Identifying health status of wind turbines by using self organizing maps and interpretation-oriented post-processing tools

Identifying the health status of wind turbines becomes critical to reduce the impact of failures on generation costs (between 25–35%). This is a time-consuming task since a human expert has to explore turbines individually. Methods: To optimize this process, we present a strategy based on Self Organizing Maps, clustering and a further grouping of turbines based on the centroids of their SOM clusters, generating groups of turbines that have similar behavior for subsystem failure. The human expert can diagnose the wind farm health by the analysis of a small each group sample. By introducing post-processing tools like Class panel graphs and Traffic lights panels, the conceptualization of the clusters is enhanced, providing additional information of what kind of real scenarios the clusters point out contributing to a better diagnosis. Results: The proposed approach has been tested in real wind farms with different characteristics (number of wind turbines, manufacturers, power, type of sensors, ...) and compared with classical clustering. Conclusions: Experimental results show that the states healthy, unhealthy and intermediate have been detected. Besides, the operational modes identified for each wind turbine overcome those obtained with classical clustering techniques capturing the intrinsic stationarity of the data.Peer ReviewedPostprint (published version

Wind turbine condition assessment through power curve copula modeling

Power curves constructed from wind speed and active power output measurements provide an established method of analyzing wind turbine performance. In this paper it is proposed that operational data from wind turbines are used to estimate bivariate probability distribution functions representing the power curve of existing turbines so that deviations from expected behavior can be detected. Owing to the complex form of dependency between active power and wind speed, which no classical parameterized distribution can approximate, the application of empirical copulas is proposed; the statistical theory of copulas allows the distribution form of marginal distributions of wind speed and power to be expressed separately from information about the dependency between them. Copula analysis is discussed in terms of its likely usefulness in wind turbine condition monitoring, particularly in early recognition of incipient faults such as blade degradation, yaw and pitch errors

Intelligent estimation of wind farm performance with direct and indirect ‘point’ forecasting approaches integrating several NWP models

Reliable wind power forecasting is essential for profitably trading wind energy in the electricity market and efficiently integrating wind-generated electricity into the power grids. In this paper, we propose short- and medium-term wind power forecasting systems targeted to the Nordic energy market, which integrate inputs on the wind flow conditions from three numerical weather prediction sources. A point forecasting scheme is adopted, which forecasts the power at the individual turbine level. Both direct and indirect forecasting approaches are considered and compared. An automated machine-learning pipeline, built and optimized using genetic programming, is implemented for developing the proposed forecasting models. The turbine level power forecasts using different approaches are then combined into a single forecast using a weighting method based on recent forecast errors. These are then aggregated for the wind farm level power estimates. The proposed forecasting schemes are implemented with data from a Norwegian wind farm. We found that in both the direct and indirect forecasting approaches, the forecasting errors could be reduced between 8% and 22%, while inputs from several NWP sources are used together. The wind downscaling model, which is used in the indirect forecasting approach, could significantly contribute to the model's accuracy. The performance of both the direct and indirect forecasting schemes is comparable for the studied wind farm.publishedVersionPaid Open Acces

Computer Vision in Wind Turbine Blade Inspections: An Analysis of Resolution Impact on Detection and Classification of Leading-Edge Erosion

Wind turbines, as critical components of the renewable energy industry, present unique maintenance challenges, particularly in remote or challenging locations such as offshore wind farms. These are amplified in the inspection of leading-edge erosion on wind turbine blades, a task still largely reliant on traditional methods. Emerging technologies like computer vision and object detection offer promising avenues for enhancing inspections, potentially reducing operational costs and human-associated risks. However, variability in image resolution, a critical factor for these technologies, remains a largely underexplored aspect in the wind energy context. This study explores the application of machine learning in detecting and categorizing leading edge erosion damage on wind turbine blades. YOLOv7, a state-of-the-art object detection model, is trained with a custom dataset consisting of images displaying various forms of leading edge erosion, representing multiple categories of damage severity. Trained model is tested on images acquired with three different tools, each providing images with a different resolution. The effect of image resolution on the performance of the custom object detection model is examined. The research affirms that the YOLOv7 model performs exceptionally well in identifying the most severe types of LEE damage, usually classified as Category 3, characterized by distinct visual features. However, the model's ability to detect less severe damage, namely Category 1 and 2, which are crucial for early detection and preventive measures, exhibits room for improvement. The findings point to a potential correlation between input image resolution and detection confidence in the context of wind turbine maintenance. These results stress the need for high-resolution images, leading to a discussion on the selection of appropriate imaging hardware and the creation of machine learning-ready datasets. The study thereby emphasizes the importance of industry-wide efforts to compile standardized image datasets and the potential impact of machine learning techniques on the efficiency of visual inspections and maintenance strategies. Future directions are proposed with the ultimate aim of enhancing the application of artificial intelligence in wind energy maintenance and management, enabling more efficient and effective operational procedures, and driving the industry towards a more sustainable future

