Big data analytics in smart grids: a review

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A Statistical Evaluation of Risk Priority Numbers in Failure Modes and Effects Analysis Applied to the Prediction of Complex Systems

Complex systems such as military aircraft and naval ships are difficult to cost effectively maintain. Frequently, large-scale maintenance of complex systems (i.e., a naval vessel) is based on the reduction of the system to its base subcomponents and the use of manufacturer-suggested, time-directed, preventative maintenance, which is augmented during the systems lifecycle with predictive maintenance which assesses the system\u27s ability to perform its mission objectives. While preventative maintenance under certain conditions can increase reliability, preventative maintenance systems are often costly, increase down time, and allow for maintenance-induced failures, which may decrease the reliability of the system (Ebeling, 1997). This maintenance scheme ignores the complexity of the system it tries to maintain. By combining the base components or subsystems into a larger system, and introducing human interaction with the system, the complexity of the system creates a unique entity that cannot be completely understood by basing predictability of the system to perform tasks on the reduction of the system to its subcomponents. This study adds to the scholarly literature by developing a model, based on the traditional failure modes and effects analysis commonly used for research and development projects, to capture the effects of the human interaction with the system. Based on the ability of personnel assigned to operate and maintain the system, the severity of the system failure on the impact on the metasystems ability to perform its mission and the likelihood of the event of the failure to occur. Findings of the research indicate that the human interaction with the system, in as far as the ability of the personnel to repair and maintain the system, is a vital component in the ability to predict likelihood of the system failure and the prioritization of the risk of system failure, may be adequately captured for analysis through use of expert opinion elicitation. The use of the expert\u27s opinions may provide additional robustness to the modeling and analysis of system behavior in the event that failure occurs

AI-driven Maintenance Support for Downhole Tools and Electronics Operated in Dynamic Drilling Environments

Downhole tools are complex electro-mechanical systems that perform critical functions in drilling operations. The electronics within these systems provide vital support, such as control, navigation and front-end data analysis from sensors. Due to the extremely challenging operating conditions, namely high pressure, temperature and vibrational forces, electronics can be subjected to complex failure modes and incur operational downtime. A novel Artificial Intelligence (AI)-driven Condition Based Maintenance (CBM) support system is presented, combining Bottom Hole Assembly (BHA) data with Big Data Analytics (BDA). The key objective of this system is to reduce maintenance costs along with an overall improvement of fleet reliability. As evidenced within the literature review, the application of AI methods to downhole tool maintenance is underrepresented in terms of oil and gas application. We review the BHA electronics failure modes and propose a methodology for BHA-Printed Component Board Assemblies (PCBA) CBM. We compare the results of a Random Forest Classifier (RFC) and a XGBoost Classifier trained on BHA electronics memory data cumulated during 208 missions over a 6 months period, achieving an accuracy of 90 % for predicting PCBA failure. These results are extended into a commercial analysis examining various scenarios of infield failure costs and fleet reliability levels. The findings of this paper demonstrate the value of the BHA-PCBA CBM framework by providing accurate prognosis of operational equipment health leading to reduced costs, minimised Non-Productive Time (NPT) and increased operational reliability

Data Challenges and Data Analytics Solutions for Power Systems

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Are electric vehicle batteries being underused? A review of current practices and sources of circularity

The increasing demand for Lithium-ion batteries for Electric Vehicle calls for the adoption of sustainable practices and a switch towards a circular economy-based system to ensure that the electrification of transportation does not come at a high environmental cost. While driving patterns have not changed much over the years, the current Electric Vehicle market is evolving towards models with higher battery capacities. In addition, these batteries are considered to reach the End of Life at 70–80% State of Health, regardless of their capacity and application requirements. These issues may cause an underuse of the batteries and, therefore, hinder the sustainability of the Electric Vehicle. The goal of this study is to review and compare the circular processes available around Electric Vehicle batteries. The review highlights the importance of prioritizing the first-life of the battery onboard, starting with reducing the nominal capacity of the models. In cases where the battery is in risk of reaching the End of Life with additional value, Vehicle to Grid is encouraged over the deployment of second-life applications, which are being strongly promoted through institutional fundings in Europe. As a result of the identified research gaps, the methodological framework for the estimation of a functional End of Life is proposed, which constitutes a valuable tool for sustainable decision-making and allows to identify a more accurate End of Life, rather than considering the fixed threshold assumed in the literature.This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 963580. This funding includes funds to support research work and openaccess publications.Peer ReviewedPostprint (published version

