Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Abstracts:Presents the recipients of IEEE Industry Applications Society (IAS) awards for 2025.

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Abstracts:Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Abstracts:One of the most critical aspects of power generation and distribution for an offshore production facility is reliability. Safety for those onboard the vessel and the ability to deliver maximum production are dependent on the availability of electrical power. Due to its critical nature, keeping power available should not be prone to delays or human errors, and thus requires an effective Power Management System (PMS) that is automated to control electrical equipment during real-time unexpected events. Power automation should be implemented in the PMS to maximize stability and availability, as well as to avoid any mis-operations or over-operations. Examples of this automation are fast power-based load shedding, automatic transformer and tie selection, automated restoration of the power system, and automated backup power transfer schemes. It is important to recognize that critical facility operations should only be enhanced by an integrated PMS and should not be dependent on such equipment. To this point, the Power System should still include the necessary hardwired interlocks to ensure the safety of personnel. Another important point regarding personnel safety is that the PMS allows for remote switching, using the graphical interface allows for reliable and common sense switching without the need for an individual to stand in front of the energized equipment. This paper will elaborate, using a real-world project, on how a power management system can automate the electrical system of an offshore platform to keep it safe, reliable, and stable.

Abstracts:Solar Photovoltaic (SPV) systems can exhibit multiple local maxima in their current-voltage (I-V) and power-voltage (P-V) characteristics due to partial shading at the panel or string level. The frequent occurrence and prolonged operation of panels under partial shading conditions may lead to a reduction in power output and hot-spot formation on the panels. Early detection and exact identification of these shaded panels can significantly reduce power losses and prevent hot spots. In this regard, this article proposes a novel method that utilizes multi-capacitorbased I-V and P-V techniques to determine the exact location of the shaded panels. The proposed I-V and P-V technique includes an additional hardware module (AHM) that generates the I-V and P-V curves of the SPV string, which are fed to an artificial intelligence (AI) based classification model through the sensor and control unit. The dual-phase classification algorithm is designed with a convolution neural network (CNN) to classify between normal and faulty panels. CNN model sends the relevant information about the faulty panel to the plant controller, which remotely bypasses the panel to prevent any physical damage and losses in the SPV system. The proposed method is realized in real-time by developing an experimental setup with a solar farm and AHM module. The IV and PV curves of the proposed system under different shading scenarios are explored.