Control and Nonlinear Dynamics on Energy Conversion Systems

The ever-increasing need for higher efficiency, smaller size, and lower cost make the analysis, understanding, and design of energy conversion systems extremely important, interesting, and even imperative. One of the most neglected features in the study of such systems is the effect of the inherent...

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Bibliographic Details
Main Author: Iu, Herbert Ho-Ching (auth)
Other Authors: El Aroudi, Abdelali (auth)
Format: Electronic Book Chapter
Language:English
Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
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DOAB: description of the publication
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520 |a The ever-increasing need for higher efficiency, smaller size, and lower cost make the analysis, understanding, and design of energy conversion systems extremely important, interesting, and even imperative. One of the most neglected features in the study of such systems is the effect of the inherent nonlinearities on the stability of the system. Due to these nonlinearities, these devices may exhibit undesirable and complex dynamics, which are the focus of many researchers. Even though a lot of research has taken place in this area during the last 20 years, it is still an active research topic for mainstream power engineers. This research has demonstrated that these systems can become unstable with a direct result in increased losses, extra subharmonics, and even uncontrollability/unobservability. The detailed study of these systems can help in the design of smaller, lighter, and less expensive converters that are particularly important in emerging areas of research like electric vehicles, smart grids, renewable energy sources, and others. The aim of this Special Issue is to cover control and nonlinear aspects of instabilities in different energy conversion systems: theoretical, analysis modelling, and practical solutions for such emerging applications. In this Special Issue, we present novel research works in different areas of the control and nonlinear dynamics of energy conversion systems. 
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653 |a explosion-magnetic generator 
653 |a quadratic boost 
653 |a matrix norm 
653 |a coordinated control system 
653 |a permanent magnet synchronous motor (PMSM) 
653 |a photovoltaic (PV) 
653 |a power conversion 
653 |a capacitance current pulse train control 
653 |a air gap eccentricity 
653 |a high step-up voltage gain 
653 |a voltage ripple 
653 |a offset-free 
653 |a goal representation heuristic dynamic programming (GrHDP) 
653 |a current mode control 
653 |a sliding mode observer (SMO) 
653 |a multi-model predictive control 
653 |a combined heat and power unit 
653 |a discontinuous conduction mode (DCM) 
653 |a current-pulse formation 
653 |a sliding mode control 
653 |a single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP) 
653 |a subharmonic oscillations 
653 |a DC micro grid 
653 |a supply air temperature 
653 |a air-handling unit (AHU) 
653 |a vibration characteristics 
653 |a magnetic saturation 
653 |a slope compensation 
653 |a fixed-point inducting control 
653 |a the load of suspension point in the z direction 
653 |a variable switching frequency DC-DC converters 
653 |a droop control 
653 |a Helmholtz number 
653 |a plasma accelerator 
653 |a contraction analysis 
653 |a sliding control 
653 |a bifurcations in control parameter 
653 |a disturbance observer 
653 |a DC motor 
653 |a multiphysics 
653 |a virtual impedance 
653 |a pulverizing system 
653 |a ultrahigh voltage conversion ratio 
653 |a corrugated pipe 
653 |a DC-DC converters 
653 |a maximum power point tracking (MPPT) 
653 |a dynamic model 
653 |a nonlinear dynamics 
653 |a new step-up converter 
653 |a micro-grid 
653 |a global stability 
653 |a extended back electromotive force (EEMF) 
653 |a small-signal model 
653 |a electromagnetic vibration 
653 |a nonlinear dynamic model 
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653 |a data-driven 
653 |a rigid body rotation 
653 |a position sensorless 
653 |a prediction 
653 |a centralized vs. decentralized control 
653 |a inferential control 
653 |a boost-flyback converter 
653 |a calculation method 
653 |a switched reluctance generator 
653 |a monodromy matrix 
653 |a bridgeless converter 
653 |a decoupling control 
653 |a distributed architecture 
653 |a wave 
653 |a buck converter 
653 |a soft sensor 
653 |a model-plant mismatches 
653 |a whistling noise 
653 |a efficiency optimization 
653 |a steel catenary riser 
653 |a moving horizon estimation 
653 |a single artificial neuron (SAN) 
653 |a space mechanism 
653 |a two-stage bypass 
653 |a electrical machine 
653 |a harmonic suppression 
653 |a local vs. global optimization 
653 |a performance recovery 
653 |a reinforcement learning (RL) 
653 |a adaptive dynamic programming (ADP) 
653 |a overvoltage 
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653 |a maximum power point tracking 
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653 |a power quality 
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653 |a feedback coefficient 
653 |a power factor correction (PFC) 
653 |a capacitance current 
653 |a predictive control 
653 |a rotor dynamics 
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