Prognosis Informed Stochastic Decision Making Framework for Operation and Maintenance of Wind Turbines

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Yibin Wang ◽  
Pingfeng Wang ◽  
Janet M. Twomey
Keyword(s):  
Decision Making ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Economic Loss ◽  
Environmental Benefits ◽  
Maintenance Strategy ◽  
System Failures ◽  
Operation And Maintenance ◽  
Failure Prognosis

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

Prognosis Informed Design Framework for Operation and Maintenance of Wind Turbines

2012 ◽  
Author(s):  
Prasanna Tamilselvan ◽  
Yibin Wang ◽  
Pingfeng Wang
Keyword(s):  
Decision Making ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Economic Loss ◽  
Environmental Benefits ◽  
Maintenance Strategy ◽  
System Failures ◽  
Operation And Maintenance ◽  
Failure Prognosis

Advances in high performance sensing and signal processing technology enable the development of failure prognosis tools for wind turbines to detect, diagnose, and predict the system-wide effects of failure events. Although prognostics can provide valuable information for proactive actions in preventing system failures, the benefits have not been fully utilized for the operation and maintenance decision making of wind turbines. This paper presents a generic failure prognosis informed decision making tool for wind farm operation and maintenance while considering the predictive failure information of individual turbine and its uncertainty. In the presented approach, the probabilistic damage growth model is used to characterize individual wind turbine performance degradation and failure prognostics, whereas the economic loss measured by monetary values and environmental performance measured by unified carbon credits are considered in the decision making process. Based on the customized wind farm information inputs, the developed decision making methodology can be used to identify optimum and robust strategies for wind farm operation and maintenance in order to maximize the economic and environmental benefits concurrently. The efficacy of proposed prognosis informed maintenance strategy is compared with the condition based maintenance strategy and demonstrated with the case study.

Offshore Wind Turbines Operation and Maintenance in China: A Case Study of Donghai Bridge Offshore Wind Farm

2013 ◽  
Vol 448-453 ◽  
pp. 1871-1874
Author(s):  
Yuan Xie
Keyword(s):  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Offshore Wind Turbines ◽  
Operation And Maintenance ◽  
Steady Stage ◽  
Power Project

China has great potential in offshore wind energy and makes an ambitious target for offshore wind power development. Operation and Maintenance (O&M) of offshore wind turbines become more and more important for China wind industry. This study introduces the current offshore wind power projects in China. Donghai Bridge Offshore Demonstration Wind Farm (Donghai Bridge Project) is the first commercial offshore wind power project in China, which was connected to grid in June 2010. O&M of Donghai Bridge Project represent the state-of-the-art of China offshore O&M. During the past two and half years, O&M of Donghai Bridge Project has gone through three phases and stepped into a steady stage. Its believed that analysis of O&M of Donghai Bridge Project is very helpful for Chinas offshore wind power in the future.

Research on Maintenance Model of Main Equipment Based on UMM

2014 ◽  
Vol 488-489 ◽  
pp. 1277-1280
Author(s):  
Shi Cong Deng ◽  
Ding Yao Xiao ◽  
Lin Fa Li ◽  
Wei Zhao Huang
Keyword(s):  
Decision Making ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Maintenance Strategy ◽  
Practical Applications ◽  
Multi Objective ◽  
Operation And Maintenance ◽  
Multi Stage ◽  
Decision Making Model ◽  
Maintenance Model

In this paper the problems of traditional operation maintenance strategy is introduced. Uncertain multi-stage and multi-objective decision-making model of operation and maintenance is shown in this paper. Considering the life cycle cost, the best maintenance scheme is determined through the establishment of an uncertain multi-stage and multi-objective decision-making model of operation and maintenance. It also proves the practical applications of UMM model by GIS example.

scholarly journals Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6743
Author(s):  
Luca Pinciroli ◽  
Piero Baraldi ◽  
Guido Ballabio ◽  
Michele Compare ◽  
Enrico Zio
Keyword(s):  
Reinforcement Learning ◽  
Wind Turbines ◽  
Energy Demand ◽  
Wind Farm ◽  
Wind Farms ◽  
Uncertain Information ◽  
Sequential Decision ◽  
Operation And Maintenance ◽  
Long Time ◽  
Policy Optimization

