On the public citation graph
A Normal Behavior Model Based on Power Curve and Stacked Regressions for Condition Monitoring of Wind Turbines
0 signed · 81 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/we.1755openalex-snapshot
A kernel plus method for quantifying wind turbine performance upgrades
inferredcites →https://doi.org/10.1002/we.2567openalex-snapshot
Combining SCADA and vibration data into a single anomaly detection model to predict wind turbine component failure
inferredcites →https://doi.org/10.1006/jcss.1997.1504openalex-snapshot
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
inferredcites →https://doi.org/10.1007/978-3-030-72322-4_35-1openalex-snapshot
Wind Turbine Anomaly Detection Based on SCADA Data
inferredcites →https://doi.org/10.1016/j.apenergy.2016.01.133openalex-snapshot
A generalized model for wind turbine anomaly identification based on SCADA data
inferredcites →https://doi.org/10.1016/j.apenergy.2016.12.096openalex-snapshot
Fault-tolerant cooperative control in an offshore wind farm using model-free and model-based fault detection and diagnosis approaches
inferredcites →https://doi.org/10.1016/j.apenergy.2021.117342openalex-snapshot
Multi-target normal behaviour models for wind farm condition monitoring
inferredcites →https://doi.org/10.1016/j.ijepes.2020.106233openalex-snapshot
Gearbox oil temperature anomaly detection for wind turbine based on sparse Bayesian probability estimation
inferredcites →https://doi.org/10.1016/j.renene.2008.10.022openalex-snapshot
On-line monitoring of power curves
inferredcites →https://doi.org/10.1016/j.renene.2010.05.014openalex-snapshot
The prediction and diagnosis of wind turbine faults
inferredcites →https://doi.org/10.1016/j.renene.2012.11.030openalex-snapshot
Wind turbine condition monitoring by the approach of SCADA data analysis
inferredcites →https://doi.org/10.1016/j.renene.2016.09.029openalex-snapshot
Control chart monitoring of wind turbine generators using the statistical inertia of a wind farm average
inferredcites →https://doi.org/10.1016/j.renene.2016.12.075openalex-snapshot
Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves
inferredcites →https://doi.org/10.1016/j.renene.2018.05.024openalex-snapshot
Anomaly detection and fault analysis of wind turbine components based on deep learning network
inferredcites →https://doi.org/10.1016/j.renene.2019.09.041openalex-snapshot
Anomaly detection for wind turbines based on the reconstruction of condition parameters using stacked denoising autoencoders
inferredcites →https://doi.org/10.1016/j.renene.2020.04.148openalex-snapshot
System-wide anomaly detection in wind turbines using deep autoencoders
inferredcites →https://doi.org/10.1016/j.rser.2013.01.012openalex-snapshot
Review of power curve modelling for wind turbines
inferredcites →https://doi.org/10.1016/j.rser.2013.10.030openalex-snapshot
A comprehensive review on wind turbine power curve modeling techniques
inferredcites →https://doi.org/10.1049/iet-rpg.2016.0248openalex-snapshot
Using SCADA data for wind turbine condition monitoring – a review
inferredcites →https://doi.org/10.1049/iet-rpg.2018.0156openalex-snapshot
SCADA‐based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes
inferredcites →https://doi.org/10.1049/iet-rpg.2020.0224openalex-snapshot
Wind turbines anomaly detection based on power curves and ensemble learning
inferredcites →https://doi.org/10.1109/icpre52634.2021.9635340openalex-snapshot
An Intelligent Data-Driven Machine Learning Approach for Fault Detection of Wind Turbines
inferredcites →https://doi.org/10.1109/jproc.2022.3171691openalex-snapshot
A Comprehensive Review on Signal-Based and Model-Based Condition Monitoring of Wind Turbines: Fault Diagnosis and Lifetime Prognosis
inferredcites →https://doi.org/10.1109/tcst.2014.2364956openalex-snapshot
Wind Turbine Fault Diagnosis and Fault-Tolerant Torque Load Control Against Actuator Faults
inferredcites →https://doi.org/10.1109/tec.2013.2294893openalex-snapshot
Development of a Novel Power Curve Monitoring Method for Wind Turbines and Its Field Tests
inferredcites →https://doi.org/10.1109/tie.2015.2422394openalex-snapshot
A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods
inferredcites →https://doi.org/10.1109/tim.2020.3044719openalex-snapshot
A Fast Abnormal Data Cleaning Algorithm for Performance Evaluation of Wind Turbine
inferredcites →https://doi.org/10.1109/tim.2020.3045800openalex-snapshot
Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach
inferredcites →https://doi.org/10.1109/tim.2021.3075742openalex-snapshot
Fault Detection of Wind Turbines by Subspace Reconstruction-Based Robust Kernel Principal Component Analysis
inferredcites →https://doi.org/10.1109/tnnls.2020.3017863openalex-snapshot
Online Learning With Adaptive Rebalancing in Nonstationary Environments
inferredcites →https://doi.org/10.1109/tpwrs.2015.2512843openalex-snapshot
Identification and Correction of Outliers in Wind Farm Time Series Power Data
inferredcites →https://doi.org/10.1109/tste.2020.2989220openalex-snapshot
Condition Monitoring of Wind Turbine Generators Using SCADA Data Analysis
inferredcites →https://doi.org/10.1155/2016/8519785openalex-snapshot
A Critical Review on Wind Turbine Power Curve Modelling Techniques and Their Applications in Wind Based Energy Systems
inferredcites →https://doi.org/10.1201/b17320?locatt=mode:legacyopenalex-snapshot
Data Classification: Algorithms and Applications
inferredcites →https://doi.org/10.3390/en12224224openalex-snapshot
Gearbox Fault Prediction of Wind Turbines Based on a Stacking Model and Change-Point Detection
inferredcites →https://doi.org/10.3390/s18113740openalex-snapshot
A Reliable Health Indicator for Fault Prognosis of Bearings
inferredcites →https://doi.org/10.5194/wes-5-1375-2020openalex-snapshot
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour models
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4659013openalex-extracted
A Condition Monitoring Approach Based on Multiple Linear Models for Fault Detection in Industrial-Scale Wind Farms
inferred← citeshttps://doi.org/10.1002/we.2961openalex-snapshot
Empirical Physics‐Based Normal Behavior Modeling of Drive Train Temperatures With High‐Resolution Wind Turbine Operating Data
inferred← citeshttps://doi.org/10.1007/s40866-026-00371-8openalex-snapshot
Optimization of Wind Power Data with Adaptive Multi-Density Quality Connection Clustering Considering Spatiotemporal Short-Term Correlation
inferred← citeshttps://doi.org/10.1016/j.apenergy.2024.124829openalex-snapshot
Gearbox pump failure prognostics in offshore wind turbine by an integrated data-driven model
inferred← citeshttps://doi.org/10.1016/j.egyai.2026.100773openalex-snapshot
A physics-informed collaborative interpretable framework for wind turbine anomaly detection
inferred← citeshttps://doi.org/10.1016/j.energy.2024.133628openalex-snapshot
Wind power curve model combining smoothed spline with first-order moments and density-adjusted wind speed strategy
inferred← citeshttps://doi.org/10.1016/j.engfailanal.2024.108099openalex-snapshot
Multivariate state estimation-based condition monitoring of slurry circulating pumps for wet flue gas desulfurization of power plants
inferred← citeshttps://doi.org/10.1016/j.est.2025.116209openalex-snapshot
Optimal sizing and operation of community hybrid energy storage systems
inferred← citeshttps://doi.org/10.1016/j.ifacol.2024.07.236openalex-snapshot
Synergetic Decomposition of Input-output Dependency for Control System Intelligent Monitoring: A Perspective from Information Theory
inferred← citeshttps://doi.org/10.1016/j.measurement.2026.122862openalex-snapshot
Uncertainty-aware indicators for metrologically robust monitoring of wind turbine power curve deviations
inferred← citeshttps://doi.org/10.1016/j.renene.2023.118965openalex-snapshot
Abnormal Data Cleaning for Wind Turbines by Image Segmentation Based on Active Shape Model and Class Uncertainty
inferred← citeshttps://doi.org/10.1016/j.ress.2025.111838openalex-snapshot
Recent advances in wind turbine condition monitoring using SCADA data: A state-of-the-art review
inferred← citeshttps://doi.org/10.1016/j.ress.2026.112458openalex-snapshot
Physics-informed condition monitoring for wind turbines via change point detection under heteroscedasticity
inferred← citeshttps://doi.org/10.1016/j.segan.2025.101775openalex-snapshot
Smart coordinated cyber-attacks on wind turbines: From detection to identification via machine learning
inferred← citeshttps://doi.org/10.1016/j.weer.2026.100034openalex-snapshot
Early fault detection of wind turbine yaw misalignment using Gaussian Mixture Copula Models on SCADA-derived power curves
inferred← citeshttps://doi.org/10.1038/s41598-025-25884-7openalex-snapshot
