Performing a maintenance of wind energy system components under good wind conditions may lead to energy not served and finally – to financial losses. The best starting time of preventive maintenance will be, that reduces the energy not served in most. To find this time, a decision model is desired, where many circumstances should be taken into account, i.e. (i) the number and the order of components to be maintained, (ii) component maintenance duration, and (iii) wind turbine(s) output power prediction. Usually, preventive maintenance is planned a few days or weeks in advance. One of the decision problem representations can be influence diagram that enables choosing a decision alternative that has the lowest expected utility (energy not served). The paper presents an decision model that can support decisions-making on starting time of preventive maintenance and maintenance order of wind energy system components. The model relies on influence diagram. The conditional probability distribution of a chance nodes of the diagram are obtained relying on Bayesian networks (BN), whereas the utilities of value node in the diagram are calculated thanks to the second order semi-Markov chains (SMC). The example shows the application of the model in real case of two wind turbines located in Poland. Both the parameters of Bayesian network nodes and semi-Markov chain are derived from real data recorded by SCADA system of the both turbines and weather forecast.

Decision model of wind turbines maintenance planning.

Guglielmo D’Amico;
2019

Abstract

Performing a maintenance of wind energy system components under good wind conditions may lead to energy not served and finally – to financial losses. The best starting time of preventive maintenance will be, that reduces the energy not served in most. To find this time, a decision model is desired, where many circumstances should be taken into account, i.e. (i) the number and the order of components to be maintained, (ii) component maintenance duration, and (iii) wind turbine(s) output power prediction. Usually, preventive maintenance is planned a few days or weeks in advance. One of the decision problem representations can be influence diagram that enables choosing a decision alternative that has the lowest expected utility (energy not served). The paper presents an decision model that can support decisions-making on starting time of preventive maintenance and maintenance order of wind energy system components. The model relies on influence diagram. The conditional probability distribution of a chance nodes of the diagram are obtained relying on Bayesian networks (BN), whereas the utilities of value node in the diagram are calculated thanks to the second order semi-Markov chains (SMC). The example shows the application of the model in real case of two wind turbines located in Poland. Both the parameters of Bayesian network nodes and semi-Markov chain are derived from real data recorded by SCADA system of the both turbines and weather forecast.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/698981
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