Low-grade wind-driven directional flow in anchored droplets.

Low-grade wind with airspeed Vwind < 5 m/s, while distributed far more abundantly, is still challenging to extract because current turbine-based technologies require particular geography (e.g., wide-open land or off-shore regions) with year-round Vwind > 5 m/s to effectively rotate the blades. Here, we report that low-speed airflow can sensitively enable directional flow within nanowire-anchored ionic liquid (IL) drops. Specifically, wind-induced air/liquid friction continuously raises directional leeward fluid transport in the upper portion, whereas three-phase contact line (TCL) pinning blocks further movement of IL. To remove excessive accumulation of IL near TCL, fluid dives, and headwind flow forms in the lower portion, as confirmed by microscope observation. Such stratified circulating flow within single drop can generate voltage output up to ~0.84 V, which we further scale up to ~60 V using drop "wind farms". Our results demonstrate a technology to tap the widespread low-grade wind as a reliable energy resource.

Prevalence and predictors of wind energy opposition in North America.

Addressing climate change requires societies to transition away from fossil fuels toward low-carbon energy, including renewables. Unfortunately, large wind projects have proven politically controversial, with groups opposing them across advanced economies. To date, there are few large-scale, systematic studies to identify the prevalence and predictors of opposition to wind energy projects. Here, we analyzed a dataset of wind energy projects across the United States and Canada between 2000 and 2016. We found that during this period, in the United States, 17% of wind projects faced significant opposition, and in Canada, 18% faced opposition, with rates in both countries growing over time. Opposition was concentrated regionally in the Northeastern United States and in Ontario, Canada. In both countries, larger projects with more turbines were more likely to be opposed. In the United States, opposition was more likely and more intense in areas with a higher proportion of White people, and a smaller proportion of Hispanic people. In Canada, opposition was more likely and more intense in wealthier communities. The most common tactics used to oppose wind energy were court cases, legislation, and physical protests. The number of people engaging in opposition to wind projects is likely small: Across articles that cited the number of individuals engaging in protests, the median number was 23 in the United States and 34 in Canada. When wealthier, Whiter communities oppose wind projects, this slows down the transition away from fossil fuel projects in poorer communities and communities of color, an environmental injustice we call "energy privilege."

A Rolling-Bead Triboelectric Nanogenerator for Harvesting Omnidirectional Wind-Induced Energy toward Shelter Forests Monitoring.

Shelter forests (or shelter-belts), while crucial for climate regulation, lack monitoring systems, e.g., Internet of Things (IoT) devices, but their abundant wind energy can potentially power these devices using the trees as mounting points. To harness wind energy, an omnidirectional fluid-induced vibration triboelectric nanogenerator (OFIV-TENG) has been developed. The device is installed on shelter forest trees to harvest wind energy from all directions, employing a fluid-induced vibration (FIV) mechanism (fluid-responding structure) that can capture and use wind energy, ranging from low wind speeds (vortex vibration) to high wind speeds (galloping). The rolling-bead triboelectric nanogenerator (TENG) can efficiently harvest energy while minimizing wear and tear. Additionally, the usage of double electrodes results in an effective surface charge density of 21.4 µC m-2 , which is the highest among all reported rolling-bead TENGs. The collected energy is utilized for temperature and humidity monitoring, providing feedback on the effect of climate regulation in shelter forests, alarming forest fires, and wireless wind speed warning. In general, this work provides a promising and rational strategy, using natural resources like trees as the supporting structures, and shows broad application prospects in efficient energy collection, wind speed warning, and environmentally friendliness.

Self-Powered Traffic Lights Through Wind Energy Harvesting Based on High-Performance Fur-Brush Dish Triboelectric Nanogenerators.

