168 research outputs found

    Nonlinear Modeling of Power Electronics-based Power Systems for Control Design and Harmonic Studies

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    The massive integration of power electronics devices in the modern electric grid marked a turning point in the concept of stability, power quality and control in power systems. The evolution of the grid toward a converter-dominated network motivates a deep renovation of the classical power system theory developed for machine-dominated networks. The high degree of controllability of power electronics converters, furthermore, paves the way to the investigation of advanced control strategies to enhance the grid stability, resiliency and sustainability. This doctoral dissertation explores four cardinal topics in the field of power electronics-based power systems: dynamic modeling, stability analysis, converters control, and power quality with particular focus on harmonic distortion. In all four research areas, a particular attention is given to the implications of the nonlinearity of the converter models on the power system

    Emerging Power Electronics Technologies for Sustainable Energy Conversion

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    This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches

    Power Semiconductors for An Energy-Wise Society

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    This IEC White Paper establishes the critical role that power semiconductors play in transitioning to an energy wise society. It takes an in-depth look at expected trends and opportunities, as well as the challenges surrounding the power semiconductors industry. Among the significant challenges mentioned is the need for change in industry practices when transitioning from linear to circular economies and the shortage of skilled personnel required for power semiconductor development. The white paper also stresses the need for strategic actions at the policy-making level to address these concerns and calls for stronger government commitment, policies and funding to advance power semiconductor technologies and integration. It further highlights the pivotal role of standards in removing technical risks, increasing product quality and enabling faster market acceptance. Besides noting benefits of existing standards in accelerating market growth, the paper also identifies the current standardization gaps. The white paper emphasizes the importance of ensuring a robust supply chain for power semiconductors to prevent supply-chain disruptions like those seen during the COVID-19 pandemic, which can have widespread economic impacts.The white paper highlights the importance of inspiring young professionals to take an interest in power semiconductors and power electronics, highlighting the potential to make a positive impact on the world through these technologies.The white paper concludes with recommendations for policymakers, regulators, industry and other IEC stakeholders for collaborative structures and accelerating the development and adoption of standards

    Artificial Intelligence-Based Methods for Power System Security Assessment with Limited Dataset

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    This thesis concerns the relationship between the load, load model, and power system stability. It investigates the possibility of developing a dynamic load model to represent the power system load characteristic during system faults when the power system operates at a high percentage of the power generation from wind farms, solar power, and vehicle-to-grid technology. Additionally, with artificial intelligence supporting the seamless integration of an increasingly distributed and multi-directional power system to unlock the vast potential of renewables, new approaches are proposed to improve the training performance for the applications of artificial neural networks in non-intrusive load monitoring and dynamic security assessment. An improved hybrid load model is proposed to represent the load characteristics in the above power system operation. Genetic algorithms and the multi-curve identification method are applied to determine the parameters of the load model, aiming to minimize the error between the estimated and measured values. The results indicate that the proposed hybrid load model has a reasonably low fitting error to represent the load dynamics. In addition, new approaches are proposed to tackle the challenges posed by limited data when training artificial neural networks (ANNs) for their application in power systems. The knowledge transfer approach is utilized to support the ANN training to generate synthetic data for non-intrusive load monitoring. The results indicate that this approach improves the issue of mode collapse and reduces the need for lengthy training iterations, making the ANN effective for generating synthetic data from limited data. Moreover, the knowledge transfer approach also supports ANN training with limited data for dynamic security assessment. Kernel principal component analysis is employed to eliminate the dimensionality reduction step. The results indicate an improvement in the training performance

    Management of Distributed Energy Storage Systems for Provisioning of Power Network Services

