A HYBRID APPROACH FOR RURAL FEEDER DESIGN

In this paper, a population based approach for conductor size selection in rural radial distribution system is presented. The proposed hybrid approach implies a particle swarm optimization (PSO) approach in combination with mutant property of differential evolution (DE) for conductor size selection in radial distribution system. The conductor size for each feeder segment is selected such that the total cost of capital investment and capitalized cost of energy losses is minimized while constraints of voltage at each node and current carrying capacity of conductor is within the limits. The applicability and effectiveness of the proposed method is demonstrated with the help of 32-node test system

Online Control of Modular Active Power Line Conditioner to Improve Performance of Smart Grid

This thesis is explored the detrimental effects of nonlinear loads in distribution systems and investigated the performances of shunt FACTS devices to overcome these problems with the following main contribution: APLC is an advanced shunt active filter which can mitigate the fundamental voltage harmonic of entire network and limit the THDv and individual harmonic distortion of the entire network below 5% and 3%, respectively, as recommended by most standards such as the IEEE-519

DC Distribution with Fuel Cells as Distributed Energy Resources

In recent years, the idea of a direct current (DC) distribution systems has returned to the public eye as a result of DC based energy sources. The battle between alternating current (AC) and DC began with Thomas Edison and George Westinghouse long ago with AC becoming the victor as a result of the transformer. However, development of power electronics and switching devices and DC based energy sources such as fuel cells has forced the reevaluation of DC systems. This dissertation is focused on once again addressing this topic, but with an examination of the technologies as they stand today. Along with examining basic efficiency of the competing technologies, distributed generation has as well peaked the interest and will be incorporated into the analysis. Optimum placement of this distributed generation (DG) source is vital to obtain the maximum benefit and techniques for optimization are examined. The sensitivity and “rules of thumb” for placement of DG in a DC system are developed. Last, the largest driving factor for system changes is cost. A brief examination of the economic factors that have the potential to drive DC systems is examined

Applications of Computational Intelligence to Power Systems

In power system operation and control, the basic goal is to provide users with quality electricity power in an economically rational degree for power systems, and to ensure their stability and reliability. However, the increased interconnection and loading of the power system along with deregulation and environmental concerns has brought new challenges for electric power system operation, control, and automation. In the liberalised electricity market, the operation and control of a power system has become a complex process because of the complexity in modelling and uncertainties. Computational intelligence (CI) is a family of modern tools for solving complex problems that are difficult to solve using conventional techniques, as these methods are based on several requirements that may not be true all of the time. Developing solutions with these “learning-based” tools offers the following two major advantages: the development time is much shorter than when using more traditional approaches, and the systems are very robust, being relatively insensitive to noisy and/or missing data/information, known as uncertainty

Advanced Signal Processing Techniques Applied to Power Systems Control and Analysis

The work published in this book is related to the application of advanced signal processing in smart grids, including power quality, data management, stability and economic management in presence of renewable energy sources, energy storage systems, and electric vehicles. The distinct architecture of smart grids has prompted investigations into the use of advanced algorithms combined with signal processing methods to provide optimal results. The presented applications are focused on data management with cloud computing, power quality assessment, photovoltaic power plant control, and electrical vehicle charge stations, all supported by modern AI-based optimization methods

Fuel Cell Renewable Hybrid Power Systems

Climate change is becoming visible today, and so this book—through including innovative solutions and experimental research as well as state-of-the-art studies in challenging areas related to sustainable energy development based on hybrid energy systems that combine renewable energy systems with fuel cells—represents a useful resource for researchers in these fields. In this context, hydrogen fuel cell technology is one of the alternative solutions for the development of future clean energy systems. As this book presents the latest solutions, readers working in research areas related to the above are invited to read it

INTELLIGENT OPTIMIZATION OF INTERLINE POWER FLOW CONTROLLER IN TRANSMISSION SYSTEM

Flexible AC Transmission system (FACTS) controllers are widely accepted worldwide to provide benefits in increasing power transfer capability and maximizeing the use of the existing transmission networks. A new generation of FACTS controllers, particularly the Interline Power Flow Controller (IPFC) based on voltage source converter (VSC) provides fast power flow control flexibility. The IPFC with its unique capability of power flow management is significantly extended to control power flows of multi-lines or a sub network. Generally IPFC employs two or more VSCs connected together with DC links and each converter provides series compensation for the selected line of the transmission system. Optimal power flow is an important factor in power system operation, planning and control. In this thesis, the mathematical model of IPFC together with the modified Newton-Raphson method for power flow is used to derive the optimal parameters (the magnitude and voltage angles) of VSCs of IPFC. The optimal parameters are derived to minimize the transmission line losses using three intelligent optimization techniques, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). The proposed methods are applied using MATLAB 7.6 and tested on IEEE 14-bus and 30-bus bench mark power systems. The optimal parameters of IPFC, the voltage profile and the transmission line losses of the bench mark power systems are derived from the simulations. The simulation results obtained with PSO technique are compared with those obtained by other two optimization techniques. The thesis also covers the basic principles and operation of IPFC, the modified Newton-Raphson power flow method and an overview of the three intelligent optimization techniques used in this thesis. The results prove the efficacy of the three intelligent methods for the optimization of IPFC parameters and minimization of transmission line losses