Traffic lights play vital roles in urban traffic management systems, providing clear directional guidance for vehicles and pedestrians while ensuring traffic safety. However, the vast quantity of traffic lights widely distributed in the transportation system aggravates energy consumption. Here, a self-powered traffic light system is proposed through wind energy harvesting based on a high-performance fur-brush dish triboelectric nanogenerator (FD-TENG). The FD-TENG harvests wind energy to power the traffic light system continuously without needing an external power supply. Natural rabbit furs are applied to dish structures, due to their outstanding characteristics of shallow wear, high performance, and resistance to humidity. Also, the grid pattern of the dish structure significantly impacts the TENG outputs. Additionally, the internal electric field and the influences of mechanical and structural parameters on the outputs are analyzed by finite element simulations. After optimization, the FD-TENG can achieve a peak power density of 3.275 W m-3. The portable and miniature features of FD-TENG make it suitable for other natural environment systems such as forests, oceans, and mountains, besides the traffic light systems. This study presents a viable strategy for self-powered traffic lights, establishing a basis for efficient environmental energy harvesting toward big data and Internet of Things applications.

The geographic extent of bird populations affected by renewable-energy development.

Bird populations are declining globally. Wind and solar energy can reduce emissions of fossil fuels that drive anthropogenic climate change, yet renewable-energy production represents a potential threat to bird species. Surveys to assess potential effects at renewable-energy facilities are exclusively local, and the geographic extent encompassed by birds killed at these facilities is largely unknown, which creates challenges for minimizing and mitigating the population-level and cumulative effects of these fatalities. We performed geospatial analyses of stable hydrogen isotope data obtained from feathers of 871 individuals of 24 bird species found dead at solar- and wind-energy facilities in California (USA). Most species had individuals with a mix of origins, ranging from 23% to 98% nonlocal. Mean minimum distances to areas of likely origin for nonlocal individuals were as close as 97 to >1250 km, and these minimum distances were larger for species found at solar-energy facilities in deserts than at wind-energy facilities in grasslands (Cohen's d = 6.5). Fatalities were drawn from an estimated 30-100% of species' desingated ranges, and this percentage was significantly smaller for species with large ranges found at wind facilities (Pearson's r = -0.67). Temporal patterns in the geographic origin of fatalities suggested that migratory movements and nonmigratory movements, such as dispersal and nomadism, influence exposure to fatality risk for these birds. Our results illustrate the power of using stable isotope data to assess the geographic extent of renewable-energy fatalities on birds. As the buildout of renewable-energy facilities continues, accurate assessment of the geographic footprint of wildlife fatalities can be used to inform compensatory mitigation for their population-level and cumulative effects.

Extensión geográfica de las poblaciones de aves afectadas por desarrollos de energía renovable Resumen Las poblaciones mundiales de aves están en declive. Las energías solar y eólica pueden reducir las emisiones de combustibles fósiles que causan el cambio climático, aunque la producción de energías renovables representa una amenaza potencial para las aves. Los censos para evaluar los efectos potenciales en los centros de energía renovable son exclusivamente locales y se sabe poco sobre la extensión geográfica representada por las aves que mueren en estas instalaciones, lo que plantea obstáculos para mitigar los efectos acumulativos y de nivel poblacional de estas muertes. Realizamos análisis geoespaciales con datos del isótopo de hidrógeno estable obtenido de las plumas de 871 ejemplares de 24 especies de aves que fueron hallados muertos en los centros de energía solar y eólica en California, EE.UU. La mayoría de las especies contó con ejemplares de orígenes mixtos, con un rango del 23% al 98% no local. La media de la distancia mínima a las áreas de probable origen de los ejemplares no locales varía entre los 97 hasta > 1,250 km. Estas distancias mínimas fueron mayores para las especies encontradas en los centros de energía solar situadas en desiertos que para las especies encontradas en los centros de energía eólica localizadas en pastizales (d de Cohen = 6.5). Las muertes representan un 30­100% de la extensión de las especies. Este porcentaje fue significativamente menor para las especies con extensiones amplias encontradas en instalaciones eólicas (r de Pearson = ­0.67). Los patrones temporales en el origen geográfico de las muertes sugieren que los movimientos migratorios y no migratorios, como la dispersión y el nomadismo, influyen en la exposición de estas aves al riesgo de muerte. Nuestros resultados demuestran la utilidad de los isótopos estables para evaluar el alcance geográfico de las muertes de aves asociadas a energías renovables. Con el progresivo aumento de instalaciones de energía renovable, una evaluación precisa de la huella geográfica de la mortandad de fauna salvaje podrá guiar la mitigación compensatoria de sus efectos acumulativos y de nivel poblacional.