Condition Monitoring of Wind Turbines Using Intelligent Machine Learning Techniques

Wind Turbine condition monitoring can detect anomalies in turbine performance which have the potential to result in unexpected failure and financial loss. This study examines common Supervisory Control And Data Acquisition (SCADA) data over a period of 20 months for 21 pitch regulated 2.3 MW turbines and is presented in three manuscripts. First, power curve monitoring is targeted applying various types of Artificial Neural Networks to increase modeling accuracy. It is shown how the proposed method can significantly improve network reliability compared with existing models. Then, an advance technique is utilized to create a smoother dataset for network training followed by establishing dynamic ANFIS network. At this stage, designed network aims to predict power generation in future hours. Finally, a recursive principal component analysis is performed to extract significant features to be used as input parameters of the network. A novel fusion technique is then employed to build an advanced model to make predictions of turbines performance with favorably low errors

Improving Ammonia Emission Modeling and Inventories by Data Mining and Intelligent Interpretation of the National Air Emission Monitoring Study Database

Ammonia emission is one of the greatest environmental concerns in sustainable agriculture development. Several limitations and fundamental problems associated with the current agricultural ammonia emission modeling and emission inventories have been identified. They were associated with a significant disconnection between field monitoring data and knowledge about the data. Comprehensive field measurement datasets have not been fully exploited for scientific research and emission regulations. This situation can be considerably improved if the currently available data are better interpreted and the new knowledge is applied to update ammonia emission modeling techniques. The world\u27s largest agricultural air quality monitoring database with more than 2.4 billion data points has recently been created by the United States\u27 National Air Emission Monitoring Study. New approaches of data mining and intelligent interpretation of the database are planned to uncover new knowledge and to answer a series of questions that have been raised. The expected results of this new research idea include enhanced fundamental understanding of ammonia emissions from animal agriculture and improved accuracy and scope in regional and national ammonia emission inventories

Improving Ammonia Emission Modeling and Inventories by Data Mining and Intelligent Interpretation of the National Air Emission Monitoring Study Database

Ammonia emission is one of the greatest environmental concerns in sustainable agriculture development. Several limitations and fundamental problems associated with the current agricultural ammonia emission modeling and emission inventories have been identified. They were associated with a significant disconnection between field monitoring data and knowledge about the data. Comprehensive field measurement datasets have not been fully exploited for scientific research and emission regulations. This situation can be considerably improved if the currently available data are better interpreted and the new knowledge is applied to update ammonia emission modeling techniques. The world’s largest agricultural air quality monitoring database with more than 2.4 billion data points has recently been created by the United States’ National Air Emission Monitoring Study. New approaches of data mining and intelligent interpretation of the database are planned to uncover new knowledge and to answer a series of questions that have been raised. The expected results of this new research idea include enhanced fundamental understanding of ammonia emissions from animal agriculture and improved accuracy and scope in regional and national ammonia emission inventories

Maintenance management of tractors and agricultural machinery: Preventive maintenance systems

Agricultural machinery maintenance has a crucial role for successful agricultural production.  It aims at guaranteeing the safety of operations and availability of machines and related equipment for cultivation operation.  Moreover, it is one major cost for agriculture operations.  Thus, the increased competition in agricultural production demands maintenance improvement, aiming at the reduction of maintenance expenditures while keeping the safety of operations.  This issue is addressed by the methodology presented in this paper.  So, the aim of this paper was to give brief introduction to various preventive maintenance systems specially condition-based maintenance (CBM) techniques, selection of condition monitoring techniques and understanding of condition monitoring (CM) intervals, advancement in CBM, standardization of CBM system, CBM approach on agricultural machinery, advantages and disadvantages of CBM.  The first step of the methodology consists of concept condition monitoring approach for the equipment preventive maintenance; its purpose is the identification of state-of-the-art in the CM of agricultural machinery, describing the different maintenance strategies, CM techniques and methods.  The second step builds the signal processing procedure for extracting information relevant to targeted failure modes.   Keywords: agricultural machinery, fault detection, fault diagnosis, signal processing, maintenance managemen

