• Energy Research

This paper proposes a practical approach to assess wind energy resource and calculate annual energy production for use on university courses in engineering. To this end, two practical exercises were designed in the open-source software GNU Octave (compatible with MATLAB) using both synthetic and field data. The script used to generate the synthetic data as well as those created to develop the practical exercises are included for the benefit of other educational bodies. With the first exercise the students learn how to characterize the wind resource at the wind turbine hub height and adjust it to the Weibull distribution. Two examples are included in this exercise: one with an appropriate fit and another where the Weibull distribution does not fit properly to the generated data. Furthermore, in this exercise, field data (gathered with a LiDAR remote sensing device) is also used to calculate shear exponents for a proper characterisation of the wind profile. The second exercise consists of the calculation of the annual energy production of a wind power plant, where the students can assess the influence of different factors (wind speed, rotor diameter, rated power, etc.) in the project. The exercises proposed can easily be implemented through either in-class or online teaching modes.

Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to which CM has been applied? (iii) What are the SCADA variables used? and (iv) What AI techniques are currently under research? Further to answering the research questions, we identify the lack of accessible WT SCADA data towards research and the need for its standardization. Our SLR was developed by reviewing more than 95 scientific articles published in the last three years.

It is common knowledge that wind energy is a crucial, strategic component of the mix needed to create a green economy. In this regard, optimizing the operations and maintenance (O&M) of wind turbines (WTs) is key, as it will serve to reduce the levelized cost of electricity (LCOE) of wind energy. Since most modern WTs are equipped with a Supervisory Control and Data Acquisition (SCADA) system for remote monitoring and control, condition-based maintenance using SCADA data is considered a promising solution, although certain drawbacks still exist. Typically, large amounts of normal-operating SCADA data are generated against small amounts of fault-related data. In this study, we use high-frequency SCADA data from an operating WT with a significant imbalance between normal and fault classes. We implement several resampling techniques to address this challenge and generate synthetic generator fault data. In addition, several machine learning (ML) algorithms are proposed for processing the resampled data and WT generator fault classification. Experimental results show that ADASYN + Random Forest obtained the best performance, providing promising results toward wind farm O&M optimization.

ABSTRACT: s the share of VRE (variable renewable energy) has grown rapidly, curtailment issues have arisen worldwide. This paper evaluates and compares curtailment situations in selected countries using an objective and quantitative evaluation tool named the "C-E map " (curtailment-energy share map). The C-E map is a correlation map between curtailment ratios that mean curtailed wind (or solar) energy per available energy and energy shares of wind (or solar). The C-E map can draw a historical trend curve in a given country/area, as an at-a-glance tool to enable historical and/or international comparison. The C-E map also can classify the given countries/areas into several categories, according to the current levels of curtailment ratio and historical trends. The C-E map helps institutional and objective understanding of curtailment for non-experts including policy makers. info:eu-repo/semantics/publishedVersion

The imminent depletion of oil resources and increasing environmental pollution have driven the use of clean energy, particularly wind energy. However, wind turbines (WTs) face significant challenges, such as critical component failures, which can cause unexpected shutdowns and affect energy production. To address this challenge, we analyzed the Supervisory Control and Data Acquisition (SCADA) data to identify significant differences between the relationship of variables based on data reconstruction errors between actual and predicted values. This study proposes a hybrid short- and long-term memory autoencoder model with multihead self-attention (LSTM-MA-AE) for WT converter fault detection. The proposed model identifies anomalies in the data by comparing the reconstruction errors of the variables involved. However, more is needed. To address this model limitation, we developed a fault prediction system that employs an adaptive threshold with an Exponentially Weighted Moving Average (EWMA) and a fixed threshold. This system analyzes the anomalies of several variables and generates fault warnings in advance time. Thus, we propose an outlier detection method through data preprocessing and unsupervised learning, using SCADA data collected from a wind farm located in complex terrain, including real faults in the converter. The LSTM-MA-AE is shown to be able to predict the converter failure 3.3 months in advance, and with an F1 greater than 90% in the tests performed. The results provide evidence of the potential of the proposed model to improve converter fault diagnosis with SCADA data in complex environments, highlighting its ability to increase the reliability and efficiency of WTs.

Abstract This paper analyzes the response of AC-Contactors connected to different electrical loads under voltage dips and supply interruptions. A wide range of magnitudes, durations and point-on-wave initiation values are considered for generating the power quality disturbances. For the first time, three types of electrical loads are used to evaluate the AC-Contactor sensitivity under most common power quality disturbances. For this purpose, over 40,000 tests are carried out on AC electromechanical contactors, contributing to reduce the uncertainties of their electrical response outlined in the specific literature. Voltage-versus-time curves of contactors with similar technical specifications, but provided by different manufacturers, are included in the paper. In this way, the behavior of contactors dealing with real situations located in industrial environments is analyzed in detail. The results present a valuable interest for commercial, industrial, utility and OEM sectors, where this kind of switching equipment is widely used in a varying power factor scenario.

Ancillary services are intended to ensure the quality, reliability, and security conditions of the electricity supply. Active power‐related ancillary services are considered balancing services, including mandatory services, such as spinning or primary regulation, and optional services, such as secondary and tertiary regulation. These optional balancing services have typically been supplied by conventional power plants, but since their contribution to the production share is decreasing due to high renewable energy penetration, power system operators are currently in need of alternatives. In addition, the recent advances in both the wind power industry and power system observability and controllability, and the necessary changes in balancing markets have made it technically and economically possible for wind power plants to contribute to optional balancing services. Under this framework, our study focuses on the contribution of wind power to balancing markets in the Spanish power system. Specifically, this work analyses the operational capability test for wind energy active power ancillary services provision and the results of the participation of wind energy to tertiary regulation and imbalance management from February 2016 to July 2017. Furthermore, the analysis includes a comparison with other generation technologies and the identification of upward and downward direction margins. These results show the real experience of the contribution of Spanish wind energy to optional active power ancillary services.This article is categorized under: Wind Power > Systems and Infrastructure

Hydro power is one of the most flexible sources of electricity production. Power systems with considerable amounts of flexible hydro power potentially offer easier integration of variable generation, e.g., wind and solar. However, there exist operational constraints to ensure mid‐/long‐term security of supply while keeping river flows and reservoirs levels within permitted limits. In order to properly assess the effective available hydro power flexibility and its value for storage, a detailed assessment of hydro power is essential. Due to the inherent uncertainty of the weather‐dependent hydrological cycle, regulation constraints on the hydro system, and uncertainty of internal load as well as variable generation (wind and solar), this assessment is complex. Hence, it requires proper modeling of all the underlying interactions between hydro power and the power system, with a large share of other variable renewables. A summary of existing experience of wind integration in hydro‐dominated power systems clearly points to strict simulation methodologies. Recommendations include requirements for techno‐economic models to correctly assess strategies for hydro power and pumped storage dispatch. These models are based not only on seasonal water inflow variations but also on variable generation, and all these are in time horizons from very short term up to multiple years, depending on the studied system. Another important recommendation is to include a geographically detailed description of hydro power systems, rivers’ flows, and reservoirs as well as grid topology and congestion. WIREs Energy Environ 2017, 6:e220. doi: 10.1002/wene.220This article is categorized under: Wind Power > Science and Materials Wind Power > Systems and Infrastructure