Quantifying the Effects of Energy Infrastructure on Bird Populations and Biodiversity.

Shale oil and gas production and wind energy generation both expanded rapidly across the United States between 2000 and 2020, raising concerns over impacts on wildlife. I combine longitudinal microdata from the National Audubon Society's Christmas Bird Count with geolocated registries of all wind turbines and shale wells constructed in the contiguous U.S. during this period to estimate the causal effects of these contrasting types of energy infrastructure on bird populations and biodiversity, which are key bellwethers of ecosystem health. Results show that the onset of shale oil and gas production reduces subsequent bird population counts by 15%, even after adjusting for location and year fixed effects, weather, counting effort, and land-use changes. Wind turbines do not have any measurable impact on bird counts. Negative effects of shale are larger when wells are drilled within important bird habitats.

Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

Towards sustainable development goals: Assessment of wind and solar potential in northwest China.

The development and utilization of renewable energy (RE) is crucial for achieving the sustainable development goals (SDGs). The northwest China, endowed with abundant RE sources such as wind and solar power, accounts for over 70% of the country's total resources. The assessment and utilization of RE in this region has become a critical means to achieve the SDGs, particularly SDG7. However, lack of knowledge regarding the RE potential poses a barrier to achieving high-quality energy development. Thus, through a Geographical Information System (GIS) based multi-criteria analysis, we assess the solar and wind energy potential in northwest China, quantitatively examine the energy potential and its contribution towards achieving the SDGs. Our results show that a substantial portion of RE can be harnessed in northwest China, with wind energy generation reaching up to 9.84PWh/km2/yr at 110m and 12.43 PWh/km2/yr at 140m. Concurrently, solar energy can contribute up to 15.16 PWh/km2/yr. Xinjiang province has the highest RE potential for it contains a large share of suitable area with good resource quality. The findings illustrate the contribution of northwest China towards achieving SDGs and facilitate the formulation of more targeted resource policies.

Dynamic role of clean energy and sustainable economic growth in coastal region: Novel observations from China.

China's coastal region is the major geographical unit for the future development of China's industrial sector. The transformation of basic structure to high-class development in China's coastal places is a significant tool for promoting the changes related to quality, power and efficiency in regional economic development. In the 21st century, environmental and energy issues have increased worldwide, and challenges related to environmental pollution, energy crises, and ecological imbalances have emerged. To climate change and energy utilization, the sustainable progress of clean energy is the new route of future energy development. Based on China's non-polluting energy growth process in the last ten years, this article explores China's clean/green energy policies and economic growth development plans. Clean energy utilization is crucial for sustainable development in the context of high-quality economic growth and climate change. However, the monetary evolution and carbon emission are not investigated whole from the clean energy aspects. Using Wind energy sources as the acceptable variable, this paper employs threshold regression and impulse functions to assess the energy consumption and economic growth on carbon emission in 30 Chinese provinces over the 2000 to 2020 period. The Deep Belief Network (DBN) model predicts wind energy utilization and efficiency. The results show that economic development and carbon emissions are connected. Further, growth influences promote the offset of carbon emissions. Green innovation alters the nexus of carbon emissions, and China's economy reduces carbon usage. It provides the decision-making policies for clean energy development.

Spatial constraints in large-scale expansion of wind power plants.

When wind turbines are arranged in clusters, their performance is mutually affected, and their energy generation is reduced relative to what it would be if they were widely separated. Land-area power densities of small wind farms can exceed 10 W/m2, and wakes are several rotor diameters in length. In contrast, large-scale wind farms have an upper-limit power density in the order of 1 W/m2 and wakes that can extend several tens of kilometers. Here, we address two important questions: 1) How large can a wind farm be before its generation reaches energy replenishment limits and 2) How far apart must large wind farms be spaced to avoid inter-wind-farm interference? We characterize controls on these spatial and temporal scales by running a set of idealized atmospheric simulations using the Weather and Research Forecasting model. Power generation and wind speed within and over the wind farm show that a timescale inversely proportional to the Coriolis parameter governs such transition, and the corresponding length scale is obtained by multiplying the timescale by the geostrophic wind speed. A geostrophic wind of 8 m/s and a Coriolis parameter of 1.05 × 10-4 rad/s (latitude of ∼46°) would give a transitional scale of about 30 km. Wind farms smaller than this result in greater power densities and shorter wakes. Larger wind farms result instead in power densities that asymptotically reach their minimum and wakes that reach their maximum extent.

