• Energy Research

Abstract The number of grid-connected large-scale solar photovoltaic (PV) power plants has increased significantly in the last 10 years, which results in high PV power penetration into the grid. Especially for the wide-area spatially distributed countries, power ramp in one PV plant can be balanced with another PV power plant generation. This has been studied in the literature for short term horizons for high-frequency data. In this study, hourly simulation data are analysed by Kendall's correlation coefficient, unsupervised and rule-based machine learning algorithms for spatio-temporal operational balancing constraints. Association rules generated by using the Apriori algorithm provide power ramp direction maps for Spatio-Temporal analysis. The K-means clustering (based on the Hartigan-Wong algorithm) is used for unsupervised learning application for the spatio-temporal relations for solar PV ramp zones. The proposed model can be used as a fast and effective decision-making tool with qualitative results for the system operator with minimal expert knowledge, while they can be also integrated to optimal power flow analysis constraints. This study introduces the first successful application of association rules integrated with the K-means technique for the Solar PV spatio-temporal balancing purpose to the best of our knowledge.

Key performance indicators (KPI) are tools for measuring the progress of a business towards its goals. Although wind energy is now a mature technology, there is a lack of well-defined best practices to asses the performance of a wind farm (WF) during the operation and maintenance (O&M) phase; processes and tools of asset management, such as KPIs, are not yet well-established. This paper presents a review of the major existing indicators used in the O&M of wind farms (WFs), as such information is not available in the literature so far. The different stakeholders involved in the O&M phase are identified and analysed together with their interests, grouped into five categories. A suggestion is made for the properties that KPIs should exhibit. For each category, major indicators that are currently in use are reviewed, discussed and verified against the properties defined. Finally, we propose a list of suitable KPIs that will allow stakeholders to have a better knowledge of an operating asset and make informed decisions. It is concluded that more detailed studies of specific KPIs and the issues of their implementation are probably needed.

While many operation and maintenance (O&M) decision support systems (DSS) have been already proposed, a serious research need still exists for wind farm O&M scheduling. O&M planning is a challenging task, as maintenance teams must follow specific procedures when performing their service, which requires working at height in adverse weather conditions. Here, an automated maintenance programming framework is proposed based on real case studies considering available wind speed and wind gust data. The methodology proposed consists on finding the optimal intervention time and the most effective execution order for maintenance tasks and was built on information from regular maintenance visit tasks and a corrective maintenance visit. The objective is to find possible schedules where all work orders can be performed without breaks, and to find out when to start in order to minimise revenue losses (i.e. doing maintenance when there is least wind). For the DSS, routine maintenance tasks are grouped using the findings of an agglomerative nesting analysis. Then, the task execution windows are searched within pre-planned maintenance day.

The need for cost effective operation and maintenance (O&M) strategies in wind farms has risen significantly with the growing wind energy sector. In order to decrease costs, current practice in wind farm O&M is switching from corrective and preventive strategies to rather predictive ones. Anticipating wind turbine (WT) failures requires sophisticated models to understand the complex WT component degradation processes and to facilitate maintenance decision making. Environmental conditions and their impact on WT reliability play a significant role in these processes and need to be investigated profoundly. This paper is presenting a framework to assess and correlate weather conditions and their effects on WT component failures. Two approaches, using (a) supervised and (b) unsupervised data mining techniques are applied to pre-process the weather and failure data. An apriori rule mining algorithm is employed subsequently, in order to obtain logical interconnections between the failure occurrences and the environmental data, for both approaches. The framework is tested using a large historical failure database of modern wind turbines. The results show the relation between environmental parameters such as relative humidity, ambient temperature, wind speed and the failures of five major WT components: gearbox, generator, frequency converter, pitch and yaw system. Additionally, the performance of each technique, associating weather conditions and WT component failures, is assessed.

Wind turbine components’ failure prognosis allows wind farm owners to apply predictive maintenance techniques to their fleets. Determining the health status of a turbine’s component typically requires verifying many variables that should be monitored simultaneously. The scope of this study is the selection of the more relevant variables and the generation of a health status indicator (Failure Index) to be considered as a decision criterion in Operation and Maintenance activities. The proposed methodology is based on Gaussian Mixture Copula Models (GMCMs) combined with a smoothing method (Cubic spline smoothing) to define a component’s health index based on the previous behavior and relationships between the considered variables. The GMCM allows for determining the component’s status in a multivariate environment, providing the selected variables’ joint probability and obtaining an easy-to-track univariate health status indicator. When the health of a component is degrading, anomalous behavior becomes apparent in certain Supervisory Control and Data Acquisition (SCADA) signals. By monitoring these SCADA signals using this indicator, the proposed anomaly detection method could capture the deviations from the healthy working state. The resulting indicator shows whether any failure is likely to occur in a wind turbine component and would aid in a preventive intervention scheduling.

The probabilistic approach has an important place in the wind energy research field as it provides cheap and fast initial information for experts with the help of simulations and estimations. Wind energy experts have been using the Weibull distribution for wind speed data for many years. Nevertheless, there exist cases, where the Weibull distribution is inappropriate with data presenting bimodal or multimodal behaviour which are unfit in high, null and low winds that can cause serious energy estimation errors. This paper presents a procedure for dealing with wind speed data taking into account non-Weibull distributions or data treatment when needed. The procedure detects deviations from the unimodal (Weibull) distribution and proposes other possible distributions to be used. The deviations of the used distributions regarding real data are addressed with the Root Mean Square Error (RMSE) and the annual energy production (AEP).

Operation of wind farms is driven by the overall aim of minimising costs while maximising energy sales. However, in certain circumstances investments are required to guarantee safe operation and survival of an asset. In this paper, we discuss the merits of various catastrophic failure prevention strategies in a Spanish wind farm. The wind farm operator was required to replace blades in two phases: temporary and final repair. We analyse the power performance of the turbine in the different states and investigate four scenarios with different timing of temporary and final repair during one year. The financial consequences of the scenarios are compared with a baseline by using a discounted cash flow analysis that considers the wholesale electricity market selling prices and interest rates. A comparison with the UK electricity market is conducted to highlight differences in the rate of return in the two countries.