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Modern wind farms promise increased capacity, sustainability, and efficiency through the integration of information technology with the existing wind energy technologies. However, this integration creates a new category of cyber-physical vulnerabilities stemming from physical faults and/or cyberattacks potentially leading to devastating physical impacts and catastrophic consequences. The large scale and high complexity of a wind farm, in addition to its growing connectivity, control functionality, and wind intermittency and variability make the task of cyber-physical protection particularly challenging. This paper introduces novel approaches for guaranteeing the safety, security, and reliability of a modern wind farm under simultaneous occurrence of faults and attacks using an advanced cyber-physical health monitoring scheme, defined as “intrusion detection and fault diagnosis system”, as well as fault-tolerant/attack-resilient control strategies at different levels. The proposed fault-tolerant control strategy is based on adaptive model predictive control at turbine level, enhanced with a control reallocation mechanism at farm level. The attack-resilient control strategy is based on an automatic signal correction (ASC) technique that is applied at network operator level. The effectiveness of the suggested approaches is demonstrated using an offshore wind farm model under wind turbulences, measurement noises, and realistic physical fault and cyberattack scenarios.

The next-generation power grid evolves from the development of fundamental cyber–physical energy systems called smart microgrids. In order to improve the reliability, safety, and security of smart microgrids and achieve a more cost-effective operation, innovative approaches for physical fault diagnosis and fault-tolerant control (FTC) as well as intrusion detection and attack-resilient control (ARC) should be investigated. Given that, this article considers a smart hybrid renewable-based microgrid with different types of distributed generation units, including solar photovoltaic (PV) array, wind turbines, and battery energy storage system. Novel active FTC and ARC strategies are designed for pulse-width modulation (PWM) converters at microgrid level. The proposed fault-tolerant controller is based on an optimal fuzzy gain-scheduling technique that is used to accommodate the adverse impacts of PV power-loss faults. Also, the proposed attack-resilient controller relies on the estimated values of sensor measurements during the occurrence of data integrity cyber-attacks. To access and evaluate the microgrid’s real-time health status, both FTC and ARC strategies employ an integrated model-based intrusion detection and fault diagnosis (IDFD) system that is designed using a fuzzy modeling and identification technique. Finally, the effectiveness of the proposed solutions is demonstrated via a series of simulations in MATLAB/Simulink using an advanced microgrid benchmark.

With the rise of wind energy production in global power generation, wind farm facilities are becoming increasingly attractive targets for malicious attacks, in particular those affecting wind farm network operators’ cybersubsystems and functionalities. Given the significance of this problem, this article proposes a novel anomaly-based intrusion detection and diagnosis system to carry out in-line monitoring as with firewalls. Also, an innovative cyberattack-resilient active power control is designed to responsively mitigate the impacts of cyberattacks on the safe regulation of active power from wind farms. An offshore wind farm benchmark is used to implement and demonstrate the effectiveness of the proposed solutions in the presence of wind turbulences, measurement noises and realistic smart cyberattack scenarios.