Wind turbine icing characteristics and icing-induced power losses to utility-scale wind turbines.

A field campaign was carried out to investigate ice accretion features on large turbine blades (50 m in length) and to assess power output losses of utility-scale wind turbines induced by ice accretion. After a 30-h icing incident, a high-resolution digital camera carried by an unmanned aircraft system was used to capture photographs of iced turbine blades. Based on the obtained pictures of the frozen blades, the ice layer thickness accreted along the blades' leading edges was determined quantitatively. While ice was found to accumulate over whole blade spans, outboard blades had more ice structures, with ice layers reaching up to 0.3 m thick toward the blade tips. With the turbine operating data provided by the turbines' supervisory control and data acquisition systems, icing-induced power output losses were investigated systematically. Despite the high wind, frozen turbines were discovered to rotate substantially slower and even shut down from time to time, resulting in up to 80% of icing-induced turbine power losses during the icing event. The research presented here is a comprehensive field campaign to characterize ice accretion features on full-scaled turbine blades and systematically analyze detrimental impacts of ice accumulation on the power generation of utility-scale wind turbines. The research findings are very useful in bridging the gaps between fundamental icing physics research carried out in highly idealized laboratory settings and the realistic icing phenomena observed on utility-scale wind turbines operating in harsh natural icing conditions.

Bidding against the wind: A choice experiment in green energy, green jobs and offshore views in North Carolina, USA.

Offshore wind development is in its nascent stages in the United States. Recent research indicates that the visual impacts of offshore wind farms are viewed negatively by the general population. This North Carolina application is the first US-focused discrete choice experiment that explicitly asks respondents to consider the positive local and global benefits from offshore wind development, such as job creation and greenhouse gas emission reductions, simultaneously with their visual impacts. We find significant willingness to pay (WTP) for reducing the visual impacts of offshore wind farms, and that the extent of disamenity varies in the population and with placement along developed tourist towns (as much as $783/year for three years) or preserved coastlines (as much as $451/year for three years). We also find that some preference classes value projects that create permanent jobs and reduce carbon emissions. We use our estimates of preferences for the positive and negative attributes to explore specific wind farm configurations and locations that could achieve positive consensus in a heterogenous population.

Enhancement of ANN-based wind power forecasting by modification of surface roughness parameterization over complex terrain.

Wind energy plays an important role in the sustainable energy transition towards a low-carbon society. Proper assessment of wind energy resources and accurate wind energy prediction are essential prerequisites for balancing electricity supply and demand. However, these remain challenging, especially for onshore wind farms over complex terrains, owing to the interplay between surface heterogeneities and intermittent turbulent flows in the planetary boundary layer. This study aimed to improve wind characteristic assessment and medium-term wind power forecasts over complex hilly terrain using a numerical weather prediction (NWP) model. The NWP model reproduced the wind speed distribution, duration, and spatio-temporal variabilities of the observed hub-height wind speed at 24 wind turbines in onshore wind farms when incorporating more realistic surface roughness effects, such as the subgrid-scale topography, roughness sublayer, and canopy height. This study also emphasizes the good features for machine learning that represent heterogeneities in the surface roughness elements in the atmospheric model. We showed that medium-term forecasting using the NWP model output and a simple artificial neural network (ANN) improved day-ahead wind power forecasts by 14% in terms of annual normalized mean absolute error. Our results suggest that better parameterizations of surface friction in atmospheric models are important for wind power forecasting and resource assessment using NWP models, especially when combined with machine learning techniques, and shed light on onshore wind power forecasting and wind energy assessment in mountainous regions.

