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"Systems availability"
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Availability Analysis of HVDC-VSC Systems: A Review
This work stems from the worldwide increasing need to precisely consider, in the design phase of an HVDC project, the availability of the HVDC system. In this paper, an overview of the availability assessment methods for HVDC-VSC transmission systems is presented. In particular, the state of the art of the procedures to estimate the availability of both the HVDC link reparable components and the conversion system on the basis of the converter configuration is given. The theoretical fundamentals of each method, together with their practical applications, have been described, in order to highlight the limits and the potentialities of each approach. The authors aim at giving a guide to choosing the best computation approach on the basis of the specific needs of the users and at summarizing all the key aspects which can be taken into account during the availability assessment of HVDC-VSC links.
Journal Article
Reliability and availability engineering : modeling, analysis, and applications
\"Do you need to know what technique to use to evaluate the reliability of an engineered system? This self-contained guide provides comprehensive coverage of all the analytical and modeling techniques currently in use, from classical non-state and state space approaches, to newer and more advanced methods such as binary decision diagrams, dynamic fault trees, Bayesian belief networks, stochastic Petri nets, non-homogeneous Markov chains, semi-Markov processes, and phase type expansions. Readers will quickly understand the relative pros and cons of each technique, as well as how to combine different models together to address complex, real-world modeling scenarios. Numerous examples, case studies and problems provided throughout help readers put knowledge into practice, and a solutions manual and Powerpoint slides for instructors accompany the book online. This is the ideal self-study guide for students, researchers and practitioners in engineering and computer science.\"--Back cover.
Book
Performance analysis of 30 MW wind power plant in an operation mode in Nouakchott, Mauritania
In this paper, the performance analysis of a 30 MW wind power plant is performed. The farm consists of fifteen (T1-T15) G9 7/2000/GAMESA 2 MW grid-connected turbines. The farm is in operation mode installed 28 km south of Nouakchott city in Mauritania. The analyzed data are monitored from July 1st, 2015 (the first operation day of the power plant) to December 31st, 2019. The parameters of performance evaluation are power generation, capacity factor, machine availability, grid availability, and system availability. It is observed from data analysis, the wind farm supplies a total energy of 507.39 GWh to the power grid and have a high average capacity factor of 42.55%. T1 produces the highest amount of electrical energy among the other turbines with a total energy output of 35.46 GWh, an average capacity factor of 44.97%, and operating hours of 33,814 hours. While T12 produced the minimum amount of energy in this period, the difference in energy compared to T1 is 4.563 GWh. It is observed that the availability of the network is unstable and needs improvement, varying between 90.86% in 2016 and 93.16% in 2018. In the first year of operation, 97.06% of the turbines were available. However, the average availability of the wind farm is approximately 94% during the total study period.
Journal Article
Multi-objective optimization of series-parallel system with mixed subsystems failure dependencies using NSGA-II and MOHH
In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.
Journal Article
Intelligent Campus System Design Based on Digital Twin
Amid the COVID-19 pandemic, prevention and control measures became normalized, prompting the development of campuses from digital to intelligent, eventually evolving to become wise. Current cutting-edge technologies include big data, Internet of Things, cloud computing, and artificial intelligence drive campus innovation, but there are still problems of unintuitive scenes, lagging monitoring information, untimely processing, and high operation and maintenance costs. Based on this, this study proposes the use of digital twin technology to digitally construct the physical campus scene, fully digitally represent it, accurately map the physical campus to the virtual campus with real-time sensing, and remotely control it to achieve the reverse control of the twin virtual campus to the physical campus. The research is guided by the theoretical model proposed by the digital twin technology, using UAV tilt photography and 3D modelling to collaboratively build the virtual campus scene. At the design stage, the interactive channel of the system is developed based on Unity3D to the realize real-time monitoring, decision making and prevention of dual spatial data. A design scheme of the spiral optimization system life cycle is formed. The modules of the smart campus system were evaluated using a system usability scale based on student experience. The experimental results show that the virtual-real campus system can enhance school management and teaching, providing important implications for promoting the development and application of campus intelligent systems.
Journal Article
An architecture framework for enterprise IT service availability analysis
This paper presents an integrated enterprise architecture framework for qualitative and quantitative modeling and assessment of enterprise IT service availability. While most previous work has either focused on formal availability methods such as fault trees or qualitative methods such as maturity models, this framework offers a combination. First, a modeling and assessment framework is described. In addition to metamodel classes, relationships and attributes suitable for availability modeling, the framework also features a formal computational model written in a probabilistic version of the object constraint language. The model is based on 14 systemic factors impacting service availability and also accounts for the structural features of the service architecture. Second, the framework is empirically tested in nine enterprise information system case studies. Based on an initial availability baseline and the annual evolution of the 14 factors of the model, annual availability predictions are made and compared with the actual outcomes as reported in SLA reports and system logs. The practical usefulness of the method is discussed based on the outcomes of a workshop conducted with the participating enterprises, and some directions for future research are offered.
Journal Article
Agent-based middleware architecture for reconfigurable manufacturing systems
Modern manufacturing systems are expected to be flexible and efficient in order to cope with challenging market demands. Thus, they must be flexible enough as to meet changing requirements such as changes in production, energy efficiency, performance optimization, fault tolerance to process or controller faults, among others. Demanding requirements can be defined as a set of quality of service (QoS) requirements to be met. This paper proposes a generic and customizable multi-agent architecture that, making use of distributed agents, monitors QoS, triggering, if needed, a reconfiguration of the control system to recover QoS. As a proof of concept, the architecture has been implemented to provide availability of the control system understood as service continuity. The prototype has been tested in a case study consisting of an assembly cell where assessment of the approach has been conducted.
Journal Article
The Demand Supply Steady-State Process-Based Multi-Level Spare Parts Optimization
Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand–supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy’s current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed.
Journal Article
Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments
Failures are normal rather than exceptional in the cloud computing environments. To improve system avai- lability, replicating the popular data to multiple suitable locations is an advisable choice, as users can access the data from a nearby site. This is, however, not the case for replicas which must have a fixed number of copies on several locations. How to decide a reasonable number and right locations for replicas has become a challenge in the cloud computing. In this paper, a dynamic data replication strategy is put forward with a brief survey of replication strategy suitable for distributed computing environments. It includes： 1） analyzing and modeling the relationship between system availability and the number of replicas; 2） evaluating and identifying the popular data and triggering a replication operation when the popularity data passes a dynamic threshold; 3） calculating a suitable number of copies to meet a reasonable system byte effective rate requirement and placing replicas among data nodes in a balanced way; 4） designing the dynamic data replication algorithm in a cloud. Experimental results demonstrate the efficiency and effectiveness of the improved system brought by the proposed strategy in a cloud.
Journal Article