Tongdan Jin

Uncertainty analysis for wind energy production with dynamic power curves

The production of wind energy often involves uncertainties due to the stochastic nature of wind speeds and the variation of the power curve. The latter occurs when a fleet of wind turbine generators (WTG) of the same type are deployed in the wind farm, yet each generator may produce different amounts of powers given the same wind speed. This paper aims to characterize the dynamics of the wind energy production and estimate the power uncertainty when a WTG operates between the cut-in wind speed and the rated wind speed. We propose a probabilistic model to characterize the dynamics of the output power assuming the power follows the normal distribution with a varying mean and a constant standard deviation. Based on that condition, the mean and the variance for the total wind energy production are derived by incorporating the stochastic wind speed and the power dynamics. Both simulations and numerical examples are provided to verify and illustrate the new model, respectively.

Managing wind turbine reliability and maintenance via performance-based contract

Performance-based contracting (PBC) is reshaping the acquisition, operation, and maintenance of capital equipment. Under the PBC scheme, we propose a holistic approach for lowering the cost of wind turbine ownership while meeting the availability requirement. Our effects are focused on integrated firms who design and market wind turbines, and also provide maintenance and repair service. PBC differs from conventional service contracts in that the wind farmers compensate the service provider for the system performance, not the spare parts and repair labor. We explore the analytical relationship between system cost, reliability and spare parts stocking. This analytical insight into the system performance allows the turbine manufacturer to optimize design, production, and after-sales services. The study aims to create a theoretical basis to facilitate the transition of the maintenance paradigm from a material-based contract to a performance-based contract in the power industry.

Understanding operational availability in performance-based logistics and maintenance services

Performance-based service contracting is envisioned to reduce the assets ownership cost while ensuring the system performance. Operational availability is considered as the key performance measure to assess the system reliability outcome. This paper presents a unified modeling approach that comprehends eight key performance drivers underpinning the operational availability. These drivers include inherent reliability, usage rate, maintenance, spare parts, fleet size, repair-by-replacement time, and parts repair and reconditioning turn-around times. We investigate the maintenance logistics in a single-site repairable inventory setting, yet the result can be appropriately extended to more complex logistics networks. This study shows that high operational availability could be attained even under low parts availability. This finding is contradictory to the traditional belief that low operational availability is ascribed to excessive parts backorders. The study has the great potentials to shift the service business paradigm from traditional material-based contracting to performance-based contracting.

Coordinating maintenance with spares logistics to minimize levelized cost of wind energy

Wind power emerges as a sustainable energy resource to meet the increasing electricity needs in the next 20-30 years. Power volatility and maintenance costs are the key challenges in harvesting this type of renewable energy. The levelized cost of energy (LCOE) allows the utility and investors to compare the costs of various generation technologies of unequal lifetimes and capacities. In this study we propose a probabilistic-based LCOE model to assess the investment risks by taking into account four major factors: wind speed, system availability, maintenance policy, and spares stock level. Moment methods are applied to estimate the mean and the variance of the energy yield. The goal of the study is to develop a decision aid methodology guiding the wind farmers to minimize the ownership cost by jointly optimizing the maintenance and the spares inventory. We assume the maintenance and repair service is carried by a third party logistics provider. Genetic algorithm is used to search the optimality of the mixed integer non-linear decision model.

Maintenance of wind turbine systems under continuous monitoring

By utilizing condition monitoring information collected from wind turbine components, condition based maintenance (CBM) can be used to reduce the operation and maintenance costs of wind power generation systems. The existing CBM methods for wind power generation systems deal with wind turbine components separately. In other words, maintenance decisions are made on individual components, rather than the whole system. However, a wind farm consists of multiple wind turbines, and each wind turbine has multiple components including main bearing, gearbox, generator, etc. There are economic dependencies among wind turbines and their components. That is, once a maintenance team is sent to the wind farm, it may be more economical to take the opportunity to maintain multiple turbines, and when a turbine is stopped for maintenance, it may be more cost-effective to simultaneously maintain multiple components which show relatively high risks. In this paper, we develop an optimal CBM solution to address the above-mentioned issues. A simulation method is developed to evaluate the cost of the CBM policy. A numerical example is provided to illustrate the proposed CBM approach.

Managing performance based logistics by balancing reliability and spare parts stocking

This paper investigates a quantitative approach for performance based logistics management in the presence of uncertainty. Our study concentrates on the situation where the customer purchased the capital equipment from the original equipment manufacturer who also provides the after-sales service. The service provider will be rewarded monetarily if equipment availability is kept above a pre-specified level. We derived an analytical model to characterize the equipment availability incorporating five performance drivers. This analytical insight into the equipment availability allows us to assess the trade-off between product reliability and the spare parts level. The equipment availability metric is examined under various scenarios by varying the usage rate, repair turn-around time, and the availability target.

Reliability growth planning for product-service integration

Reliability growth testing allows the manufacturers to identify and eliminate critical failure modes during new product development. Such practices become increasingly difficult to implement in todays fast-paced, decentralized business environment. In addition, a growing number of firms start to bundle the product with the services by offering lifecycle reliability commitment under performance-based contracting. Hence industries need to re-examine conventional reliability growth scheme, and seek new methodologies to meet the changing requirements. This paper proposes a lifecycle reliability growth model in which the growth efforts are seamlessly integrated into design, manufacturing and post-installation of a new product. We investigate the interactions between testing time, corrective actions and latent failures in two phases. First, an optimal reliability growth testing schedule is devised to minimize the failure rate in early development phase. Second, reliability growth initiative is further extended to post-installation by deploying on-site corrective actions against latent failures. The paper is concluded with the emphasis on deploying lifecycle reliability program to facilitate the integration of product and services.

Reliability prognostics for electronics via built-in diagnostic tools

This paper proposes a practical model to monitor the degradation of electronic equipment and further to predict the remaining useful life based on the self-diagnostic data. The de gradation precursor, characterized by voltage or current signals, is modeled as a Non-stationary Gaussian process with tim e-varying mean and variance. Statistical testing is then used to characterize the trend patterns for the mean and the variance, from which different types of degradation paths will be extrapolated. Regression tools and time series models can be adopted to forecast the system remaining useful life. A case study drawn from the semiconductor testing equipment is used to demonstrate the applicability and the performance of the proposed method.

Optimal replacement of safety-critical aircraft parts with utilization uncertainties

Two maintenance optimization programs have been proposed to find the optimum lifetime limits of turbine wheels, a safety-critical component in the aircraft engine. The deterministic model enables us to quickly calculate the optimum replacement time provided that the accurate cumulative operating hours are available for the critical components. When the component operating hours involve utilization uncertainties, the stochastic model has been demonstrated as an effective method in terms of finding the optimum age-based replace time. The result is accurate as compared to the solutions form Monte Carlo simulations.

A zero-carbon supply chain model: minimising levelised cost of onsite renewable generation

ABSTRACT This paper develops a linear optimisation model to investigate whether it is feasible to operate a net-zero-carbon supply chain network via 100% of onsite wind and solar generation. In particular, this work determines the technology, location and size of renewable generating units in an integrated production–transportation system with the goal of minimising the levelised cost of renewable energy. Numerical experiments show that it is technically feasible and economically viable to realise a zero-carbon supply chain operation provided the local facility possesses a medium wind speed and the installation cost of photovoltaics drops to