Optimal control of wind energy conversion systems with doubly-fed induction generators

Wind energy conversion systems (WECSs) have become the interesting topic over recent years for the renewable electrical power source. They are a more environmentally friendly and sustainable resource in comparison with the fossil energy resource. The WECS using a doubly-fed induction generator (DFIG) to convert mechanical power into electrical power has a significant advantage. This WECS requires a smaller power converter in comparison with a squirrel cage induction generator. Efficiency of the DFIG-WECS can be improved by a suitable control system to maximise the output power from WECS. A maximum power point tracking (MPPT) controller such as tip-speed ratio (TSR)control and power signal feedback (PSF) control is use to maximise mechanical power from wind turbine and a model-based loss minimisation control (MBLC) is used to minimise electrical losses of the generator. However, MPPT and MBLC require the parameters of the wind turbine and the generator for generating the control laws like optimal generator speed reference and d-axis rotor current reference. The Efficiencies of the MPPT and MBLC algorithms deteriorate when wind turbine and generator parameters change from prior knowledge. The field oriented control for a DFIG in the WECS is extended by introducing a novel control layer generating online optimal generator speed reference and d-axis rotor current reference in order to maximise power produced from the WECS under wind turbine and DFIG parameter uncertainties, which is proposed. The single input rule modules (SIRMs) connected fuzzy inference model is applied to the control algorithm for optimal power control for variable-speed fixed-pitch wind turbine in the whole wind speed range by generating an online optimal speed reference to achieve optimal power under wind turbine parameter uncertainties. The proposed control combines a hybrid maximum power point tracking (MPPT) controller, a constant rotational speed controller for below-rated wind speed and a limited-power active stall regulation by rotational speed control for above-rated wind speed. The three methods are appropriately organised via the fuzzy controller based SIRMs connected fuzzy inference model to smooth transition control among the three methods. The online parameter estimation by using Kalman filter is applied to enhance model-based loss minimisation control (MBLC). The d-axis rotor current reference of the proposed MBLC can adapt to the accurate determination of the condition of minimum electrical losses of the DFIG when the parameters of the DFIG are uncertain. The proposed control algorithm has been verified by numerical simulations in Matlab/Simulink and it has been demonstrated that the energy generated for typical wind speed profiles is greater than that of a traditional control algorithm based on PSF MPPT and MBLC

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Increasing the Confidence of In Silico Modelling in Toxicology

Consideration of all chemicals that we are exposed to on a daily basis is a daunting task, which has been traditionally assessed through animal testing procedures. However, the ethical and financial considerations associated with such testing has long been a topic of concern, with the desire to pursue alternative methods evident. Towards this, the vision of 21st century toxicology actively promoted the use of new approach methodologies (NAMs) that avoid the usage of animal testing, as well as fostering a more efficient means for toxicological assessment. Captured within these NAMs are in silico methods which include a range of in silico (or computational) approaches, one of the most popular being Quantitative Structure- Activity Relationships (QSARs). Although it is acknowledged that the majority of these in silico methods are by no means novel, it is the consideration of such within regulatory decisionmaking frameworks that is. Whilst these methods are being promoted for usage within regulatory settings, fundamental issues regarding assessment of confidence as well as knowledge sharing need to be addressed to further promote acceptance. Therefore, the aim of this thesis was to provide detailed analysis of methods for in silico model validation, and knowledge-sharing efforts that incorporate the state-of-the-art practices, which could potentially bolster their acceptance within regulatory settings. Recently developed uncertainty assessment criteria for the evaluation of QSARs were analysed with a particular focus on how they can be employed to demonstrate fitness-for-purpose. These uncertainty assessment criteria were subsequently developed further, with considerations of challenges in QSAR, such as mixture assessment and machine learning (ML) approaches. To facilitate this, a review was conducted of the key characteristics of QSAR methods applied to mixtures, using the knowledge gathered to identify areas for additional consideration within the criteria. ML approaches were studied, with six models developed to address ML-specific considerations within the criteria. The concept of model sharing has been promoted through the application of the FAIR (Findable, Accessible, Interoperable, Reusable) principles to in silico methods. Outcomes from each chapter and the overall thesis promote the advancement of regulatory acceptance of QSAR models and predictions, through development of improved reporting strategies and sharing methodologies. The thesis additionally benefits the field thorough considerations of the most challenging aspects of QSARs, and how these subfields, such as mixture assessment and ML approaches, can gain credibility