--Cape Peninsula University of Technology, 2018. / Gas turbines have achieved widespread popularity in industrial fields. This is due to the high power, reliability, high efficiency, and its use of cheap gas as fuel. However, a major draw-back of gas turbines is due to the strong function of ambient air temperature with its output power. With every degree rise in temperature, the power output drops between 0.54 and 0.9 percent. This loss in power poses a significant problem for utilities, power suppliers, and co-generations, especially during the hot seasons when electric power demand and ambient temperatures are high. One way to overcome this drop in output power is to cool the inlet air temperature. There are many different commercially available means to provide turbine inlet cooling. This disserta-tion reviews the various technologies of inlet air cooling with a comprehensive overview of the state-of-the-art of inlet fogging systems. In this technique, water vapour is being used for the cooling purposes. Therefore, the water quality requirements have been considered in this thesis. The fog water is generally demin-eralized through a process of Reverse Osmosis (RO). The drawback of fogging is that it re-quires large amounts of demineralized water. The challenge confronting operators using the fogging system in remote locations is the water scarcity or poor water quality availability. However, in isolated hot areas with high levels of radiation making use of solar PV energy to supply inlet cooling system power requirements is a sustainable approach. The proposed work herein is on the development of a photovoltaic (PV) application for driv-ing the fogging system. The design considered for improved generation of Acaica power plant in Cape Town, South Africa. In addition, this work intends to provide technical infor-mation and requirements of the fogging system design to achieve additional power output gains for the selected power plant.

Dissertation submitted for Masters of Science degree in Mathematical Statistics in the Faculty of Science, School of Statistics and Actuarial Science, University of the Witwatersrand Johannesburg May 2016 / Electricity demand in South Africa exhibit some randomness and has some important implications on scheduling of generating capacity and maintenance plans. This work focuses on the development of a short term probabilistic forecasting model for the 19:00 hours daily demand. The model incorporates deterministic influences such as; temperature effects, maximum electricity demand, dummy variables which include the holiday effects, weekly and monthly seasonal effects. A benchmark model is developed and an out-of-sample comparison between the two models is undertaken. The study further assesses the residual demand analysis for risk uncertainty. This information is important to system operators and utility companies to determine the number of critical peak days as well as scheduling load flow analysis and dispatching of electricity in South Africa. Keywords: Semi-parametric additive model, generalized Pareto distribution, extreme value mixture modelling, non stationary time series, electricity demand

Digitized by Kansas Correctional Industries

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Thesis (Ph. D. in electrical engineering)--Washington State University, December 2007. / Includes bibliographical references (p. 135-146).

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

Advanced technologies, a world-wide trend to deregulation of power systems and environmental constraints have attracted increasing interest in the operation of electric power systems with multiple storage resources. Under the competitive pressure of the deregulation, new efficient solution techniques to adapt quickly to the changing marketplace are in demand. This thesis presents a new evolutionary method, Constructive Evolutionary Programming (CEP), for minimising the system operational cost of scheduling electric power systems with multiple storage resources. The method combines the advantages of Constructive Dynamic Programming and Evolutionary Programming. Instead of evolving the "primal" variables such as storage content releases and thermal generator outputs, CEP evolves the piecewise linear convex cost-to-go functions (i.e. the storage content value curves). The multi-stage problem of multi-storage power system scheduling is thus decomposed into many smaller one-stage subproblems with evolved cost-to-go functions. For each evolutionary individual, linear programming is used in the forward pass process to solve the dispatch subproblems and the total system operational cost over the scheduling period is assigned to its fitness. Case studies demonstrate that the proposed method is robust and efficient for multi-storage power systems, particularly large complex hydrothermal system with cascaded and pumped storages. Although the proposed method is in the early stage of development, relying on assumptions of piecewise linear convexity in a deterministic environment, methods for the incorporation of stochastic models, electrical network and nonlinear, non-convex and non-smooth models are discussed. In addition, a number of possible improvements are also outlined. Due to its simplicity but robustness and efficiency, there are potential research directions for the further development of this evolutionary optimisation method.

No description available.

Today's power systems are undergoing dynamic changes in their operation. The high cost of capital improvements that include new generation and transmission projects has prompted power system planners to look for other alternatives in dealing with increased loads and overall system growth. A dynamic braking resistor is a device that allows for an increased rating of a transmission system's transient stability limit. This allows increased power flows over existing transmission lines without the need to build additional transmission facilities. This thesis investigates the application of dynamically controlled resistive braking in the Pacific Northwest power system. Specifically, possible control alternatives, to replace the present dynamic brake control system at Chief Joseph station, are examined. This examination includes determination of appropriate locations for control system input, development of control algorithms, development of computer and laboratory power system models, and testing and recommendations based upon the developed control algorithms. / Graduation date: 1995

When large disturbances occur in interconnected power systems, there exists the danger that the power system states may leave an associated region of stability, if timely corrective action is not taken. Open-loop remedial control actions such as field-forcing, line-tripping, switching of series-capacitors, energizing braking resistors, etc., are helpful in reducing the effects of the disturbance, but do not guarantee that the post-fault power system will be stabilized. Linear controllers are widely used in the power industry, and provide excellent damping when the power system state is close to the equilibrium. In general, they provide conservative regions of stability. This study focuses on the development of nonlinear controllers to enhance the stability of interconnected power systems following large disturbances, and allow stable operation at high power levels. There is currently interest in the power industry in using thyristor-controlled series-capacitors for the dual purpose of exercising tighter control on steady-state power flows and enhancing system stability. This device is used to implement the nonlinear controller in this dissertation. A mathematical model of the power system controlled by the thyristor-controlled series-capacitor is developed for the purpose of controller design. Discrete-time, nonlinear predictive controllers are derived by minimizing criterion functions that are quadratic in the output variables over a finite-horizon of interest, with respect to the control variables. The control policies developed in this manner are centralized in nature. The stabilizing effect of such controllers is discussed. A potential drawback is the need to have large prediction horizons to assure stability. In this context, a coordinated-control policy is proposed, in which the nonlinear predictive controller is designed with a small prediction horizon. For a class of disturbances, such nonlinear predictive controllers return the power system state to a small neighborhood of the post-fault equilibrium, where linear controllers provide asymptotic stabilization and rapid damping. Methods of coordinating the controllers are discussed. Simulation results are provided on a sample four-machine power system model. There exists considerable uncertainty in power system models due to constantly shifting loads and generations, line-switching following disturbances, etc. The performance of fixed-parameter controllers may not be good when the plant description changes considerably from the reference. In this context, a bilinear model-based self-tuning controller is proposed for the stabilization of power systems for a class of faults. A class of generic predictive controllers are presented for use with the self-tuning controller. Simulation results on single-machine and multimachine power systems are provided. / Graduation date: 1995