Risk Assessment of a Wind Turbine: A New FMECA-Based Tool With RPN Threshold Estimation

A wind turbine is a complex system used to convert the kinetic energy of the wind into electrical energy. During the turbine design phase, a risk assessment is mandatory to reduce the machine downtime and the Operation & Maintenance cost and to ensure service continuity. This paper proposes a procedure based on Failure Modes, Effects, and Criticality Analysis to take into account every possible criticality that could lead to a turbine shutdown. Currently, a standard procedure to be applied for evaluation of the risk priority number threshold is still not available. Trying to fill this need, this paper proposes a new approach for the Risk Priority Number (RPN) prioritization based on a statistical analysis and compares the proposed method with the only three quantitative prioritization techniques found in literature. The proposed procedure was applied to the electrical and electronic components included in a Spanish 2 MW on-shore wind turbine

Wind turbine condition monitoring strategy through multiway PCA and multivariate inference

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, a in [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.Peer ReviewedPostprint (published version

A data-mining approach for wind turbine fault detection based on scada data analysis using artificial neural networks

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20-30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm

Diagnostics and prognostics utilising dynamic Bayesian networks applied to a wind turbine gearbox

The UK has the largest installed capacity of offshore wind and this is set to increase significantly in future years. The difficulty in conducting maintenance offshore leads to increased operation and maintenance costs compared to onshore but with better condition monitoring and preventative maintenance strategies these costs could be reduced. In this paper an on-line condition monitoring system is created that is capable of diagnosing machine component conditions based on an array of sensor readings. It then informs the operator of actions required. This simplifies the role of the operator and the actions required can be optimised within the program to minimise costs. The program has been applied to a gearbox oil testbed to demonstrate its operational suitability. In addition a method for determining the most cost effective maintenance strategy is examined. This method uses a Dynamic Bayesian Network to simulate the degradation of wind turbine components, effectively acting as a prognostics tool, and calculates the cost of various preventative maintenance strategies compared to purely corrective maintenance actions. These methods are shown to reduce the cost of operating wind turbines in the offshore environment

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Intelligent Data Fusion for Applied Decision Support

Data fusion technologies are widely applied to support a real-time decision-making in complicated, dynamically changing environments. Due to the complexity in the problem domain, artificial intelligent algorithms, such as Bayesian inference and particle swarm optimization, are employed to make the decision support system more adaptive and cognitive. This dissertation proposes a new data fusion model with an intelligent mechanism adding decision feedback to the system in real-time, and implements this intelligent data fusion model in two real-world applications. The first application is designing a new sensor management system for a real-world and highly dynamic air traffic control problem. The main objective of sensor management is to schedule discrete-time, two-way communications between sensors and transponder-equipped aircraft over a given coverage area. Decisions regarding allocation of sensor resources are made to improve the efficiency of sensors and communications, simultaneously. For the proposed design, its loop nature takes account the effect of the current sensor model into the next scheduling interval, which makes the sensor management system able to respond to the dynamically changing environment in real-time. Moreover, it uses a Bayesian network as the mission manager to come up with operating requirements for each region every scheduling interval, so that the system efficiently balances the allocation of sensor resources according to different region priorities. As one of this dissertation\u27s contribution in the area of Bayesian inference, the resulting Bayesian mission manager is shown to demonstrate significant performance improvement in resource usage for prioritized regions such as a runway in the air traffic control application for airport surfaces. Due to wind\u27s importance as a renewable energy resource, the second application is designing an intelligent data-driven approach to monitor the wind turbine performance in real-time by fusing multiple types of maintenance tests, and detect the turbine failures by tracking the turbine maintenance statistics. The current focus has been on building wind farms without much effort towards the optimization of wind farm management. Also, under performing or faulty turbines cause huge losses in revenue as the existing wind farms age. Automated monitoring for maintenance and optimizing of wind farm operations will be a key element in the transition of wind power from an alternative energy form to a primary form. Early detection and prediction of catastrophic failures helps prevent major maintenance costs from occurring as well. I develop multiple tests on several important turbine performance variables, such as generated power, rotor speed, pitch angle, and wind speed difference. Wind speed differences are particularly effective in the detection of anemometer failures, which is a very common maintenance issue that greatly impacts power production yet can produce misleading symptoms. To improve the detection accuracy of this wind speed difference test, I discuss a new method to determine the decision boundary between the normal and abnormal states using a particle swarm optimization (PSO) algorithm. All the test results are fused to reach a final conclusion, which describes the turbine working status at the current time. Then, Bayesian inference is applied to identify potential failures with a percentage certainty by monitoring the abnormal status changes. This approach is adaptable to each turbine automatically, and is advantageous in its data-driven nature to monitor a large wind farm. This approach\u27s results have verified the effectiveness of detecting turbine failures early, especially for anemometer failures

Predictive maintenance with industrial sensor data

The Norwegian Ministry of Petroleum and Energy Commissions report shows that the government is making a large step closer to its ambition of allocating regions for 30,000 MW offshore wind via way of means of 2040. According to a report by IRENA, offshore wind operation and maintenance (O & M) costs make up a significant portion of the overall cost of electricity for offshore wind farms in G20 countries, ranging from 16-25%. To address this issue, it is essential to explore methods for improving operational reliability and reducing the maintenance costs of wind turbines. One promising approach is predictive maintenance, which involves leveraging data collected from sensors already equipped with the turbines to detect and address potential issues before they become more serious. Predictive maintenance is important in wind farms to reduce downtime and optimize the performance of wind turbines. Various rotating components in wind turbines make them complicated machinery, and if any of those parts fails, it can cause the entire turbine to shut down. This can result in lost revenue for the wind farm operator and lead to higher maintenance costs if the problem is not addressed quickly. This can be possible through a Supervisory Control and Data Acquisition (SCADA) system, which collects and analyzes data from various turbine components. We have developed a method for detecting and monitoring failures in critical components such as the gearbox and generator, based on historical SCADA data. Our approach utilizes machine learning models, specifically extreme gradient boosting (XGBoost), and has been tested on two real-world case studies involving eight different turbines. The outcomes show both the effectiveness and usefulness of our technique for boosting wind turbine reliability and minimizing maintenance costs

Automated On-line Fault Prognosis for Wind Turbine Monitoring using SCADA data

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective