Deliverables

The list of deliverables submitted to date is as follows:

D1.1Report on use case definitions
and
evaluation metrics
This document describes the use case definitions and evaluation metrics that will be carried out in the SmartWind project. It contains the final results obtained in the process of scope definition and initial design of the solution to be developed in the project SmartWind.
The contents included in this deliverable will allow the reader to acquire a better understanding of the objectives of the project SmartWind, how the different members of the consortium will cooperate to comply with the objectives of the project, and what to expect to be obtained after the development of validations of the solution.
A list of 12 use cases have been developed, which ranges from the detection and prediction of failures in most of the main mechanical and electrical subsystems that compose wind turbines (WT), to the provision of Operations and Maintenance (O&M) recommendations and analytics at a wind farm level.
The approach that each of the use cases proposes to achieve the objectives of the project has been defined, as well as the mechanisms which will be used to measure the success of each one of them. Therefore, a list of Key Performance Indicators has been defined that summarises general optimization targets of the project but also of the different use cases considered in SmartWind.
D1.2Requirements for
use cases’ information architecture
Within the project SmartWind, an integrated platform for cost reduction and revenue optimisation for wind farms operators is developed based on advanced and automated functions for data analysis, performance diagnosis, fault detection, performance diagnosis, root cause analysis and Operations and Maintenance (O&M) recommendations. These functions are designed and integrated in a cloud platform for the important and relevant subsystems of a wind farm and the turbines, which are defined in the use cases investigated in the project.
In this deliverable, the optimization objectives of the use cases are summarised including the early detection of component failures and measures to minimize the main deterioration effects that limit the energy yield of the wind farm and that deteriorate the lifetime and availability of the assets. As a central part of the document, the requirements for the information architecture of the integrated platform are defined. Specifically, the required measurement data and the corresponding technological, functional and non-functional requirements are summarised for each use case of the project. Furthermore, business requirements of the WF operator regarding the overall information architecture have to be taken into account to ensure the application-oriented research of SmartWind.
To give an overview of the results of D 1.2, conceptual illustrations of the existing and planned information architecture visualize the data flow and the interdependencies of different use cases in the project SmartWind. They include the data transfer from the wind farm to the platform with its data pre-processing and evaluation within the use cases to fulfil tasks of predictive maintenance and operation optimization. The results of the analyses are brought together and evaluated to provide O&M recommendation and action to the wind farm operator.
To conclude, D 1.2 forms the basis for the information architecture that will be developed in SmartWind, as the requirements posed by the different use cases at the available measurement signals are merged. Therewith, the feasibility of planned evaluation processes within the use cases is ensured and restrictions can be considered in an early stage of the development process.
D2.1Existing and desirable sensors to develop predictive maintenance for each use caseThis document describes the existing and desirable sensors of the test wind farm to develop predictive maintenance and operation optimization for each use case. Monitoring of signals requires the identification of suitable sensors solutions. Sensor selection, deployment and characterization are therefore fundamental steps for the implementation of condition-based maintenance strategies and technical developments envisaged within the project.
Initially in Chapter 3, this document details the signals that will be used to determine the condition of the production equipment components and the sensor systems currently available on the demonstrators.
In Chapter 4, this document proposes a set of guidelines for selecting the sensor system solutions best suited to monitor the condition of use cases’ equipment. Such guidelines are intended to be applicable to different technological fields and will be tested in the context of the end users’ demonstrators.
D3.1Physical Signal and Attribute Analysis related to each use caseThe present document describes the main results obtained after the execution of the task T3.1 Physical Signal and Attribute Analysis, one of the tasks that conform to SP3 Data Analytics for Performance Monitoring and Fault Detection.
This deliverable is structured following a division between the processes carried out in the different use cases by the different partners in the project for the initial analysis of data, alarms and parameters to be used in the training of models, preparation of algorithms, and their later execution while implemented in the totality of the O&M decision support system to be developed in the project SmartWind.
One of the first steps to be carried out in any data analysis project is the initial processing of the input data, to allow the preparation of the initial inputs for the later stages of the process. In the context of the project, data will be captured and treated in an initial process of data cleansing and data quality, implemented as a part of the UC12. After that initial process, the modules responsible for the execution of the different use cases will execute an initial preparation of the data for the proper detection and prediction of problems and failures.
In the same way, the different models being developed in the modules for the implementation of the different use cases will require the processing of signals and their preparation (using clustering, outlier removal, among other techniques) to ensure a proper development of the models.
As a result of the process of analysis of the available magnitudes and alarms for the later detection of issues and failures in each of the subsystems as well as the design and development of the best tools for the initial analysis of them in the context of each of the use cases, some initial preliminary results were achieved, such as potential underperformances or issues detected in magnitudes related to each subsystem under study.
The current dataset is composed of data relative to a total of 5 wind turbines (WT), with a frequency of 10 minutes in a timespan that encompasses a total of 6 months (from January 2020 to June 2020). Wider datasets will allow a more detailed identification of problems to polish the mechanisms and preliminary results presented in this document.