AI-driven approaches for optimizing the energy efficiency of integrated energy system

To decarbonize the global energy system and replace the unidirectional architecture of existing grid networks, integrated and electrified energy systems are becoming more demanding. Energy integration is critical for renewable energy sources like wind, solar, and hydropower. However, there are still specific challenges to overcome, such as their high reliance on the weather and the complexity of their integrated operation. As a result, this research goes through the study of a new approach to energy service that has arisen in the shape of data-driven AI technologies, which hold tremendous promise for system improvement while maximizing energy efficiency and reducing carbon emissions. This research aims to evaluate the use of data-driven AI techniques in electrical integrated energy systems, focusing on energy integration, operation, and planning of multiple energy supplies and demand. Based on the formation point, the main research question is: "To what extent do AI algorithms contribute to attaining greater efficiency of integrated grid systems?". It also included a discussion on four key research areas of AI application: Energy and load prediction, fault prediction, AI-based technologies IoT used for smart monitoring grid system optimization such as energy storage, demand response, grid flexibility, and Business value creation. The study adopted a two-way approach that includes empirical research on energy industry expert interviews and a Likert scale survey among energy sector representatives from Finland, Norway, and Nepal. On the other hand, the theoretical part was from current energy industry optimization models and a review of publications linked to a given research issue. The research's key findings were AI's significant potential in electrically integrated energy systems, which concluded AI's implication as a better understanding of energy consumption patterns, highly effective and precise energy load and fault prediction, automated energy management, enhanced energy storage system, more excellent business value, a smart control center, smooth monitoring, tracking, and communication of energy networks. In addition, further research directions are prospects towards its technical characteristics on energy conversion

Computation of loop flows in electric grids with high wind energy penetration

2013 Fall.Includes bibliographical references.In a deregulated electricity market, the financial transmission rights (FTRs) and the bid-sell principle for energy trades are used to determine the expected power flows on transmission lines. Expected power flows are calculated by applying the superposition theorem on the approved electronic tags (e-tags). Multiple parallel paths in interconnected networks lead to division of power flows determined by the impedances of the parallel paths and the physical laws of electricity. The actual power flows in the network do not conform to the market expectations leading to unscheduled flows (USF) on transmission lines. USF have historically been estimated and accommodated deterministically for a given set of e-tags. However, wide-area interconnections experience variability and uncertainty due to a significant penetration of wind energy connected at the transmission level, thus imparting a stochastic nature to USF. A linear model, from the literature, has been adopted to model USF using a mathematical artifact called `minor loop flows'. This research develops an automated framework that provides accurate estimates of loop flows suitable for both market and network level accommodation of variable USF. This generic framework will be applicable to any power transmission network with intermittent energy resources. A loop detection algorithm (LDA) based on graph theory is proposed to detect loops in a transmission network of any size. The LDA is formulated as a modification of the A-star (A*) algorithm, the lowest ancestor theorem, and Dijkstra's algorithm. The LDA has an order of complexity of V2, where V is the total number of vertices or buses in the network under consideration. An application of a geographical information systems (GIS) technique has been established to obtain the transmission line layouts. The outcome of the LDA (i.e., minor loops) and line layouts (i.e., azimuth) are processed to compute the incidence matrix of the estimator. The variability due to the penetration of wind energy is accounted in the proposed framework using the probabilistic load flow analysis based on Monte Carlo simulations. Three techniques - ordinary least squares (OLS), analytic ridge regression (RR), and robust regression (M-estimators) - are used to estimate minor loop flows. The estimation techniques adhere to the auto-correction of the quality of estimates in case of ill-conditioning of the incidence matrix. Accuracy of loop flow estimates is highly significant, as they may be used for assigning economic responsibility of USF in electricity markets. Wind power generation companies (WGENCOs) employ forecasting models to participate in the primary electricity markets. Forecasting models used to predict the output of wind power plants are inherently erroneous and hence, their impacts on USF are studied. The impact of forecasting errors associated with the output of wind plants is investigated using the concept of prediction intervals rather than point accurate forecasts. Loop flow estimates corresponding to the prediction intervals of power output of wind power plants are computed to provide statistical bounds. The proposed framework is tested on the IEEE 14-bus and the IEEE 30-bus standard test systems with suitable modifications to represent wind energy penetration. Accurate loops are detected for the aforementioned test systems using the LDA. Thus, an automated and generic computation of loop flows is proposed along with a step-wise demonstration on IEEE test systems is provided. Future work and concluding remarks summarize the research work in this dissertation