The amount of money that utilities spend on operations and maintenance (O&M) is increasing with no slowdown in sight. Operational performance is an area organizations look to most often when trying to optimize staffing levels while reducing costs. In this use case, we’ll explore how a modern data platform can help manage wind turbine icing and improve operational performance.
Wind energy is quickly evolving into a main pillar of the world’s green energy initiative, and as such its operations are being looked at with greater scrutiny. Unlike other operations within the industry, the operation of wind turbines must be evaluated a little differently because each turbine is affected differently by weather conditions, altitude, and other factors based on its location.
When optimizing the performance of wind turbines, you need to consider several different factors, including the engineering specification of the turbine and its blades along with its location and the weather affecting its performance. To make sense of all this data, you need a data platform that will allow you to combine the data and apply machine learning (ML) as quickly as possible to provide insights to better optimize your operational performance. In the case of wind turbines, ice or even frost on the blade has been shown to affect the turbine's aerodynamic efficiency substantially, and it can reduce power generation by up to 80%. The ability to use ML and advanced analytics to swiftly understand, prepare for, and manage this loss is imperative to minimize the overall impact and maintain operational efficiency.
All three capabilities connect unidirectionally into the serving data store, and cloud storage within the Persist, Curate, Create pillar.
These capabilities are connected within the pillar. Cloud storage is unidirectionally connected to the serving data store; it is also bidirectionally connected to batch processing.
The metadata lines unidirectionally connect from the serving data store and cloud storage to governance.
Two capabilities connect into the Analyze, Learn, Predict pillar: The serving data store unidirectionally connect to the analytics and visualization, predict , learn and AI services. Whereas the Cloud Storage connects bi-directionally to learn but unidirectionally to the analytics and visualization, learn and AI services.
The Measure, Act pillar captures how the data analysis may be applied to support a wind turbine icing delivery model and monitor performance. These applications are divided into two groups.
The first group Peoples and Partners includes operations and maintenance.
The second group Applications includes Oracle Field Service, Oracle Utilities Work and Asset Management, enterprise asset management, work management system, and field service management.
The three central pillars—Ingest, Transform; Persist, Curate, Create; and Analyze, Learn, Predict—are supported by infrastructure, network, security, and IAM.
Wind turbine operational performance logical architecture
There are three main ways to inject data into an architecture to enable utilities to effectively evaluate their operational performance strategy for wind turbines.
Data persistence and processing is built on three components.
The ability to analyze, learn, and predict is facilitated by three technologies.
Inefficient maintenance strategies can degrade operational performance and profitability, and lead to unsatisfied customers. This wind turbine icing use case is just one example of how you can use ML and other advanced analytic techniques, including predictive and prescriptive analytics, to fine-tune your operational performance strategy. By using these techniques, you can now anticipate freezing events and asset breakdowns and generate actionable insights in real time. These insights trigger prescriptive workflows so you can take preemptive action and optimize your maintenance. The following examples are some of the possible outcomes you can realize when you use the right data platform to improve your operational performance:
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