scholarly journals Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6601
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
Charlie Plumley
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Supervisory Control ◽  
Data Acquisition System ◽  
Novel Technique ◽  
Machine Learning Model ◽  
System Data ◽  
Pitch System ◽  
Run Up ◽  
Isolation Forest

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

scholarly journals Comparison of New Anomaly Detection Technique for Wind Turbine Condition Monitoring Using Gearbox SCADA Data

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5152
Author(s):  
Conor McKinnon ◽  
James Carroll ◽  
Alasdair McDonald ◽  
Sofia Koukoura ◽  
David Infield ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Anomaly Detection ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Support Vector ◽  
Specific Training ◽  
Average Accuracy ◽  
Operations And Maintenance ◽  
Novel Method ◽  
Isolation Forest

Anomaly detection for wind turbine condition monitoring is an active area of research within the wind energy operations and maintenance (O & M) community. In this paper three models were compared for multi-megawatt operational wind turbine SCADA data. The models used for comparison were One-Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Elliptical Envelope (EE). Each of these were compared for the same fault, and tested under various different data configurations. IF and EE have not previously been used for fault detection for wind turbines, and OCSVM has not been used for SCADA data. This paper presents a novel method of condition monitoring that only requires two months of data per turbine. These months were separated by a year, the first being healthy and the second unhealthy. The number of anomalies is compared, with a greater number in the unhealthy month being considered correct. It was found that for accuracy IF and OCSVM had similar performances in both training regimes presented. OCSVM performed better for generic training, and IF performed better for specific training. Overall, IF and OCSVM had an average accuracy of 82% for all configurations considered, compared to 77% for EE.

scholarly journals An Improved Feature Selection Method Based on Random Forest Algorithm for Wind Turbine Condition Monitoring

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5654
Author(s):  
Guo Li ◽  
Chensheng Wang ◽  
Di Zhang ◽  
Guang Yang
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Supervisory Control ◽  
Feature Selection Method ◽  
Selection Method ◽  
Random Forest Algorithm ◽  
Gated Recurrent Unit ◽  
Euclidian Distances

Feature selection and dimensionality reduction are important for the performance of wind turbine condition monitoring models using supervisory control and data acquisition (SCADA) data. In this paper, an improved random forest algorithm, namely Feature Simplification Random Forest (FS_RF), is proposed, which is capable of identifying features closely correlated with wind turbine working conditions. The Euclidian distances are employed to distinguish the weight of the same feature among different samples, and its importance is measured by means of the random forest algorithm. The selected features are finally verified by a two-layer gated recurrent unit (GRU) neural network facilitating condition monitoring. The experimental results demonstrate the capacity and effectiveness of the proposed method for wind turbine condition monitoring.

scholarly journals Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3132 ◽  
Author(s):  
Jorge Maldonado-Correa ◽  
Sergio Martín-Martínez ◽  
Estefanía Artigao ◽  
Emilio Gómez-Lázaro
Keyword(s):  
Early Detection ◽  
Literature Review ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Systematic Literature Review ◽  
Supervisory Control ◽  
Wind Farm ◽  
Effective Solution ◽  
Operation And Maintenance ◽  
Research Questions

Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

scholarly journals Fault Detection and Diagnosis Methods for Fluid Power Pitch System Components—A Review

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1305
Author(s):  
Magnus F. Asmussen ◽  
Jesper Liniger ◽  
Henrik C. Pedersen
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Wind Energy ◽  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Fault Detection And Diagnosis ◽  
Fluid Power ◽  
Pitch System ◽  
Fluid Power Systems

Wind turbines have become a significant part of the global power production and are still increasing in capacity. Pitch systems are an important part of modern wind turbines where they are used to apply aerodynamic braking for power regulation and emergency shutdowns. Studies have shown that the pitch system is responsible for up to 20% of the total down time of a wind turbine. Reducing the down time is an important factor for decreasing the total cost of energy of wind energy in order to make wind energy more competitive. Due to this, attention has come to condition monitoring and fault detection of such systems as an attempt to increase the reliability and availability, hereby the reducing the turbine downtime. Some methods for fault detection and condition monitoring of fluid power systems do exists, though not many are used in today’s pitch systems. This paper gives an overview of fault detection and condition monitoring methods of fluid power systems similar to fluid power pitch systems in wind turbines and discuss their applicability in relation to pitch systems. The purpose is to give an overview of which methods that exist and to find areas where new methods need to be developed or existing need to be modified. The paper goes through the most important components of a pitch system and discuss the existing methods related to each type of component. Furthermore, it is considered if existing methods can be used for fluid power pitch systems for wind turbine.

scholarly journals Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 201 ◽  
Author(s):  
Kevin Leahy ◽  
Colm Gallagher ◽  
Peter O’Donovan ◽  
Dominic T. J. O’Sullivan
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Maintenance Strategies ◽  
Scada System ◽  
System Data ◽  
The Common ◽  
Common Problems ◽  
Original Equipment Manufacturers ◽  
Historical Periods ◽  
Operational Data

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting.

scholarly journals Alarms management by supervisory control and data acquisition system for wind turbines

2021 ◽  
Vol 23 (1) ◽  
pp. 110-116
Author(s):  
Isaac Segovia Ramirez ◽  
Behnam Mohammadi-Ivatloo ◽  
Fausto Pedro García Márquez
Keyword(s):  
Data Acquisition ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Supervisory Control ◽  
Principal Component ◽  
Data Acquisition System ◽  
Fault Detection And Diagnosis ◽  
Acquisition System ◽  
False Alarms ◽  
Original Dataset

Wind energy is one of the most relevant renewable energy. A proper wind turbine maintenance management is required to ensure continuous operation and optimized maintenance costs. Larger wind turbines are being installed and they require new monitoring systems to ensure optimization, reliability and availability. Advanced analytics are employed to analyze the data and reduce false alarms, avoiding unplanned downtimes and increasing costs. Supervisory control and data acquisition system determines the condition of the wind turbine providing large dataset with different signals and alarms. This paper presents a new approach combining statistical analysis and advanced algorithm for signal processing, fault detection and diagnosis. Principal component analysis and artificial neural networks are employed to evaluate the signals and detect the alarm activation pattern. The dataset has been reduced by 93% and the performance of the neural network is incremented by 1000% in comparison with the performance of original dataset without filtering process.

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