CLOSED LOOP ENERGY MAXIMIZING CONTROL OF A WAVE ENERGY CONVERTER USING AN ESTIMATED LINEAR MODEL THAT APPROXIMATES THE NONLINEAR FROUDE-KRYLOV FORCE

Wave energy converters (WECs) exploit ocean wave energy and convert it into useful forms such as electricity. But for WECs to be successful on a large scale, two primary conditions need to be satisfied. The energy generated must satisfy the network requirements, and second, energy flow from waves to the grid needs to be maximized. In this dissertation, we address the second problem. Most control techniques for WECs today use the Cummins\u27 linear model to simulate WEC hydrodynamics. However, it has been shown that under the application of a control force, where WEC motions are amplified, the linear model diverges from actual motions. Hence, it becomes necessary to model the nonlinear motion for realistic energy capture prediction. In this work, it is shown that a closed form energy optimal solution to the nonlinear model requires satisfaction of initial conditions that violate physical restrictions. Numerical optimization based controllers that use physical constraints as a necessary condition require large computation costs and are difficult to implement in real time. To mitigate computation costs for real-time implementation while precisely predicting nonlinear behavior, an efficient method of modelling WECs using an estimated linear model for computing the energy optimal control solution is presented. The estimated linear model is compared against the Cummins\u27 model for accuracy of motion during an uncontrolled case. It is also shown that, there exists a force which results in higher energy extraction than optimal force from Cummins\u27 model when applied to a nonlinear model. Additional analyses are also performed to evaluate the robustness of the proposed method in random and extreme sea states

Soft Computing approaches in ocean wave height prediction for marine energy applications

El objetivo de esta tesis consiste en investigar el uso de técnicas de Soft Computing (SC) aplicadas a la energía producida por las olas o energía undimotriz. Ésta es, entre todas las energías marinas disponibles, la que exhibe el mayor potencial futuro porque, además de ser eficiente desde el punto de vista técnico, no causa problemas ambientales significativos. Su importancia práctica radica en dos hechos: 1) es aproximadamente 1000 veces más densa que la energía eólica, y 2) hay muchas regiones oceánicas con abundantes recursos de olas que están cerca de zonas pobladas que demandan energía eléctrica. La contrapartida negativa se encuentra en que las olas son más difíciles de caracterizar que las mareas debido a su naturaleza estocástica. Las técnicas SC exhiben resultados similares e incluso superiores a los de otros métodos estadísticos en las estimaciones a corto plazo (hasta 24 h), y tienen la ventaja adicional de requerir un esfuerzo computacional mucho menor que los métodos numérico-físicos. Esta es una de las razones por la que hemos decidido explorar el uso de técnicas de SC en la energía producida por el oleaje. La otra se encuentra en el hecho de que su intermitencia puede afectar a la forma en la que se integra la electricidad que genera con la red eléctrica. Estas dos son las razones que nos han impulsado a explorar la viabilidad de nuevos enfoques de SC en dos líneas de investigación novedosas. La primera de ellas es un nuevo enfoque que combina un algoritmo genético (GA: Genetic Algorithm) con una Extreme Learning Machine (ELM) aplicado a un problema de reconstrucción de la altura de ola significativa (en un boya donde los datos se han perdido, por ejemplo, por una tormenta) utilizando datos de otras boyas cercanas. Nuestro algoritmo GA-ELM es capaz de seleccionar un conjunto reducido de parámetros del oleaje que maximizan la reconstrucción de la altura de ola significativa en la boya cuyos datos se han perdido utilizando datos de boyas vecinas. El método y los resultados de esta investigación han sido publicados en: Alexandre, E., Cuadra, L., Nieto-Borge, J. C., Candil-García, G., Del Pino, M., & Salcedo-Sanz, S. (2015). A hybrid genetic algorithm—extreme learning machine approach for accurate significant wave height reconstruction. Ocean Modelling, 92, 115-123. La segunda contribución combina conceptos de SC, Smart Grids (SG) y redes complejas (CNs: Complex Networks). Está motivada por dos aspectos importantes, mutuamente interrelacionados: 1) la forma en la que los conversores WECs (wave energy converters) se interconectan eléctricamente para formar un parque, y 2) cómo conectar éste con la red eléctrica en la costa. Ambos están relacionados con el carácter aleatorio e intermitente de la energía eléctrica producida por las olas. Para poder integrarla mejor sin afectar a la estabilidad de la red se debería recurrir al concepto Smart Wave Farm (SWF). Al igual que una SG, una SWF utiliza sensores y algoritmos para predecir el olaje y controlar la producción y/o almacenamiento de la electricidad producida y cómo se inyecta ésta en la red. En nuestro enfoque, una SWF y su conexión con la red eléctrica se puede ver como una SG que, a su vez, se puede modelar como una red compleja. Con este planteamiento, que se puede generalizar a cualquier red formada por generadores renovables y nodos que consumen y/o almacenan energía, hemos propuesto un algoritmo evolutivo que optimiza la robustez de dicha SG modelada como una red compleja ante fallos aleatorios o condiciones anormales de funcionamiento. El modelo y los resultados han sido publicados en: Cuadra, L., Pino, M. D., Nieto-Borge, J. C., & Salcedo-Sanz, S. (2017). Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms. Energies, 10(8), 1097