A standardized protocol for assessing the performance of automatic detection systems used in onshore wind power plants to reduce avian mortality.

While wind power plants are an important contribution to the production of renewable energy to limit climate change, collision mortality from turbines is a danger for birds, including many protected species. To try to mitigate collision risks, automatic detection systems (ADSs) can be deployed on wind power plants; these work by detecting incoming birds using a detection/classification process and triggering a specific reaction (scaring off the bird or shutting down the turbine). Nonetheless, bird fatalities still occur at ADS-equipped wind power plants, which raises the question of the performance of these tools. To date, the lack of a transparent, peer-reviewed experimental process to compare the performance of types of ADS has meant there is no robust protocol to assess these systems. With the aim of filling this gap, we developed two standardized protocols that provide objective and unbiased assessments of the performance of different types of ADS, based on their probability of detecting/classifying birds at risk of collision. Both protocols rely on precise 3D tracking of wild birds by human observers using a laser rangefinder, and the comparison of these tracks with those detected and recorded by an ADS. The first protocol evaluates a system's general performance, generating comparable data for all types of ADS. In this protocol, detection/classification probability is estimated conditional on several abiotic and biotic environmental factors such as bird size, distance from the target, the flight angle and azimuth of the bird, as well as weather conditions. The second protocol aims to verify that the performance of an ADS installed on a given wind power plant complies with its regulatory requirements. In this protocol, detection/classification probability is specifically estimated for a given target species at a given regulatory detection distance. This protocol also estimates the proportion of time an ADS is functional on site over a year, and the proportion of reaction orders successfully operated by wind turbines. These protocols have been field-tested and made publicly available for use by government agencies and wind power plant operators.

Potential impacts of climate change on renewable energy in Egypt.

The need for renewable energy sources is recently necessitated by attaining sustainability and climate change mitigation. Accordingly, the use of renewable energy sources has been growing rapidly during the last two decades. Yet, the potentials of renewable energy sources are generally influenced by several climatic factors that either determine the source of energy such as wind speed in the case of wind power or affect the performance of system such as the reduction in solar PV power production due to temperature increase. This highlights the need for assessing climate change impacts on renewable energy sources in the future to ensure their reliability and sustainability.This paper is intended to assess impacts of climate change on wind and solar potential energy in Egypt by the year 2065 under RCP 8.5 scenario. For this purpose, a GIS-based methodology of three main steps was applied. The results revealed that solar energy potential in Egypt is expected to be relatively less vulnerable to climate change compared to wind energy. In this respect, it was found that while wind energy potential was estimated to range ± 12%. By the year 2065 under RCP 8.5 scenario, PV module power is expected to decrease by about 1.3% on average. Such assessment can assist in developing more sustainable and flexible renewable energy policy in Egypt.

A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy Assessment.

The joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators.

Hybrid Triboelectric-Electromagnetic-Piezoelectric Wind Energy Harvester toward Wide-Scale IoT Self-Powered Sensing.

Wind energy is the most promising alternative to fossil fuels as a clean, nonpolluting, and renewable source of energy. However, how to achieve stable and efficient harvesting of wind energy has been a major challenge. Here, a triboelectric-electromagnetic-piezoelectric hybrid wind energy harvester (TEP-WEH) based on the cantilever is proposed. The TEP-WEH achieves a power density of 62.79 mW (m3 rpm)-1 at a 3 m s-1 wind speed, attributable to a rational and optimized structural design. In addition, owing to the soft contact strategy of the TENG module, the TEP-WEH provides excellent durability and drivability. The harvester is demonstrated to successfully and continuously light a commercial lighting bulb rated at 5 W and provide an energy supply for self-powered sensing. This work provides an efficient solution for wind energy harvesting and wide-scale self-powered IoT sensing.

A utilization distribution for the global population of Cape Vultures (Gyps coprotheres) to guide wind energy development.

