Search Results (7)

Power transformers (PTs) play a vital role in the electrical power system. Assessing their health to predict their remaining useful life is essential to optimise maintenance. Scheduling the right maintenance for the right equipment at the right time is the ultimate goal of any power system utility. Optimal maintenance has a number of benefits: human and social, by limiting sudden service interruptions, and economic, due to the direct and indirect costs of unscheduled downtime. PT now produces large amounts of easily accessible data due to the increasing use of IoT, sensors, and connectivity between physical assets. As a result, power transformer prognostics and health management (PT-PHM) methods are increasingly moving towards artificial intelligence (AI) techniques, with several hundreds of scientific papers published on the topic of PT-PHM using AI techniques. On the other hand, the world of AI is undergoing a new evolution towards a third generation of AI models: large-scale foundation models. What is the current state of research in PT-PHM? What are the trends and challenges in AI and where do we need to go for power transformer prognostics and health management? This paper provides a comprehensive review of the state of the art in PT-PHM by analysing more than 200 papers, mostly published in scientific journals. Some elements to guide PT-PHM research are given at the end of the document. Full article

Success in the industrial sector is compromised by diverse conditions such as imperfect product production, manufacturing line interruptions, and unscheduled maintenance. The precise use of common practices in production environments is an available solution to eliminate some of these issues. Applying a warm-up period in a manufacturing process is adequate and cost-effective for almost all companies. It improves the equipment’s productivity and helps the manufacturing line generate fewer defective products. Even though several inventory management studies have included a warm-up phase in their models, its use in economic production quantity (EPQ) models remains largely unexplored. Adding a warm-up phase to the production cycle minimizes maintenance expenses and defective products and increases the machine’s performance. In this study, the dependency between the machine downtime and the warm-up length is examined for the first time. The warm-up time depends on the machine’s off-state period: if the machine has a longer operation timeout, then a longer warm-up period is needed. The model includes a function to model the warm-up time relative to the machine downtime and two types of defective products: scrapping and reworking items. The study is concluded with some numerical examples, a sensitivity analysis, and some management insights related to the EPQ. Full article

The energy supply entities widely adopt distributed generators (DG) to meet the additional power requirement due to scheduled or unscheduled interruptions. The expansion of transmission and distribution systems via the inclusion of loads and generators and the occurrence of line interruptions are significant causes of congestion of transmission lines in interconnected systems. The management and alleviation of congested lines is a primary requirement for a power system network’s reliable and efficient operation. The researchers investigated the potential scope of distributed generation (DG) to alleviate the congested branches in interconnected transmission systems. The development of a reliable scheme to arrive at the best location and size of local generators for alleviating congestion deserves considerable importance. This paper attempted to develop a simple and reliable strategy for the optimum placement and sizing of DGs to be integrated with a transmission line system of DGs for congestion relief in transmission lines by analyzing power flow solutions. This research work considered the 14-bus system of IEEE for the preliminary analysis to identify the parameters employed for assessing the severity of line congestion and the best placement and sizing of DGs for congestion relief. This work analyzed power flows by load flow algorithms using ETAP software in the 14-bus IEEE system for different line outage cases. The analysis of power flow solutions of the 14-bus system of IEEE revealed that the percentage violation of the system can be regarded as an essential parameter to assess the extent of congestion in an interconnected system. A detailed power flow analysis of the system with various capacities of DG integration at several buses in the system revealed the application of two indices, namely the index of severity (SI) and sensitivity factor (SF), for optimum placement with the best capacity of DGs for congestion alleviation in the system. This work proposed a reliable algorithm for the best siting and sizing of DGs for congestion relief by using the identified indices. The proposed methodology is system indices allied load flow-based algorithm. This work produced a fast simulation solution without any mismatch through this developed scheme. The approximations linked with the algorithm were very minute, resulting in comprehensive bests instead of inexact limited bests with less simulation time and more convergence probability and availing the benefits of the mathematical approach. The work investigated the feasibility of the proposed methodology for optimum placing and quantifying DGs for congestion solutions for a practical interconnected bus system in the supply entity of the Kerala grid with many buses. Any transmission system operator can adopt this method in similar connected systems anywhere. The proposed algorithm determined the most severe cases of congestion and the optimum site and size of DGs for managing congested feeders in the grid system. The analysis of the losses in the system for different cases of DG penetration by load flow analysis validated the suitability of the obtained results. Full article