Robust control techniques for DFIG driven WECS with improved efficiency

Wind energy has emerged as one of the fastest growing renewable energy sources since mid-80‘s due to its low cost and environmentally friendly nature compared to conventional fossil fuel based power generation. Current technologies for the design and implementation of wind energy conversion systems (WECSs) include induction generator and synchronous generator based units. The doubly fed induction generator (DFIG) is chosen in this thesis because of its economic operation, ability to regulate in sub-synchronous or super-synchronous speed and decoupled control of active and reactive powers. Among the major challenges of wind energy conversion system, extraction of maximum power from intermittent generation and supervision on nonlinear system dynamics of DFIG-WECS are of critical importance. Maximization of the power produced by wind turbine is possible by optimizing tip-speed ratio (TSR), turbine rotor speed or torque and blade angle. The literature reports that a vast number of investigations have been conducted on the maximum power point tracking (MPPT) of wind turbines. Among the reported MPPT control algorithms, the hill climb search (HCS) method is typically preferred because of its simple implementation and turbine parameter-independent scheme. Since the conventional HCS algorithm has few drawbacks such as power fluctuation and speed-efficiency trade-off, a new adaptive step size based HCS controller is developed in this thesis to mitigate its deficiencies by incorporating wind speed measurement in the controller. In addition, a common practice of using linear state-feedback controllers is prevalent in speed and current control of DFIG-based WECS. Traditional feedback linearization controllers are sensitive to system parameter variations and disturbances on grid-connected WECS, which demands advanced control techniques for stable and efficient performance considering the nonlinear system dynamics. An adaptive backstepping based nonlinear control (ABNC) scheme with iron-loss minimization algorithm for RSC control of DFIG is developed in this research work to obtain improved dynamic performance and reduced power loss. The performance of the proposed controller is tested and compared with the benchmark tuned proportional-integral (PI) controller under different operating conditions including variable wind speed, grid voltage disturbance and parameter uncertainties. Test results demonstrate that the proposed method exhibits excellent performance on the rotor side and grid side converter control. In addition, the compliance with the modern grid-code requirements is achieved by featuring a novel controller with disturbance rejection mechanism. In order to reduce the dependency on system‘s mathematical model, a low computational adaptive network fuzzy interference system (ANFIS) based neuro-fuzzy logic controller (NFC) scheme is developed for DFIG based WECS. The performance of the proposed NFC based DFIG-WECS is tested in simulation to regulate both grid and rotor side converters under normal and voltage dip conditions. Furthermore, a new optimization technique known as grey wolf optimization (GWO) is also designed to regulate the battery power for DFIG driven wind energy system operating in standalone mode. In order to verify the effectiveness of the proposed control schemes, simulation models are designed using Matlab/Simulink. The proposed model for MPPT and nonlinear control of grid-connected mode and GWO based power control of standalone DFIG-WECS has been successfully implemented in the real-time environment using DSP controller board DS1104 for a laboratory 480 VA DFIG. The comparison among different controllers suggests that each control technique has its own specialty in wind power control application with specific merits and shortcomings. However, the PI controller provides fast convergence, the ANFIS based NFC controller has better adaptability under grid disturbances and ABNC has moderate performance. Overall, the thesis provides a detailed overview of different robust control techniques for DFIG driven WECS in grid-connected and standalone operation mode with practical implementation

Machine Learning and Data Mining Applications in Power Systems

This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis. Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision-making and control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data-mining statistical signal detection, and estimation may help to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information, and to transform information into actionable intelligence. This Special Issue includes fifteen articles, authored by international research teams from several countries

The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks

This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Wind Farm

During the last two decades, increase in electricity demand and environmental concern resulted in fast growth of power production from renewable sources. Wind power is one of the most efficient alternatives. Due to rapid development of wind turbine technology and increasing size of wind farms, wind power plays a significant part in the power production in some countries. However, fundamental differences exist between conventional thermal, hydro, and nuclear generation and wind power, such as different generation systems and the difficulty in controlling the primary movement of a wind turbine, due to the wind and its random fluctuations. These differences are reflected in the specific interaction of wind turbines with the power system. This book addresses a wide variety of issues regarding the integration of wind farms in power systems. The book contains 14 chapters divided into three parts. The first part outlines aspects related to the impact of the wind power generation on the electric system. In the second part, alternatives to mitigate problems of the wind farm integration are presented. Finally, the third part covers issues of modeling and simulation of wind power system