The life cycle of wind turbines depends on the operation and maintenance policies adopted. With the critical components of wind turbines being equipped with condition monitoring and Prognostics and Health Management (PHM) capabilities, it is feasible to significantly optimize operation and maintenance (O&M) by combining the (uncertain) information provided by PHM with the other factors influencing O&M activities, including the limited availability of maintenance crews, the variability of energy demand and corresponding production requests, and the long-time horizons of energy systems operation. In this work, we consider the operation and maintenance optimization of wind turbines in wind farms woth multiple crews. A new formulation of the problem as a sequential decision problem over a long-time horizon is proposed and solved by deep reinforcement learning based on proximal policy optimization. The proposed method is applied to a wind farm of 50 turbines, considering the availability of multiple maintenance crews. The optimal O&M policy found outperforms other state-of-the-art strategies, regardless of the number of available maintenance crews.

An opportunistic maintenance strategy for offshore wind turbine based on accessibility evaluation

2019 ◽  
Vol 44 (5) ◽  
pp. 455-468
Author(s):  
Xie Lubing ◽  
Rui Xiaoming ◽  
Li Shuai ◽  
Hu Xin
Keyword(s):  
Wind Turbine ◽  
Preventive Maintenance ◽  
Wind Farm ◽  
Weather Conditions ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Strategy ◽  
Offshore Wind Farm ◽  
Opportunistic Maintenance ◽  
Operation And Maintenance

The maintenance costs of offshore wind turbines operated under the irregular, non-stationary conditions limit the development of offshore wind power industry. Unlike onshore wind farms, the weather conditions (wind and waves) have greater impacts on the operation and maintenance of offshore wind farm. Accessibility is a key factor related to the operation and maintenance of offshore wind turbine. Considering the impact of weather conditions on the maintenance activities, the Markov method and dynamic time window are applied to represent the weather conditions, and an index used to evaluate the maintenance accessibility is then proposed. As the wind turbine is a multi-component complex system, this article uses the opportunistic maintenance strategy to optimize the preventive maintenance age and opportunistic maintenance age for the main components of the wind turbine. Taking the minimum expectation cost as objective function, this strategy integrates the maintenance work of the key components. Finally, an offshore wind farm is taken for simulation case study of this strategy; the results showed that the maintenance cost of opportunistic maintenance strategy is 10% lower than that of the preventive maintenance strategy, verifying the effectiveness of the opportunistic maintenance.

scholarly journals MATHEMATICAL MODELING AND NUMERICAL STUDY OF ATMOSPHERIC BOUNDARY LAYER NEAR WINDFARMS

2021 ◽  
Vol 3 (65) ◽  
pp. 33-40
Author(s):  
Vladislav N. Kovalnogov ◽  
◽  
Yuriy A. Khakhalev ◽  
Ekaterina V. Tsvetova ◽  
Larisa V. Khakhaleva ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Turbines ◽  
High Performance ◽  
Wind Farm ◽  
Numerical Study ◽  
Wind Farms ◽  
Time Range ◽  
Winter Period

The article analyzes Russian and foreign sources relating to the interaction of wind turbines with the surface layers of the atmosphere. It specifies the main problems of mathematical modeling of the atmospheric boundary layer near the wind farms due to adverse meteorological conditions, in particular, constant zero crossings in the autumn-winter period, various precipitation, a wide time range, air parameters, terrain and other features. The authors analyze the evolution of mathematical models of turbulence to describe the boundary layer near wind turbines from earlier to rapidly developing and currently used. To achieve greater accuracy and naturalism, it is proposed to use high-performance efficient algorithms based on combining scales and physics of phenomena. The authors propose a mathematical model for studying the state of the atmospheric polydisperse boundary layer under conditions of the Ulyanovsk wind farm, taking into account the dispersed particles in the flow, surface curvature, pressure gradient and other influences.

scholarly journals Maintenance Models Applied to Wind Turbines. A Comprehensive Overview

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 225 ◽  
Author(s):  
Yuri Merizalde ◽  
Luis Hernández-Callejo ◽  
Oscar Duque-Perez ◽  
Víctor Alonso-Gómez
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Comprehensive Overview ◽  
Fossil Energy ◽  
Operation And Maintenance ◽  
Cost Of Energy ◽  
Wide Range ◽  
Different Types ◽  
The Cost ◽  
Machine Learning Models

Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

Sign in / Sign up

Export Citation Format

Share Document