Purpose This paper aims to develop a comprehensive model of a magnetic sensor array that will be operational for a multitude of electric components in continuous and nonintrusive condition monitoring (CM) or in readiness assessment (RA) applications. Design/methodology/approach A universal nonintrusive model of a flexible antenna array is introduced to monitor and identify failures in electric machine drives. An adjustable sensor is designed to serve as a RA for a vast range of electrical elements in a typical power system by capturing the low-frequency radiated magnetic fields. Findings The optimal placement of the most sensitive radiated fields from several components has been discovered in this case study, enabling the detection of healthy current flow throughout. Thereafter, the short-circuit investigation, representing faulty situations, is implemented and compared with healthy cases. Practical implications This sensing technique can be used for nonintrusive CM of components that are out of reach and cannot have the sensor to be held around it such as components in offshore winds, wind energy generation and power and chemical plants. Originality/value The results are provided for three commonly used machines with a single sensor array with numerous settings. The three dimensional (3 D) finite element analysis is applied in the structuring of the sensor, detection of the optimum location and recognition of faults in the machines. Finally, based on the setup design, 3 D printing is used for the construction of the sensor array. Thus, the sensor array with fault detection avoids major component failures and increases system reliability/resiliency.

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

The transportation industry is rapidly transitioning from Internal Combustion Engine (ICE) based vehicles to Electric Vehicles (EVs) to promote clean energy. However, large-scale adoption of EVs can compromise the reliability of the power grids by introducing large uncertainty in the demand. Demand response with a controlled charge scheduling strategy for EVs can mitigate such issues. In this paper, a deep reinforcement learning- based charge scheduling strategy is developed for individual EVs by considering user’s dynamic driving behavior and charging preferences. The temporal dynamics of user’s anxiety about charging the EV battery is rigorously addressed. A dynamic weight allocation technique is applied to continuously tune user’s priority for charging and cost-saving with respect to charging duration. The sequential charging control problem is formulated as a Markov decision process, and an episodic approach to the deep deterministic policy gradient (DDPG) algorithm with target policy smoothing and delayed policy update techniques is applied to develop the optimal charge scheduling strategy. A real-world dataset that captures user’s driving behavior, such as arrival time, departure time, and charging duration, is utilized in this study. The extensive simulation results reveal the effectiveness of the proposed algorithm in minimizing energy cost while satisfying user’s charging requirements.