The rapid development of wind energy in southern Africa represents an additional threat to the already fragile populations of African vultures. The distribution of the vulnerable Cape Vulture Gyps coprotheres overlaps considerably with wind energy development areas in South Africa, creating conflicts that can hinder both vulture conservation and sustainable energy development. To help address this conflict and aid in the safe placement of wind energy facilities, we map the utilization distribution (UD) of this species across its distributional range. Using tracking data from 68 Cape Vultures collected over the last 20 years, we develop a spatially explicit habitat use model to estimate the expected UDs around known colonies. Scaling the UDs by the number of vultures expected to use each of the colonies, we estimate the Cape Vulture population utilization distribution (PUD) and determine its exposure to wind farm impacts. To complement our results, we model the probability of a vulture flying within the rotor sweep area of a wind turbine throughout the species range and use this to identify areas that are particularly prone to collisions. Overall, our estimated PUD correlates well with reporting rates of the species from the Southern African Bird Atlas Project, currently used to assess potential overlap between Cape Vultures and wind energy developments, but it adds important benefits, such as providing a spatial gradient of activity estimates over the entire species range. We illustrate the application of our maps by analyzing the exposure of Cape Vultures in the Renewable Energy Development Zones (REDZs) in South Africa. This application is a scalable procedure that can be applied at different planning phases, from strategic, nationwide planning to project-level assessments.

Experiences with the quality of telemedical care in an offshore setting - a qualitative study.

BACKGROUND: The evaluation and the improvement of the quality of telemedical care become increasingly important in times where this type of care is offered to a broad number of patients more and more. As telemedical care in an offshore setting has already been in use for decades, analyzing the extensive experience of offshore paramedics using telemedical care can help identify determinants of quality. Therefore, the aim of this study was to explore determinants of the quality of telemedical care using the experiences of experienced offshore paramedics. METHODS: We conducted a qualitative analysis of 22 semi-structured interviews with experienced offshore paramedics. The results were categorized in a hierarchical category system using content analysis as described by Mayring. RESULTS: All 22 participants were males, having a mean of 3.9 years of experience working with telemedicine support offshore. Generally, participants stated that for them telemedical interaction did not differ much from personal interaction. However, the offshore paramedics personality and way to communicate were mentioned to impact the quality of telemedical care as it influenced the way cases were presented. Furthermore, interviewees described it to be impossible to use telemedicine in cases of an emergency as it was too time-consuming, technically too complex, and lead to cognitive overload as other tasks with higher priority needed their attention. Three determinants of a successful consultation were mentioned: low levels of complexity in the reason for consultation, telemedical guidance training for the teleconsultant physician and for the delegatee. CONCLUSION: Appropriate indications for telemedical consultation, communication training of consultation partners, and the impact of personality need to be addressed to enhance the quality of future telemedical care.

Condition Monitoring of Wind Turbine Systems by Explainable Artificial Intelligence Techniques.

The performance evaluation of wind turbines operating in real-world environments typically relies on analyzing the power curve, which shows the relationship between wind speed and power output. However, conventional univariate models that consider only wind speed as an input variable often fail to fully explain the observed performance of wind turbines, as power output depends on multiple variables, including working parameters and ambient conditions. To overcome this limitation, the use of multivariate power curves that consider multiple input variables needs to be explored. Therefore, this study advocates for the application of explainable artificial intelligence (XAI) methods in constructing data-driven power curve models that incorporate multiple input variables for condition monitoring purposes. The proposed workflow aims to establish a reproducible method for identifying the most appropriate input variables from a more comprehensive set than is usually considered in the literature. Initially, a sequential feature selection approach is employed to minimize the root-mean-square error between measurements and model estimates. Subsequently, Shapley coefficients are computed for the selected input variables to estimate their contribution towards explaining the average error. Two real-world data sets, representing wind turbines with different technologies, are discussed to illustrate the application of the proposed method. The experimental results of this study validate the effectiveness of the proposed methodology in detecting hidden anomalies. The methodology successfully identifies a new set of highly explanatory variables linked to the mechanical or electrical control of the rotor and blade pitch, which have not been previously explored in the literature. These findings highlight the novel insights provided by the methodology in uncovering crucial variables that significantly contribute to anomaly detection.