Unmanned Aerial Vehicles (UAVs) offer a wide range of interesting military and commercial applications at reduced costs. A couple of their more interesting characteristics include: They are capable of hovering and flying at high and low speeds, and they can move in any direction and take off and land vertically. A very promising class of these vehicles comprises small vehicles that can be flown either indoor or outdoor and offer the possibility for near-area surveillance, crop dusting, precision farming [1], microwave autonomous copter systems for geological remote sensing as shown in [2], geographic studies, fire monitoring, security applications, search- and-rescue, and attack and rendezvous [3], [4]. However, challenging requirements in terms of size, weight, and maneuverability for these systems are presented in [5]. A type of aircraft with an important and significant potential is the so-called Quadrotor that is considered by most research studies as a promising UAV [6].

The installation of wind energy increased in the last twenty years, as its cost decreased, and it contributes to reducing GHG emissions. A race toward gigantism characterizes wind turbine development, primarily driven by offshore projects. The larger wind turbines are facing higher loads, and the imperatives of mass reduction make them more flexible. Size increase of wind turbines results in higher structural vibrations that reduce the lifetime of the components (blades, main shaft, bearings, generator, gearbox, etc.) and might lead to failure or destruction. This paper aims to present in detail the problems associated with wind turbine vibration and a thorough literature review of the different mitigation solutions. We explore the advantages, drawbacks, and challenges of the existing vibration control systems for wind turbines. These systems belong to six main categories, according to the physical principles used and how they operate to mitigate the vibrations. This paper offers a multi-criteria analysis of a vast number of systems in different phases of development, going from full-scale testing to prototype stage, experiments, research, and ideas.

Vibration is one of the most dangerous phenomena that happens to a structure. It leads to premature fatigue and eventually failure, with potentially fatal consequences. A smart structure is an excellent solution to this problem; it adds an actuator, a sensor, and an appropriate control law to the system to reduce/eliminate the vibration. This study developed a complete analytical model for a cantilever beam with a collocated PZT sensor/actuator pair. First, we used a coupling of a collocated PZT sensor and an actuator to measure and control vibration levels based on a PID control law considering the physical constraints associated with PZT operation as the voltage level of the actuator. Next, the damping coefficient of the structure was determined by using genetic algorithms best fit to satisfy specific vibration conditions. Finally, we conducted a complete optimization for sensor/actuator position and PID parameters, using genetic algorithms. Thus, this paper gives a thorough understanding of the potential vibration control of the cantilever beam.

High-voltage disconnect switches (HVDSs) are crucial for ensuring visible electrical isolation but often operate under challenging environmental conditions. While current monitoring methods can detect faults, simply identifying them is not enough for effective maintenance planning or efficient resource management. Electric utilities need tools or platforms that enable them to identify various failure modes and their impact on the safety and reliability of the system. This paper presents a failure mode and effects analysis of a HVDS and an assessment of its criticality (FMECA). For each failure mode identified by the FMECA, a cause-and-effect analysis is used to further investigate their root causes. Next, the criticality of each failure mode is evaluated using the Risk Priority Number (RPN). Finally, each failure mode is subjected to an analysis to prioritize the sensor used to measure the physical quantity associated with the failure. This sensor prioritization is performed by considering not only the RPN but also the sensor’s utility in terms of failure detection efficiency and ease of installation. The results provide a robust foundation for the development of predictive maintenance strategies customized for HVDS equipment, thereby contributing to enhanced reliability and resilience of electrical power systems.