A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms

We study the preventive maintenance scheduling problem of wind farms in the offshore wind energy sector which operates under uncertainty due to the state of the ocean and market demand. We formulate a fuzzy multi-objective non-linear chance-constrained programming model with newly-defined reliability and cost criteria and constraints to obtain satisfying schedules for wind turbine maintenance. To solve the optimization model, a 2-phase solution framework integrating the operational law for fuzzy arithmetic and the non-dominated sorting genetic algorithm II for multi-objective programming is developed. Pareto-optimal solutions of the schedules are obtained to form the trade-offs between the reliability maximization and cost minimization objectives. A numerical example is illustrated to validate the model

Genetic algorithms for scheduling generation and maintenance in power systems

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Outage planning of electrical power system networks using genetic algorithm

An electrical company is responsible for the maintenance of a transmission network of high voltage electricity. The maintenance schedule must be planned so as to minimize outage costs, taking into consideration various factors such as system security/reliability, system availability, and manpower utilization. With the rapid growth of organization, planning engineers are required to fulfill additional roles in order to increase productivity. To this end, a fast response and accurate mechanism is required to assist the planning engineers in dealing with the daily operation. This paper describes how a proposed maintenance schedule can be obtained automatically by the adoption of genetic algorithm. The main aim is to determine the maintenance schedule of circuit outage with minimizing the maintenance cost and maximizing the circuit availability under certain unavoidable system constraints. Further, an additional search mechanism called "final tuning search" is developed to enhance the system performance.published_or_final_versio

A reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordWe study the preventive maintenance scheduling problem of wind farms in the offshore wind energy sector which operates under uncertainty due to the state of the ocean and market demand. We formulate a fuzzy multi-objective non-linear chance-constrained programming model with newly-defined reliability and cost criteria and constraints to obtain satisfying schedules for wind turbine maintenance. To solve the optimization model, a 2-phase solution framework integrating the operational law for fuzzy arithmetic and the non-dominated sorting genetic algorithm II for multi-objective programming is developed. Pareto-optimal solutions of the schedules are obtained to form the trade-offs between the reliability maximization and cost minimization objectives. A numerical example is illustrated to validate the model.Recruitment Program of High-end Foreign Expert

The Contribution Of Organisational Climate To Employee Well-Being

There is ample space for further human resource based research in the service industry sector in South Africa. For that reason, this study developed and tested a conceptual framework that linked employee well-being to four organisational climate factors; namely, manager-employee relationships, working conditions, remuneration and work allocation. An adapted six section structured questionnaire was administered to a conveniently recruited sample composed of 164 employees drawn from seven service industry enterprises located in Southern Gauteng, South Africa. Hypotheses were tested using regression analysis. All four organisational climate dimensions were statistically significant, implying that they predict employee well-being in the service industry. The results of this study may be used by managers in similar environments as either diagnostic tools or as a reference benchmark for strategic interventions in solving employee well-being related problems.

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio

Using Experiments to Foster Innovation and Improve the Effectiveness of Energy Efficiency Programs

This paper argues that the establishment of a process designed to manage innovation must be developed in California to foster the creation of needed program improvements and develop new and more effective energy efficiency delivery programs. This paper discusses several key institutional problems that must be overcome to achieve significant progress