Search Results (184)

With the increasing penetration of renewable energy, power system frequency stability faces multiple challenges. In addition to the decline of system inertia traditionally provided by synchronous machines, uncertainties such as wind power forecast errors, converter control characteristics, and stochastic load fluctuations further exacerbate the system’s sensitivity to power disturbances, increasing the risks of frequency deviation and instability. Among these factors, insufficient inertia is widely recognized as one of the most direct and critical drivers of the initial frequency response. This study focuses on this issue and explores the use of battery energy storage system (BESS) parameter optimization to enhance system stability. To this end, a simulation platform was developed in PSS^®E V34 based on the IEEE New England 39-bus system, incorporating three wind turbines and two BESS units. The WECC generic models were adopted, and three wind disturbance scenarios were designed, including (i) disconnection of a single wind turbine, (ii) derating of two turbines to 50% output, and (iii) derating of three turbines to 50% output. In this study, a one-at-a-time (OAT) sensitivity analysis was first performed to identify the key parameters affecting frequency response, followed by optimization using an improved particle swarm optimization (IPSO) algorithm. The simulation results show that the minimum system frequency was 59.888 Hz without BESS control, increased to 59.969 Hz with non-optimized BESS control, and further improved to 59.976 Hz after IPSO. Compared with the case without BESS, the overall improvement was 0.088 Hz, of which IPSO contributed an additional 0.007 Hz. These results clearly demonstrate that IPSO can significantly strengthen the frequency support capability of BESS and effectively improve system stability under different wind disturbance scenarios. Full article

Ice loads continue to pose challenges to the structural safety of offshore wind turbines (OWTs), while the rapid development of offshore wind power in cold regions is enabling the deployment of OWTs in deeper waters. To accurately simulate the dynamic response of an OWT under combined ice–wind loading, this paper proposes a Discrete Element Method–Wind Turbine Integrated Analysis (DEM-WTIA) framework. The framework can synchronously simulate discontinuous ice-crushing processes and aeroelastic–structural dynamic responses through a holistic turbine model that incorporates rotor dynamics and control systems. To address the issue of insufficient prediction accuracy for dynamic responses, we introduced a multivariate time series forecasting method that integrates a secondary decomposition strategy with a hybrid prediction model. First, we developed a parallel signal processing mechanism, termed Adaptive Complete Ensemble Empirical Mode Decomposition with Improved Singular Spectrum Analysis (CEEMDAN-ISSA), which achieves adaptive denoising via permutation entropy-driven dynamic window optimization and multi-feature fusion-based anomaly detection, yielding a noise suppression rate of 76.4%. Furthermore, we propose the F-Transformer prediction model, which incorporates a Frequency-Enhanced Channel Attention Mechanism (FECAM). By integrating the Discrete Cosine Transform (DCT) into the Transformer architecture, the F-Transformer mines hidden features in the frequency domain, capturing potential periodicities in discontinuous data. Experimental results demonstrate that signals processed by ISSA exhibit increased signal-to-noise ratios and enhanced fidelity. The F-Transformer achieves a maximum reduction of 31.86% in mean squared error compared to the standard Transformer and maintains a coefficient of determination (R²) above 0.91 under multi-condition coupled testing. By combining adaptive decomposition and frequency-domain enhancement techniques, this framework provides a precise and highly adaptable ultra-short-term response forecasting tool for the safe operation and maintenance of offshore wind power in cold regions. Full article