A review on data-centric decision tools for offshore wind operation and maintenance activities: challenges and opportunities

This paper reviews state-of-the-art numerical tools for the operation and maintenance (O&M) of offshore wind farms, focusing on decision support models for maintenance scheduling and the consideration of human and environmental uncertainty. In this review, various factors that can influence the successful conduct of maintenance operations will be examined and special attention will be paid to the most significant ones. Data-driven technologies for improved offshore asset management are also examined and the most used data-driven methods for modelling and optimising turbine operation and maintenance are presented. A focus will be placed on the choice of maintenance strategy, which is the basis for the planning of operations and thus the optimisation problem discussed. As offshore maintenance is a complex operation whose efficiency and safety depend on human and environmental factors, special attention will be paid to the planning strategy that minimises the risks involved while maximising efficiency by considering these factors. The choice of planning technique for turbine maintenance and better consideration of uncertainties are crucial areas of improvement as they can lead to better overall efficiency, higher profit margins, better safety, and improved sustainability of offshore wind farms. The paper covers the application of digital technologies for offshore wind O&M planning and the associated challenges. The paper also highlights the various environmental and human factors to be considered for the operation and maintenance of wind turbines

Data Science-Based Full-Lifespan Management of Lithium-Ion Battery

This open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers

Resilience, Reliability, and Recoverability (3Rs)

Recent natural and human-made disasters, mortgage derivatives crises, and the need for stable systems in different areas have renewed interest in the concept of resilience, especially as it relates to complex industrial systems with mechanical failures. This concept in the engineering systems (infrastructure) domain could be interpreted as the probability that system conditions exceed an irrevocable tipping point. But the probability in this subject covers the different areas that different approaches and indicators can evaluate. In this context, reliability engineering is used the reliability (uptime) and recoverability (downtime) indicators (or performance indicators) as the most useful probabilistic tools for performance measurement. Therefore, our research penalty area is the resilience concept in combination with reliability and recoverability. It must be said that the resilience evaluators must be considering a diversity of knowledge sources. In this thesis, the literature review points to several important implications for understanding and applying resilience in the engineering area and The Arctic condition. Indeed, we try to understand the application and interaction of different performance-based resilience concepts. In this way, a collection of the most popular performance-based resilience analysis methods with an engineering perspective is added as a state-of-the-art review. The performance indicators studies reveal that operational conditions significantly affect the components, industry activities, and infrastructures performance in various ways. These influential factors (or heterogeneity) can broadly be studied into two groups: observable and unobservable risk factors in probability analysis of system performance. The covariate-based models (regression), such as proportional hazard models (PHM), and their extent are the most popular methods for quantifying observable and unobservable risk factors. The report is organized as follows: After a brief introduction of resilience, chapters 2,3 priorly provide a comprehensive statistical overview of the reliability and recoverability domain research by using large scientific databases such as Scopus and Web of Science. As the first subsection, a detailed review of publications in the reliability and recoverability assessment of the engineering systems in recent years (since 2015) is provided. The second subsection of these chapters focuses on research done in the Arctic region. The last subsection presents covariate-based reliability and recoverability models. Finally, in chapter 4, the first part presents the concept and definitions of resilience. The literature reviews four main perspectives: resilience in engineering systems, resilience in the Arctic area, the integration of “Resilience, Reliability, and Recoverability (3Rs)”, and performance-based resilience models