• Energy Research

The development of accurate techno-economic models is crucial to boost the commercialisation of floating offshore wind farms. However, conventional techno-economic models oversimplify operation and maintenance (O&M) aspects, neglecting key maintenance factors, such as component failure rates, metocean conditions, repair times, maintenance vessels and ports. To address this limitation, this paper presents an O&M-aware techno-economic model that comprehensively incorporates the most relevant maintenance factors and evaluates their impacts on site-identification across the North Sea and the Iberian Peninsula based on diverse O&M strategies. Results reveal that operational expenditure can contribute significantly to the levelised cost of energy, ranging from 22% to 50% in the North Sea and 19% to 46% in the Iberian Peninsula. Furthermore, results demonstrate that suitable sites vary based on O&M strategy: preventive strategies favour areas with abundant wind resources like northern Scotland, Norway and Galicia, whereas corrective strategy prioritise sites with less severe metocean conditions, such as southern Scotland and extensive regions in the Mediterranean Sea, including the Gulf of Roses and the Alboran Sea. Finally, the downtime of turbines, an aspect traditionally neglected in techno economic frameworks, emerges as a key factor for accurate techno-economic assessment and site-identification.

Condition monitoring of power transformers is crucial for the reliable and cost-effective operation of the power grid. The health index (HI) formulation is a pragmatic approach to combine multiple information sources and generate a consistent health state indicator for asset management planning. Generally, existing transformer HI methods are based on expert knowledge or data-driven models of specific transformer subsystems. However, the effect of uncertainty is not considered when integrating expert knowledge and data-driven models for the system-levelHI estimation. With the increased dynamic and non-deterministic engineering problems, the sources of uncertainty are increasing across power and energy applications, e.g. electric vehicles with new dynamic loads or nuclear power plants with de-energized periods, and transformer health assessment under uncertainty is becoming critical for accurate condition monitoring. In this context, this paper presents a novel soft computing driven probabilistic HI framework for transformer health monitoring. The approach encapsulates data analytics and expert knowledge along with different sources of uncertainty and infers a transformer HI value with confidence intervals for decision-making under uncertainty. Using real data from a nuclear power plant, the proposed framework is compared with traditional HI implementations and results confirm the validity of the approach for transformer health assessment.

The digital era offers many opportunities to the wind energy industry and research community. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration method called WeDoWind was developed in recent work. The main innovation of this method is the way it creates tangible incentives to motivate and empower different types of people from all over the world to actually share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA data based wind turbine fault detection models are investigated by carrying out a new case study, the "WinJi Gearbox Fault Detection Challenge", based on the WeDoWind Method. Six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to have a high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. They can be used by the sector in order to improve model evaluation and data sharing in the future.

This paper evaluates how operation and maintenance (O&M) factors affect energy production and optimal deployment sites for floating offshore wind farms (FOWs) in the North Sea and the Iberian Peninsula. The geospatial analysis incorporates reliability, maintainability, accessibility, and availability aspects, and evaluates their impact on energy production. The results demonstrate that O&M factors have a significant impact on the final energy production and therefore on the identification of optimal deployment sites, both quantitatively and qualitatively. In the North Sea, promising deployment sites are identified in regions with lower wind resources but shorter turbine downtime, such as Denmark, Germany and southern Scotland. In the Iberian Peninsula, areas with high resource potential, such as the northwest Spanish and Portuguese coasts, may be less appealing than the less powerful Mediterranean regions due to lower maintainability. In particular, the efficiency of future FOW farms in the North Sea and Atlantic Ocean regions of the Iberian Peninsula heavily relies on vessel operational limits for major repairs. Increasing the significant wave height limit for major repairs from 1.5 m to 2 m results in an average capacity factor increment of 2.54% across ScotWind farms and over 6% along the northwest coast of the Iberian Peninsula.

