Elements of maintenance system and tools for implementation within framework of Reliability Centred Maintenance- A review

For plant systems to remain reliable and safe they must be effectively maintained through a sound maintenance management system. The three major elements of maintenance management systems are; risk assessment, maintenance strategy selection and maintenance task interval determination. The implementation of these elements will generally determine the level of plant system safety and reliability. Reliability Centred Maintenance (RCM) is one method that can be used to optimise maintenance management systems. This paper discusses the three major elements of a maintenance system, tools utilised within the framework of RCM for performing these tasks and some of the limitations of the various tools. Each of the three elements of the maintenance management system has been considered in turn. The information will equip maintenance practitioners with basic knowledge of tools for maintenance optimisation and stimulate researchers with respect to developing alternative tools for application to plant systems for improved safety and reliability. The research findings revealed that there is a need for researchers to develop alternative tools within the framework of RCM which are efficient in terms of processing and avoid the limitations of existing methodologies in order to have a safer and more reliable plant system.

Condition-based maintenance—an extensive literature review

This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions

Distributed Energy Resources : An Assessment of New Jersey’s Clean Energy Future

The demand for renewable energy in New Jersey will continue to grow as economic opportunities and community support drive development. The effective integration of Distributed Energy Resources (DERs) will transform energy production, storage, and use. To achieve sustainable energy production, the current reliance on fossil fuels must be reduced and replaced with less carbon-intensive energy sources that optimize the electric grid. DERs help pioneer the path to a clean energy transition where the implementation of new renewable energy projects will diversify New Jersey’s energy portfolio and provide a more resilient, equitable, and independent energy source. This thesis investigates different perspectives of small wind and solar energy options that are supported by state and governmental initiatives in New Jersey and shows a quantitative review to support these programs. The research combines various scopes, resources, and methods to analyze current perspectives involved in the wind and solar industry with capacities under 10 MW (megawatt). The first assessment will consist of analyzing stakeholder values on sustainable community solar placement characteristics consisting of environmental and social-economic factors, and governmental support. The second assessment involves the aggregation of onshore wind turbine life cycle data and costs in combination with various life extension and disposal strategies to verify small-wind as a carbon-friendly and cost-effective energy source. In Chapter 1, we review the current conditions and motivation to transition to a clean energy resource, such as the current reliance on fossil fuels and the associated negative impact on local economies and ecosystems. Additionally, we explain how DERs can play a core role in facilitating energy goals better than large-scale utility projects through providing an opportunity to optimize the electric grid, the ability to account for flexible load demands, and increased targeted consumer economic benefits (such as reduced rates). The impact of implementing DERs is strategic and will be critical in supporting the energy transition process. A fundamental principle for sustainable energy development is the optimization of the grid. In Chapter 2, environmental, social, and technical land use characteristics are utilized to determine strategic community solar placement. In this objective we analyzed 9 completed survey responses from solar providers and environmental organizations to gain clarity on their beliefs toward the community solar program, its impact on communities and the environment, challenges, and the future of the industry. The information that was collected through the survey was categorized into a Saaty Rating Scale using an Analytical Hierarchy Process (AHP) to determine the relative importance of each variable. This data was then represented spatially using an intuitive mapping analysis tool, ArcGIS Pro, to visualize optimal shared solar locations. In Chapter 3, we utilize a Life Cycle Cost Assessment (LCCA) that estimates the environmental and economic impacts of a 1.5 MW onshore wind turbine using Life Cycle Assessment (LCA) and Life Cycle Cost (LCC). This objective involves scenario analysis of various disposal and life extension options. The assessment can inform policymakers who want to achieve economically viable clean energy alternatives. In Chapter 4, we review policies and implications of this study and how DERs can play a role in promoting sustainable energy practices that are eco-conscious and provide benefits to low to moderate income populations. These methods assist in providing a comprehensive understanding of small-scale wind and solar that can support environmental-focused policies and future decision-making

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi criteria analysis challenges and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore the uncertainty and supply chain performance lead to improved users confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance while development performance can be under development.Comment: Revie

A hybrid power heronian function-based multi-criteria decision-making model for workplace charging scheduling algorithms.

This study proposes a new multi-criteria decision-making model to determine the best smart charging scheduling that meets electric vehicle (EV) user considerations at work-places. An optimal charging station model is incorporated into the decision-making for a quantitative evaluation. The proposed model is based on a hybrid Power Heronian functions in which the linear normalization method is improved by applying the inverse sorting algorithm for rational and objective decision-making. This enables EV users to specify and evaluate multi-criteria for considering their aspects at workplaces. Five different charging scheduling algorithms with AC dual port L2 and DC fast charging electric vehicle supply equipment (EVSE) are investigated. Based on EV users from the field, the required charging time, EVSE occupancy, the number of EVSE units, and user flexibility are found to have the highest importance degree, while charging cost has the lowest importance degree. The experimental results show that, in terms of meeting EV users' considerations at workplaces, scheduling EVs based on their charging energy needs performs better as compared to scheduling them by their arrival and departure times. While the scheduling alternatives display similar ranking behavior for both EVSE types, the best alternative may differ for the EVSE type. To validate the proposed model, a comparison against three traditional models is performed. It is demonstrated that the proposed model yields the same ranking order as the alternative approaches. Sensitivity analysis validates the best and worst scheduling alternatives

Evaluation of the Alternatives of Introducing Electric Vehicles in developing countries using Type-2 neutrosophic numbers based RAFSI model

This study focuses on implementing electric vehicles (EVs) in developing countries where energy production is mainly based on fossil fuels. Although for these countries the environmental short-run benefits of the EVs cannot offset the short-run costs, it may still be the best option to implement the EVs as soon as possible. Hence, it is necessary to evaluate the alternatives to introducing EVs to the market due to the environmental concerns that created an opportunity for some developing countries to catch up with the international competition. Therefore, we develop a case scenario to explore the decision-making process in implementing the EVs with three alternatives and twelve criteria. We solve the decision-making problem by using Type-2 neutrosophic numbers (T2NNs) based on the RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) method. The proposed model combines the advantages of the RAFSI technique, and it applies T2NNs to address the uncertainties. The results show that the alternatives that may suspend the implementation of the EVs are inferior. Direct implementation of EVs is prioritized. The policy implications of the results are discussed in the study