Maintenance strategy optimization for complex power systems susceptible to maintenance delays and operational dynamics

Maintenance is a necessity for most multicomponent systems, but its benefits are often accompanied by considerable costs. However, with the appropriate number of maintenance teams and a sufficiently tuned maintenance strategy, optimal system performance is attainable. Given system complexities and operational uncertainties, identifying the optimal maintenance strategy is a challenge. A robust computational framework, therefore, is proposed to alleviate these difficulties. The framework is particularly suited to systems with uncertainties in the use of spares during maintenance interventions, and where these spares are characterized by delayed availability. It is provided with a series of generally applicable multistate models that adequately define component behavior under various maintenance strategies. System operation is reconstructed from these models using an efficient hybrid load-flow and event-driven Monte Carlo simulation. The simulation's novelty stems from its ability to intuitively implement complex strategies involving multiple contrasting maintenance regimes. This framework is used to identify the optimal maintenance strategies for a hydroelectric power plant and the IEEE-24 RTS. In each case, the sensitivity of the optimal solution to cost level variations is investigated via a procedure requiring a single reliability evaluation, thereby reducing the computational costs significantly. The results show the usefulness of the framework as a rational decision-support tool in the maintenance of multicomponent multistate systems

A decision support model to improve rolling stock maintenance scheduling based on reliability and cost

Thesis (MEng)--Stellenbosch University, 2014.ENGLISH ABSTRACT: The demand for rail travel has increased over the years. As a result, it is becoming mandatory for railway industries to maintain very high availability of their assets to ensure that service levels are high. Railway industries require both their infrastructure and rolling stock assets maintained efficiently to sustain reliability. There has been on-going research on how maintenance can be carried out in a cost effective manner. However, the majority of this research has been done for infrastructure and the rolling stock maintenance has not been properly covered. The purpose of this research is to contribute to the maintenance sector of rolling stock for railway industries by developing a decision support model for rolling stock based on reliability and cost. The model is developed as an optimization problem of a system containing several components dependent on each other with different reliability characteristics. In this model, a mixed integer nonlinear problem is developed and solved using an exact method and metaheuristics methods. The Metrorail facility in Cape Town was chosen as a case study. Failure history and cost data were gathered from the facility and the information was applied to the model developed. The case study was investigated and different results were achieved using both exact and metaheuristics methods. The final result from the study is an optimal maintenance schedule based on reliability and cost. The developed model serves as a practical tool railway companies can adopt to schedule rolling stock maintenance to achieve a high level of reliability and at the same time maintaining minimum cost expenditure.AFRIKAANSE OPSOMMING: Die vraag na spoorvervoer het oor die jare toegeneem. Dus het dit belangrik geword dat die spoorweg se bates hoogs toeganklik moet wees om te verseker dat die vlak van dienslewering hoog bly. Die spoorweg industrie besef dat hulle infrastruktuur, lokomotiewe, waens ens. effektief in stand gehou moet word sodat dit betroubaar kan wees. Navorsing word nog steeds gedoen oor hoe instandhouding op ’n koste-effektiewe wyse gedoen kan word. Die meeste van hierdie navorsing gaan egter oor infrastruktuur en instandhouding word nie ordentlik gedek nie. Die doel met hierdie navorsing is om by te dra tot die instandhoudingsektor van die spoorweg deur om ’n besluit-ondersteunende model vir lokomotiewe, waens, ens wat op betroubaarheid en koste gegrond is, te ontwikkel. Die model is ontwikkel as ’n optimasie probleem van ’n sisteem wat verskillende komponente wat van mekaar afhanklik is maar oor verskillende betroubaarheidskenmerke beskik, inluit. In hierdie model word ’n gemengde, heeltal nie-lineêre probleem ontwikkel en met ’n eksakte metode en metaheuristiese metodes opgelos. Die Metrorail fasiliteit in Kaapstad is vir die gevalle studie gekies. Die geskiedenis van mislukkings en koste data is by die fasiliteit versamel en die inligting is op die model wat ontwikkel is, toegepas. Die gevalle studie is ondersoek, en verskillende resultate is met eksakte en metaheuristiese metodes bereik. Die finale uitkomste van die studie is ’n optimale instandhoudingskedule wat op betroubaarheid en koste gegrond is. Die model wat ontwikkel is dien as ’n praktiese instrument wat spoormaatskappye kan gebruik om die instandhouding van lokomotiewe, waens ens. te reël sodat ’n hoë vlak van betroubaarheid bereik kan word en kostes terselfdertyd tot ’n minimum beperk kan word

Coordinated generation and transmission maintenance scheduling using mixed integer linear programming

