Anomaly indicators for Kaplan turbine components based on patterns of normal behavior

This paper describes and proposes some indicators for continuous monitoring of anomalous conditions in the hydraulic system of a Kaplan turbine using SCADA data. The indicators are based on significant deviations between the estimated values for key variables describing the current working conditions of the components at the plant, and those actually observed. This monitoring strategy requires models describing the expected values for variables through the whole range of possible working conditions of the monitored components. These models are normal behavior models able to characterize the typical relationships between a set of variables used as inputs to the models and the corresponding output of a target variable whose expected value has to be predicted. The criteria to select the variables to use in the models are based on the physical working principles of the component. The paper is focused on models of normal behavior applied to a real case of condition monitoring of a Kaplan turbine regulating mechanism.Anomaly indicators for Kaplan turbine components based on patterns of normal behaviorpublishedVersio

Anomaly indicators for Kaplan turbine components based on patterns of normal behavior

This paper describes and proposes some indicators for continuous monitoring of anomalous conditions in the hydraulic system of a Kaplan turbine using SCADA data. The indicators are based on significant deviations between the estimated values for key variables describing the current working conditions of the components at the plant, and those actually observed. This monitoring strategy requires models describing the expected values for variables through the whole range of possible working conditions of the monitored components. These models are normal behavior models able to characterize the typical relationships between a set of variables used as inputs to the models and the corresponding output of a target variable whose expected value has to be predicted. The criteria to select the variables to use in the models are based on the physical working principles of the component. The paper is focused on models of normal behavior applied to a real case of condition monitoring of a Kaplan turbine regulating mechanism

Anomaly indicators for Kaplan turbine components based on patterns of normal behavior

This paper describes and proposes some indicators for continuous monitoring of anomalous conditions in the hydraulic system of a Kaplan turbine using SCADA data. The indicators are based on significant deviations between the estimated values for key variables describing the current working conditions of the components at the plant, and those actually observed. This monitoring strategy requires models describing the expected values for variables through the whole range of possible working conditions of the monitored components. These models are normal behavior models able to characterize the typical relationships between a set of variables used as inputs to the models and the corresponding output of a target variable whose expected value has to be predicted. The criteria to select the variables to use in the models are based on the physical working principles of the component. The paper is focused on models of normal behavior applied to a real case of condition monitoring of a Kaplan turbine regulating mechanism.Anomaly indicators for Kaplan turbine components based on patterns of normal behaviorpublishedVersio

Quantification of condition monitoring benefit for offshore wind turbines

Condition monitoring (CM) systems are increasingly installed in wind turbines with the goal of providing component-specific information to wind farm operators, theoretically increasing equipment availability via maintenance and operating actions based on this information. In the offshore case, economic benefits of CM systems are often assumed to be substantial, as compared with experience of onshore systems. Quantifying this economic benefit is non-trivial, especially considering the general lack of utility experience with large offshore wind farms. A quantitative measure of these benefits is therefore of value to utilities and operations and maintenance (O & M) groups involved in planning and operating future offshore wind farms. The probabilistic models presented in this paper employ a variety of methods including discrete-time Markov Chains, Monte Carlo methods and time series modelling. The flexibility and insight provided by this framework captures the necessary operational nuances of this complex problem, thus enabling evaluation of wind turbine CM offshore. The paper concludes with a study of baseline CM benefit, sensitivity to O & M costs and finally effectiveness of the CM system itself