Population-based wind farm monitoring based on a spatial autoregressive approach

• URL: http://arxiv.org/abs/2310.10555v1
• Date: Mon, 16 Oct 2023 16:26:40 GMT
• Title: Population-based wind farm monitoring based on a spatial autoregressive approach
• Authors: W. Lin, K. Worden and E.J. Cross
• Abstract summary: Population-based structural health monitoring can further reduce the cost of health monitoring systems. To monitor turbine performance at a population/farm level, an important initial step is to construct a model that describes the behaviour of all turbines. This paper proposes a population-level model that explicitly captures the spatial and temporal correlations (between turbines) induced by the wake effect.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An important challenge faced by wind farm operators is to reduce operation and maintenance cost. Structural health monitoring provides a means of cost reduction through minimising unnecessary maintenance trips as well as prolonging turbine service life. Population-based structural health monitoring can further reduce the cost of health monitoring systems by implementing one system for multiple structures (i.e.~turbines). At the same time, shared data within a population of structures may improve the predictions of structural behaviour. To monitor turbine performance at a population/farm level, an important initial step is to construct a model that describes the behaviour of all turbines under normal conditions. This paper proposes a population-level model that explicitly captures the spatial and temporal correlations (between turbines) induced by the wake effect. The proposed model is a Gaussian process-based spatial autoregressive model, named here a GP-SPARX model. This approach is developed since (a) it reflects our physical understanding of the wake effect, and (b) it benefits from a stochastic data-based learner. A case study is provided to demonstrate the capability of the GP-SPARX model in capturing spatial and temporal variations as well as its potential applicability in a health monitoring system.

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