Scheduling of electrical equipment for maintenance tasks is crucial in power system planning as it would affect system operating cost and security. Most existing Mixed Integer Linear Programming (MILP) approaches do not address the interactions between Generation Maintenance Scheduling (GMS), Transmission Maintenance Scheduling (TMS) and Security-Constrained Unit Commitment (SCUC). This research develops a MILP algorithm for the GMS, TMS and SCUC sub-problems to improve the accuracy of coordinated generation and transmission maintenance scheduling. Power flow equation which is based on sensitivity factors is modified to improve the accuracy of transmission maintenance scheduling. To reduce the complexity of the solution procedure as well as to enhance accuracy of the maintenance scheduling model, coupling constraints equations have been formulated to integrate the GMS, TMS and SCUC sub-problems. To further improve the maintenance scheduling ability, a new technique for total operating cost assessment is developed based on an hourly basis to achieve the lowest possible operating cost. Numerical case studies were evaluated on the 6-bus, IEEE 118-bus and utility systems. A comparative study is carried out between the coordinated and individual maintenance scheduling, MILP and Lagrangian Relaxation (LR) approaches, and the maintenance scheduling based on the hourly and day-to-day basis. Simulation results show that coordinated maintenance scheduling is superior to individual maintenance scheduling as it yields lower operating costs. Besides, the proposed MILP outperformed the LR with a cost reduction of up to 5% and lowered the gap tolerance by 0.13%. Moreover, cost saving of nearly 0.14% was achieved using the hourly basis in comparison to the day-to-day basis. From this research, it can be concluded that coordinated maintenance scheduling can provide optimal maintenance schedule which would benefit most of the system planners

Design for maintenance: new algorithmic approach

This paper proposes a new simultaneous optimization model of the industrial systems design and maintenance. This model aims to help the designer in searching for technical solutions and the product architecture by integrating the maintenance issues from the design stage. The goal is to reduce the life-cycle cost (LCC) of the studied system.Design/methodology/approachLiterature indicates that the different approaches used in the design for maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and the maintainability of a multicomponent system as well as the modeling of the dynamic maintenance. This article proposes to go further in the optimization of the product, by simultaneously characterizing the design, in terms of reliability and maintainability, as well as the dynamic planning of the maintenance operations. This combinatorial characterization is performed by a two-level hybrid algorithm based on the genetic algorithms.FindingsThe proposed tool offers, depending on the life-cycle expectation, the desired availability, the desired business model (sales or rental), simulations in terms of the LCCs, and so an optimal product architecture.Research limitations/implicationsIn this article, the term “design” is limited to reliability properties, possible redundancies, component accessibility (maintainability), and levels of monitoring information.Originality/valueThis work is distinguished by the use of a hybrid optimization algorithm (two-level computation) using genetic algorithms. The first level is to identify an optimal design configuration that takes into account the LCC criterion. The second level consists in proposing a dynamic and optimal maintenance plan based on the maintenance-free operating period (MFOP) concept that takes into account certain criteria, such as replacement costs or the reliability of the system

Risk-based shutdown inspection and maintenance for a processing facility

In this research, a risk-based shutdown inspection and maintenance interval optimization for a processing facility is proposed. Often inspection and maintenance activities can’t be performed until the processing unit or plant is taken into a non-operational state, generally known as “shutdown”. Extensive work on inspection and maintenance interval estimation modeling is available in the concerned literature however, no to very limited application on shutdown inspection and maintenance modeling is observed for a continuous operating facility. Majority of the published literature deals to optimize individual equipment inspection and maintenance interval without considering the overall impact of plant unavailability due to shutdown. They all deal to optimize individual equipment inspection and maintenance interval considering cost, risk, availability and reliability. The efforts towards finding an optimal inspection and maintenance interval is not considered in these studies especially when it requires unit or plant to be in shutdown state from an operational state for performing inspection and maintenance. This topic is selected to bridge the existing gap in the available literature and to provide a means to develop a methodology to estimate the shutdown inspection and maintenance interval for a continuous processing unit or plant, rather an inspection and maintenance interval for each piece of equipment considering the overall asset availability, reliability and risk. A component failure due to wear or degradation is a major threat to asset failure in a processing facility. A carefully planned inspection and maintenance strategy not only mitigate the effects of age-based degradation and reduce the threat of failure but also minimize the risk exposure. Generally failure caused by wear or degradation is modeled as a stochastic process. For an effective inspection and maintenance strategy, the stochastic nature of failure has to be taken into consideration. The proposed methodology aims to minimize the risk of exposure considering effect of failure on human life, financial investment and environment by optimizing the interval of process unit shutdown. Risk-based shutdown inspection and maintenance optimization quantifies the risk to which individual equipment are subjected and uses this as a basis for the optimization of a shutdown inspection and maintenance strategy