Wind Turbine Active Fault Tolerant Control Based on Backstepping Active Disturbance Rejection Control and a Neurofuzzy Detector

© 2023 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Wind energy conversion systems have become an important part of renewable energy history due to their accessibility and cost-effectiveness. Offshore wind farms are seen as the future of wind energy, but they can be very expensive to maintain if faults occur. To achieve a reliable and consistent performance, modern wind turbines require advanced fault detection and diagnosis methods. The current research introduces a proposed active fault-tolerant control (AFTC) system that uses backstepping active disturbance rejection theory (BADRC) and an adaptive neurofuzzy system (ANFIS) detector in combination with principal component analysis (PCA) to compensate for system disturbances and maintain performance even when a generator actuator fault occurs. The simulation outcomes demonstrate that the suggested method successfully addresses the actuator generator torque failure problem by isolating the faulty actuator, providing a reliable and robust solution to prevent further damage. The neurofuzzy detector demonstrates outstanding performance in detecting false data in torque, achieving a precision of 90.20% for real data and 100%, for false data. With a recall of 100%, no false negatives were observed. The overall accuracy of 95.10% highlights the detector’s ability to reliably classify data as true or false. These findings underscore the robustness of the detector in detecting false data, ensuring the accuracy and reliability of the application presented. Overall, the study concludes that BADRC and ANFIS detection and isolation can improve the reliability of offshore wind farms and address the issue of actuator generator torque failure.Peer reviewe

Stochastic unit commitment in microgrids based on model predictive control

This article deals with the problem of Stochastic Unit Commitment (SUC), considering the stochastic nature of demand and meteorological phenomena. This paper shows the optimal operation of a hybrid microgrid composed of the following generation units: wind turbine (WT), photovoltaic solar panel (PV), diesel engine generator (DE), micro-turbine (MT), as well as storage devices such as Battery Energy Storage (BES), considering its constraints and the requirements of the reserve generation. For this purpose, a Model-based Predictive Control (MPC), which uses dynamic models of prediction of renewable power and demand in real time, is developed, allowing feedback at each step of time, which corrects the uncertainty of the models. A comparison with a classic UC formulation has been made. The results reach a lower cost solution

Modelling and control of a Concentrating Solar Power Plant prototype

30th Irish Signals and Systems Conference (ISSC) -- JUN 17-18, 2019 -- Maynooth Univ, Maynooth, IRELANDRouzbehi, Kumars/0000-0002-3623-4840; Escano, Juan Manuel/0000-0003-1274-566XWOS: 000535732000025This paper presents a type of solar thermal power plant and also studies the efficiency of the common type of this distributed generation resource. in order to improve the efficiency of the power station, several techniques are studied such as the combined cycle plant, the thermoelectric generator and the intelligent control application. the thermodynamic analysis of the power plant and the performance of the equipment with an intelligent control system are validated by the developed experimental prototype.IEEE, IMEX, Analog Devices, Rohde & Schwarz, IEEE UK & Ireland Signal Proc Chapter, IEEE Computat Intelligence Soc UK & IrelandVI Plan of Research and Transfer of the University of Seville [VI PPIT-US]The authors would like to acknowledge the VI Plan of Research and Transfer of the University of Seville (VI PPIT-US) for funding this work