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    Because of environmentally friendly reasons and advanced technological development, a significant number of renewable energy sources (RESs) have been integrated into existing power networks. The increase in penetration and the uneven allocation of the RESs and load demands can lead to power quality issues and system instability in the power networks. Moreover, high penetration of the RESs can also cause low inertia due to a lack of rotational machines, leading to frequency instability. Consequently, the resilience, stability, and power quality of the power networks become exacerbated. This thesis proposes and develops new strategies for energy storage (ES) systems distributed in power networks for compensating for unbalanced active powers and supply-demand mismatches and improving power quality while taking the constraints of the ES into consideration. The thesis is mainly divided into two parts. In the first part, unbalanced active powers and supply-demand mismatch, caused by uneven allocation and distribution of rooftop PV units and load demands, are compensated by employing the distributed ES systems using novel frameworks based on distributed control systems and deep reinforcement learning approaches. There have been limited studies using distributed battery ES systems to mitigate the unbalanced active powers in three-phase four-wire and grounded power networks. Distributed control strategies are proposed to compensate for the unbalanced conditions. To group households in the same phase into the same cluster, algorithms based on feature states and labelled phase data are applied. Within each cluster, distributed dynamic active power balancing strategies are developed to control phase active powers to be close to the reference average phase power. Thus, phase active powers become balanced. To alleviate the supply-demand mismatch caused by high PV generation, a distributed active power control system is developed. The strategy consists of supply-demand mismatch and battery SoC balancing. Control parameters are designed by considering Hurwitz matrices and Lyapunov theory. The distributed ES systems can minimise the total mismatch of power generation and consumption so that reverse power flowing back to the main is decreased. Thus, voltage rise and voltage fluctuation are reduced. Furthermore, as a model-free approach, new frameworks based on Markov decision processes and Markov games are developed to compensate for unbalanced active powers. The frameworks require only proper design of states, action and reward functions, training, and testing with real data of PV generations and load demands. Dynamic models and control parameter designs are no longer required. The developed frameworks are then solved using the DDPG and MADDPG algorithms. In the second part, the distributed ES systems are employed to improve frequency, inertia, voltage, and active power allocation in both islanded AC and DC microgrids by novel decentralized control strategies. In an islanded DC datacentre microgrid, a novel decentralized control of heterogeneous ES systems is proposed. High- and low frequency components of datacentre loads are shared by ultracapacitors and batteries using virtual capacitive and virtual resistance droop controllers, respectively. A decentralized SoC balancing control is proposed to balance battery SoCs to a common value. The stability model ensures the ES devices operate within predefined limits. In an isolated AC microgrid, decentralized frequency control of distributed battery ES systems is proposed. The strategy includes adaptive frequency droop control based on current battery SoCs, virtual inertia control to improve frequency nadir and frequency restoration control to restore system frequency to its nominal value without being dependent on communication infrastructure. A small-signal model of the proposed strategy is developed for calculating control parameters. The proposed strategies in this thesis are verified using MATLAB/Simulink with Reinforcement Learning and Deep Learning Toolboxes and RTDS Technologies' real-time digital simulator with accurate power networks, switching levels of power electronic converters, and a nonlinear battery model

    Emerging Power Electronics Technologies for Sustainable Energy Conversion

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    This Special Issue summarizes, in a single reference, timely emerging topics related to power electronics for sustainable energy conversion. Furthermore, at the same time, it provides the reader with valuable information related to open research opportunity niches

    Brain Computations and Connectivity [2nd edition]

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    This is an open access title available under the terms of a CC BY-NC-ND 4.0 International licence. It is free to read on the Oxford Academic platform and offered as a free PDF download from OUP and selected open access locations. Brain Computations and Connectivity is about how the brain works. In order to understand this, it is essential to know what is computed by different brain systems; and how the computations are performed. The aim of this book is to elucidate what is computed in different brain systems; and to describe current biologically plausible computational approaches and models of how each of these brain systems computes. Understanding the brain in this way has enormous potential for understanding ourselves better in health and in disease. Potential applications of this understanding are to the treatment of the brain in disease; and to artificial intelligence which will benefit from knowledge of how the brain performs many of its extraordinarily impressive functions. This book is pioneering in taking this approach to brain function: to consider what is computed by many of our brain systems; and how it is computed, and updates by much new evidence including the connectivity of the human brain the earlier book: Rolls (2021) Brain Computations: What and How, Oxford University Press. Brain Computations and Connectivity will be of interest to all scientists interested in brain function and how the brain works, whether they are from neuroscience, or from medical sciences including neurology and psychiatry, or from the area of computational science including machine learning and artificial intelligence, or from areas such as theoretical physics

    Nonlinear Systems

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    Open Mathematics is a challenging notion for theoretical modeling, technical analysis, and numerical simulation in physics and mathematics, as well as in many other fields, as highly correlated nonlinear phenomena, evolving over a large range of time scales and length scales, control the underlying systems and processes in their spatiotemporal evolution. Indeed, available data, be they physical, biological, or financial, and technologically complex systems and stochastic systems, such as mechanical or electronic devices, can be managed from the same conceptual approach, both analytically and through computer simulation, using effective nonlinear dynamics methods. The aim of this Special Issue is to highlight papers that show the dynamics, control, optimization and applications of nonlinear systems. This has recently become an increasingly popular subject, with impressive growth concerning applications in engineering, economics, biology, and medicine, and can be considered a veritable contribution to the literature. Original papers relating to the objective presented above are especially welcome subjects. Potential topics include, but are not limited to: Stability analysis of discrete and continuous dynamical systems; Nonlinear dynamics in biological complex systems; Stability and stabilization of stochastic systems; Mathematical models in statistics and probability; Synchronization of oscillators and chaotic systems; Optimization methods of complex systems; Reliability modeling and system optimization; Computation and control over networked systems
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