Evaluation of an offshore wind farm computational fluid dynamics model against operational site data

Modelling wind turbine wake effects at a range of wind speeds and directions with actuator disk (AD) models can provide insight but also be challenging. With any model it is important to quantify the level of error, but this can also present a challenge when comparing a steady-state model to measurement data with scatter. This paper models wind flow in a wind farm at a range of wind speeds and directions using an AD implementation. The results from these models are compared to data collected from the actual farm being modelled. An extensive comparison is conducted, constituted from 35 cases where two turbulence models, the standard k-ε and k-ω SST are evaluated. The steps taken in building the models as well as processes for comparing the AD computational fluid dynamics (CFD) results to real-world data using the regression models of ensemble bagging and Gaussian process are outlined. Turbine performance data and boundary conditions are determined using the site data. Modifications to an existing opensource AD code are shown so that the predetermined turbine performance can be implemented into the CFD model. Steady state solutions are obtained with the OpenFOAM CFD solver. Results are compared in terms of velocity deficit at the measurement locations. Using the standard k-ε model, a mean absolute error for all cases together of roughly 8% can be achieved, but this error changes for different directions and methods of evaluating it

Applicability of machine learning approaches for structural damage detection of offshore wind jacket structures based on low resolution data

Structural damage in offshore wind jacket support structures are relatively unlikely due to the precautions taken in design but it could imply dramatic consequences if undetected. This work explores the possibilities of damage detection when using low resolution data, which are available with lower costs compared to dedicated high-resolution structural health monitoring. Machine learning approaches showed to be generally feasible for detecting a structural damage based on SCADA data collected in a simulation environment. Focus is here given to investigate model uncertainties, to assess the applicability of machine learning approaches for reality. Two jacket models are utilised representing the as-designed and the as-installed system, respectively. Extensive semi-coupled simulations representing different operating load cases are conducted to generate a database of low-resolution signals serving the machine learning training and testing. The analysis shows the challenges of classification approaches, i.e. supervised learning aiming to separate healthy and damage status, in coping with the uncertainty in system dynamics. Contrarily, an unsupervised novelty detection approach shows promising results when trained with data from both, the as-designed and the as-installed system. The findings highlight the importance of investigating model uncertainties and careful selection of training data

Geothermal exploration in the Sani-Afytos area of the Kassandra peninsula (Chalkidiki peninsula, Northern Greece)

Η περιοχή Σάνης - Αφύτου της Χερσονήσου Κασσάνδρας (Χα).κιδική) απετέλεσε περιοχή συστηματικής γεωθερμικής έρευνας. Από την αξιολόγηση των δεδομένων βαθιών γεωτρήσεων έρευνας υδρογονανθράκων διαπιστώθηκε το σημαντικό πάχος (μέχρι περίπου 3600 m) των Παλαιογενών, Νεογενών και Τεταρτογενών ιζημάτων που καλύπτουν το μεταμορφωμένο Μεσοζωικό (κυρίως ανθρακικό) υπόβαθρο της περιοχής. Η συστηματική θερμομετρική έρευνα κατέδειξε την παρουσία υπόθερμων; νερών (20-28°C) σε βάθη μέχρι 300 m και την χωρική κατανομή της θερμοκρασίας σε βάθη 150 και 200 m σύμφωνα με τις κύριες τεκτονικές δομές ΒΔ-ΝΑ και ΒΑ-ΝΔ διεύθυνσης της περιοχής. Με την ανόρυξη γεωθερμικών γεωτρήσεων (ερευνητικών και παραγωγής) βάθους 422-583 m εντοπίσθηκαν νερά 31,7-36°C στα Ανω-Μειοκανικά ιζήματα. Η μέση τιμή της γεωθερμικής βαθμίδας υπολογίστηκε σε 3-4°C/100 m. Παραγωγική γεώτρηση βάθους 520 m δίνει νερά 34°C με δυνατότητα παροχής περίπου 50 m3/h. Τα γεωθερμικά νερά είναι Na-HCO} και Na-CI με Σ.Δ.Α. 0,89-2,03 g/l. Με τη βοήθεια χημικών γεωθερμομέτρων εκτιμάται ότι η θερμοκρασία του βαθιού γεωθερμικού ρευστού είναι της τάξης των 80- 100°C. Η παρουσία αέριας φάσης (77% κ.ό. CH4, 21,8% κ.ό. Ν2) διαπιστώθηκε σε μία από τις ερευνητικές γεωτρήσεις. Αποτέλεσμα της γεωθερμικής έρευνας ήταν ο χαρακτηρισμός της περιοχής ως «γεωθερμικό πεδίο Σάνης - Αφύτου» (Φ.Ε.Κ. 1012/τ.Β/19-7-2005) και οι προοπτικές ανάπτυξης με χρήση των γεωθερμικών ρευστών στον τουρισμό και σε άλλες δραστηριότητες.The Sani-Afytos area in the Kassandra Peninsula (Chalkidiki) was the area of systematic geothermal exploration. Based on deep oil borehole data, the Paleogene, Neogene and Quaternary sediments show significant thickness reaching 3600 m and cover the metamorphosed Mesozoic, mainly carbonate, basement. The detailed water temperature investigation proved the presence of sub-thermal waters (20-28°C) at depths up to 300 m and the spatial distribution of the isothermal curves at depths of 150 and 200 m according to the main NW-SE and SE-NW tectonic structures of the area. Through the construction of geothermal exploration and production wells at depths of 422-583 m, thermal waters of 31.7-36°C were detected within the Upper Miocene sediments. The average value of the geothermal gradient was calculated to be 3-4°CI 100 m. One production well of 520 m depth provides waters of 34°C while its potential flow rate is approximately 50 m /h. The geothermal waters were classified in Na-HCOi and Na-CI types of waters with TD. S 0.89-2.03 g/l. With the aid of chemical geothermometers the deep temperature was estimated to be 80-100°C. In one exploration well, the presence of gas phase (77% v/v CH4, 21.8% v/v N2) was detected. The geothermal exploration resulted in the characterization of the area as the "geothermal field of Sani-Afytos" and in the prospective development using the geothermal fluids in the tourism and other activities

Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications

This work presents a comprehensive review and discussion of the identification of critical components of the currently installed and next generation of offshore wind turbines. A systematic review on the reliability, availability, and maintainability data of both offshore and onshore wind turbines is initially performed, collecting the results from 24 initiatives, at system and subsystem level. Due to the scarcity of data from the offshore wind industry, the analysis is complemented with the extensive experience from onshore structures. Trends based on the deployment parameters for the influence of design characteristics and environmental conditions on the onshore wind turbines' reliability and availability are first investigated. The estimation of the operational availability for a set of offshore wind farm scenarios allowed a comparison with the recently published performance statistics and the discussion of the integrity of the data available to date. The failure statistics of the systems deployed offshore are then discussed and compared to the onshore ones, with regard to their normalised results. The availability calculations supported the hypothesis of the negative impact of the offshore environmental conditions on the reliability figures. Nonetheless, similarities in the reliability figures of the blade adjustment system and the maintainability of the power generation and the control systems are outlined. Finally, to improve the performance prediction of future offshore projects, recommendations on the effort worth putting into research and data collection are provided

Feasibility of machine learning algorithms for classifying damaged offshore jacket structures using SCADA data

The best practise for structural damage detection currently relies on the installation of structural health monitoring systems for the collection of dedicated high frequency measurements. Switching to the employment of the wind turbine's SCADA (Supervisory Control and Data Acquisition) signals and their commonly recorded low frequency statistics can lead to a reduction in the number of ad-hoc monitoring sensors and quantity of data required. In this paper, aero-hydro-servo-elastic simulations for a model of a turbine are used to assess its loads and any changes in the dynamics under healthy state and a damaged configuration case study. To prove the feasibility of the damage detection through low-resolution data, the statistics of the typically recorded signals from the SCADA and the structural monitoring systems are fed into a database for training and testing of classification algorithms. The ability of the machine learning models to generalise the classification for both stochasticity and uncertainties in the environmental conditions are tested. Decision tree-based classifiers showed the capability to capture the damage for the majority of the operating conditions considered. Though the setup of the traditional SCADA sensors had to be supplemented with an additional structural health monitoring sensor, the detection of the damage has been shown feasible by referring to low-frequency statistics only

The effect of marine growth dynamics in offshore wind turbine support structures

Offshore Wind Turbine (OWT) support structures are invariably subject to colonisation by marine organisms, which are not spatially or temporally linear. Marine Growth (MG) varies based on location and season, and with structural and material characteristics. MG is a major consideration for engineers. As organisms settle on the structure they may increase surface roughness and cross-sectional area, altering drag and inertia coefficients and increasing hydrodynamic loading. Furthermore, the added mass from MG also influences structural integrity. As such, there is considerable uncertainty surrounding the response of OWTs to MG, as this phenomenon is often overlooked in FEA modelling. This paper uses the parametric FEA model of an OWT support structure developed in (Martinez-Luengo, Kolios, and Wang 2017) to analyse how different growth rates and patterns of zonation of MG affect the structural integrity of the system. MG has a great impact in the fatigue life of the structure, as a reduction of 58.6-59.2% is presented in the baseline scenarios