Intelligent fault prediction and diagnosis for wind-powered heating systems using graph neural networks
inferred← citeshttps://doi.org/10.1063/5.0129012openalex-snapshot
A dynamic probabilistic analysis method for wind turbine rotor based on the surrogate model
inferred← citeshttps://doi.org/10.1088/1361-6501/ae5681openalex-snapshot
Wind turbine condition monitoring based on state reconstruction with multi-scale feature aggregation transformer
inferred← citeshttps://doi.org/10.1109/access.2024.3379529openalex-snapshot
A Multi-View Spatio-Temporal Feature Fusion Approach for Wind Turbine Condition Monitoring Based on SCADA Data
inferred← citeshttps://doi.org/10.1109/access.2026.3651571openalex-snapshot
Early Fault Warning and Identification of Wind Turbines Based on Three-Dimensional Visualization of Dynamic Network Marker
inferred← citeshttps://doi.org/10.1109/aie61866.2024.10561323openalex-snapshot
A Normal Behavior Model Based on Machine Learning for Wind Turbine Cyber-Attack Detection
inferred← citeshttps://doi.org/10.1109/icca69928.2026.11618288openalex-snapshot
Reliable Multi-Target SCADA-Based Condition Monitoring for Wind Turbine Pitch Fault Detection
inferred← citeshttps://doi.org/10.1109/icpsasia58343.2023.10294878openalex-snapshot
Relative Health Status Assessment and Power Allocation of Wind Turbines Based on SSA Optimized VMD
inferred← citeshttps://doi.org/10.1109/mim.2023.10146566openalex-snapshot
Instrumentation and Measurement Systems: Aerosense: A Wireless, Non-Intrusive, Flexible, and MEMS-Based Aerodynamic and Acoustic Measurement System for Operating Wind Turbines
inferred← citeshttps://doi.org/10.1109/redec65313.2025.11190265openalex-snapshot
Actual Power Curve Construction and Detection of Normal and Abnormal Data in Wind Turbine Operation: AlFujeij Wind Farm Case Study
inferred← citeshttps://doi.org/10.1109/tase.2025.3593296openalex-snapshot
Wind Turbine Blade Icing Predictive Fault Warning System Based on Conditional Time Series Denoising Diffusion Data Restoration Model
inferred← citeshttps://doi.org/10.1109/tdei.2023.3322669openalex-snapshot
A Hybrid Regression Model to Estimate Remaining Useful Life of Transformer Liquid
inferred← citeshttps://doi.org/10.1109/tii.2023.3331766openalex-snapshot
Multitarget Normal Behavior Model Based on Heterogeneous Stacked Regressions and Change-Point Detection for Wind Turbine Condition Monitoring
inferred← citeshttps://doi.org/10.1109/tim.2023.3324347openalex-snapshot
When Deep Normal Behavior Models Meet Fault Samples: A Generalized Wind Turbine Anomaly Detection Scheme
inferred← citeshttps://doi.org/10.1109/tim.2024.3375416openalex-snapshot
Wind Turbine Hybrid Physics-Based Deep Learning Model for a Health Monitoring Approach Considering Provision of Ancillary Services
inferred← citeshttps://doi.org/10.1109/tim.2025.3545992openalex-snapshot
A Hybrid Deep and Broad Learning Architecture for Wind Power Forecasting Based on Spatial–Temporal Feature Selection
inferred← citeshttps://doi.org/10.1109/tim.2025.3568938openalex-snapshot
Three-Stage Composite Outlier Identification of Wind Power Data: Integrating Physical Rules With Regression Learning and Mathematical Morphology
inferred← citeshttps://doi.org/10.1109/tim.2025.3578697openalex-snapshot
Fault Propagation Analysis for Manufacturing Process Monitoring via a Temporal Causal Modeling Algorithm
inferred← citeshttps://doi.org/10.1109/tim.2025.3619206openalex-snapshot
A Condition Monitoring Method for Wind Turbine Generators Based on Latent Variables
inferred← citeshttps://doi.org/10.1109/tste.2024.3459005openalex-snapshot
Preserving Normal Power Curve Data With Sparse Density via Wind Speed-Power Correlation Trend Cleaning Method
inferred← citeshttps://doi.org/10.32604/cmes.2025.070070openalex-snapshot
Offshore Wind Turbines Anomalies Detection Based on a New Normalized Power Index
inferred← citeshttps://doi.org/10.3390/en16010180openalex-snapshot
Applications and Modeling Techniques of Wind Turbine Power Curve for Wind Farms—A Review
inferred← citeshttps://doi.org/10.3390/en16176377openalex-snapshot
Privacy-Preserving Fleet-Wide Learning of Wind Turbine Conditions with Federated Learning
inferred← citeshttps://doi.org/10.3390/jmse13071337openalex-snapshot
Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model
inferred← citeshttps://doi.org/10.5194/wes-10-1137-2025openalex-snapshot
Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.