In Brazil, approximately 30% of unscheduled interruptions of energy supply are caused by fires and vegetation interference in the energy distribution networks, resulting in great losses for companies of the electricity sector. To reduce the interruptions caused by these kinds of events, the energy distribution companies continually monitor and manage the vegetation in the vicinity of electric cables. However, due to the great extension and capillarity of the networks, it is not always possible to cover the entire network, and it is necessary to define priority segments to be managed. Taking into the account this context, the main objective of this study was to develop multi-criteria indicators to identify segments of the energy distribution networks with higher priority for management, based on vegetation attributes extracted from remote sensing images. For this purpose, we tested two artificial intelligence algorithms, support vector machine (SVM) and artificial neural networks (ANN), to automatically identify different classes of vegetation using PlanetScope images. Our results showed that the ANN algorithm presented better results for the vegetation classification when compared to the results obtained with the SVM algorithm. The application of the developed indicators showed adherent results, even in densely urbanized areas. We hope that the use of the developed indicators can help Brazilian energy distribution companies in optimizing vegetation management and consequently reducing unscheduled interruptions. Full article

Lock gates are an important part of the transportation infrastructure within the United States (US). Unfortunately, many existing lock gates have reached or exceeded their initial design lives and require frequent repairs to remain in service. Unscheduled repairs often increase as gates age, having a local economic impact on freight transport, which can create economic ripples throughout the nation. Metal fatigue is a key cause of unscheduled service interruptions, degrading lock gate components over time. Additionally, because lock gates are submerged during operation, crack detection prior to component failure can be difficult, and repair costs can be high. This paper presents an analytical and experimental investigation into fatigue damage within common lock gate geometries, as well as fatigue mitigation strategies with a focus on extending gate service lives. Detailed finite element analyses are combined with fatigue and fracture mechanics theories to predict critical fatigue regions within common gate details and develop retrofit strategies for mitigating fatigue cracking. Full-scale experimental fatigue testing of a critical lock gate component is conducted to provide a baseline for the evaluation of retrofit strategies. Retrofit strategies and issues in using carbon fiber reinforced polymer (CFRP) plates having optimized pre-stress levels are discussed. Full article

Doxorubicin and cyclophosphamide (AC)-based chemotherapy has been a standard regimen for early-stage breast cancer (ESBC) with an intermediate risk (10–20%) of febrile neutropenia (FN). Secondary prophylaxis of granulocyte colony-stimulating factor (G-CSF) is considered in patients receiving AC-based chemotherapy; however, relevant studies are limited. Here, we retrospectively reviewed the electronic medical records of 320 patients who completed adjuvant AC-based chemotherapy from September 2016 to September 2020. Approximately 46.6% of the patients developed severe neutropenic events (SNE) during AC-based chemotherapy. Secondary prophylaxis of G-CSF reduced the risk of recurrent SNE (p < 0.01) and the relative dose intensity (RDI) < 85% (p = 0.03) in patients who had experienced SNE during AC-based chemotherapy. Age ≥ 65 years (p = 0.02) and alanine aminotransferase (ALT) or aspartate aminotransferase (AST) > 60 IU/L (p = 0.04) were significant risk factors for RDI < 85%. The incidences of FN, grade 4 neutropenia, unscheduled hospitalization, and interruption to the dosing regimen were reduced in patients administered secondary prophylaxis with G-CSF (before vs. after administration: FN, 19.4% vs. 4.6%; grade 4 neutropenia, 86.1% vs. 14.8%; unscheduled hospitalization, 75.9% vs. 11.1%; interruption to the dosing regimen, 18.5% vs. 8.3%). This study indicated the importance of active intervention of G-CSF use to prevent recurrent SNE and improve clinical outcomes in patients with breast cancer who receive AC-based chemotherapy. Full article

Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme. Full article