Accurate lifetime prediction of transformers operated in power grids with renewable energy systems is a challenging task because it requires a large amount of data that is not usually available. In the case of wind energy, this complexity is intensified with the stochastic ageing process influenced by the intermittency of the wind and weather conditions. Existing models make use of detailed power topologies to evaluate transformer stress profiles and associated degradation. However, this modelling approach requires high computational resources and long simulation times. In this context, this paper presents a lifetime prediction model for transformers designed through probabilistic machine learning, thermal modelling and ageing analysis. The proposed model is compared with synthetic wind-to-power detailed simulations of a wind farm and validated with real data. The lifetime prediction is evaluated with different mission profile estimates and results show that the accuracy of the probabilistic machine learning model is very high, with an error of 0.47% for the median value and 80% prediction interval errors within 6%–7% with respect to observations. Moreover, there is a substantial reduction in the simulation time and memory requirements when compared to the synthetic model. A detailed sensitivity analysis demonstrates the influence on transformer ageing of different overloading strategies, thermal constants and the geographic location of the wind farm.

The online Remaining Useful Life (RUL) estimation of underground cables and their reliability analysis requires obtaining the cable failure time probability distribution. Monte Carlo (MC) simulations of complex thermal heating and electro-thermal degradation models can be employed for this analysis, but uncertainties need to be considered in the simulations, to produce accurate RUL expectation values and confidence margins for the results. The process requires performing large simulation sets, based on past temperature or load measurements and future load predictions. Field Programmable Gate Arrays (FPGAs) permit accelerating simulations for live analysis, but the thermal models involved are complex to be directly implemented in hardware logic. A new standalone FPGA architecture has been proposed for the fast and on-site degradation and reliability analysis of underground cables, based on MC simulation, and the effect of load uncertainties on the predicted cable End Of Life (EOL) has been analyzed from the results.

Renewable energies are a key element of the modern sustainable development. They play a key role in contributing to the reduction of the impact of fossil sources and to the energy supply in remote areas where the electrical grid cannot be reached. Due to the intermittent nature of the primary renewable resource, the feasibility assessment, the performance evaluation and the lifecycle management of a renewable power plant are very complex activities. In order to achieve a more accurate system modelling, improve the productivity prediction and better plan the lifecycle management activities, the modelling of a renewable plant may consider not only the physical process of energy transformation, but also the stochastic variability of the primary resource and the degradation mechanisms that affect the aging of the plant components resulting, eventually, in the failure of the system. This paper presents a modelling approach which integrates both the deterministic and the stochastic nature of renewable power plants using a novel methodology inspired from reliability engineering: the Stochastic Hybrid Fault Tree Automaton. The main steps for the design of a renewable power plant are discussed and implemented to estimate the energy production of a real photovoltaic power plant by means of a Monte Carlo simulation process. The proposed approach, modelling the failure behavior of the system, helps also with the evaluation of other key performance indicators like the power plant and the service availability.

Climate change is expected to worsen the frequency, intensity, and impacts of extreme weather events. Renewable energy sources (RESs) play a key role in the decarbonization process to decelerate climate change effects. However, extreme events pose a significant threat to renewable energy infrastructure. Accordingly, understanding the impacts of extremes on RESs becomes crucial to ensure the reliability of power grids. In this context, this research presents a novel probabilistic risk assessment framework to evaluate the degradation of wind turbine transformers (WTTs) and photovoltaic (PV) panels in the face of extreme weather conditions. The framework uses a Gaussian copula to model the joint probability of extreme events, effectively incorporating multivariate phenomena. Case studies involving WTTs and PV panels operated in different wind and solar power plants, illustrate the effectiveness of the proposed methodology, demonstrating its ability to capture the combined influence of different meteorological variables on degradation rates. These results underscore the potential of this framework to assess weather-related risks in renewable energy systems, thereby enhancing their resilience and reliability.

The contribution of renewable energies to the reduction of the impact of fossil fuels sources and especially energy supply in remote areas has occupied a role more and more important during last decades. The estimation of renewable power plants performances by means of deterministic models is usually limited by the innate variability of the energy resources. The accuracy of energy production forecasting results may be inadequate. An accurate feasibility analysis requires taking into account the randomness of the primary resource operations and the effect of component failures in the energy production process. This paper treats a novel approach to the estimation of energy production in a real photovoltaic power plant by means of dynamic reliability analysis based on Stochastic Hybrid Fault Tree Automaton (SHyFTA). The comparison between real data, deterministic model and SHyFTA model confirm how the latter better estimate energy production than deterministic model.