Accurate modelling of near surface wind speeds is essential for robust resource assessment, turbine design, and grid integration. This study presents a unified framework comparing four candidate marginal distributions—Weibull, Gamma, Lognormal, and Generalised Extreme Value (GEV)—across 21 years of daily observations from 11 sites in New South Wales and southern Queensland, Australia. Parameters are estimated by maximum likelihood, with L-moments used when numerical fitting fails. Univariate goodness-of-fit is evaluated via information criteria (Akaike Information Criterion, AIC; Bayesian Information Criterion, BIC) and distributional tests (Anderson–Darling, Cramér–von Mises, Kolmogorov–Smirnov). To capture spatial dependence, we fit an 11-dimensional regular vine (“R-vine”) copula to the probability-integral-transformed data, selecting pair-copula families by AIC and estimating parameters by sequential likelihood. A composite score (70% univariate, 30% copula) ranks distributions per location. Results demonstrate that Lognormal best matches central behaviour at most sites, Weibull remains competitive for bulk modelling, Gamma often excels in moderate tails, and GEV best represents extremes. All turbine yield results presented are illustrative, showing how statistical choices impact energy estimates; they should not be interpreted as operational forecasts. In a case study, 5000 joint simulations from the top-two models drive IEC V90 and E82 power curves, revealing up to 10% variability in annual energy yield due solely to marginal choice. This workflow provides a replicable template for comprehensive wind resource and load hazard analysis in complex terrains. Full article

As the deployment of wind energy systems continues to rise globally, ensuring the reliability and efficiency of grid-connected Doubly Fed Induction Generator (DFIG) wind turbines has become increasingly critical. Two core challenges faced by these systems include fault diagnosis in power electronic converters and accurate prediction of wind conditions for adaptive power control. Recent advancements in artificial intelligence (AI) have introduced powerful tools for addressing these challenges. This study presents the first unified comparative performance analysis of two deep learning-based models: (i) a Convolutional Neural Network-Long Short-Term Memory CNN-LSTM with Variational Mode Decomposition for real-time Grid Side Converter (GSC) fault diagnosis, and (ii) an Incremental Generative Adversarial Network (IGAN) for wind attribute prediction and adaptive droop gain control, applied to grid-integrated DFIG wind turbines. Unlike prior studies that address fault diagnosis and wind forecasting separately, both models are evaluated within a common MATLAB/Simulink framework using identical wind profiles, disturbances, and system parameters, ensuring fair and reproducible benchmarking. Beyond accuracy, the analysis incorporates multi-dimensional performance metrics such as inference latency, robustness to disturbances, scalability, and computational efficiency, offering a more holistic assessment than prior work. The results reveal complementary strengths: the CNN-LSTM achieves 88% accuracy with 15 ms detection latency for converter faults, while the IGAN delivers more than 95% prediction accuracy and enhances frequency stability by 18%. Comparative analysis shows that while the CNN-LSTM model is highly suitable for rapid fault localization and maintenance planning, the IGAN model excels in predictive control and grid performance optimization. Unlike prior studies, this work establishes the first direct comparative framework for diagnostic and predictive AI models in DFIG systems, providing novel insights into their complementary strengths and practical deployment trade-offs. This dual evaluation lays the groundwork for hybrid two-tier AI frameworks in smart wind energy systems. By establishing a reproducible methodology and highlighting practical deployment trade-offs, this study offers valuable guidance for researchers and practitioners seeking explainable, adaptive, and computationally efficient AI solutions for next-generation renewable energy integration. Full article

Wind turbine power forecasting is crucial for optimising energy production, planning maintenance, and enhancing grid stability. This research focuses on predicting the output of a Senvion MM92 wind turbine at the Kelmarsh wind farm in the UK using SCADA data from 2020. Two approaches are explored: a hybrid model combining Stochastic Differential Equations (SDEs) with Neural Networks (NNs) and Deep Learning models, in particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and the Combination of Convolutional Neural Networks (CNNs) and LSTM. Notably, while SDE-NN models are well suited for predictions in cases where data patterns are chaotic and lack consistent trends, incorporating stochastic processes increases the complexity of learning within SDE models. Moreover, it is worth mentioning that while SDE-NNs cannot be classified as purely “white box” models, they are also not entirely “black box” like traditional Neural Networks. Instead, they occupy a middle ground, offering improved interpretability over pure NNs while still leveraging the power of Deep Learning. This balance is precious in fields such as wind power prediction, where accuracy and understanding of the underlying physical processes are essential. The evaluation of the results demonstrates the effectiveness of the SDE-NNs compared to traditional Deep Learning models for wind power prediction. The SDE-NNs achieve slightly better accuracy than other Deep Learning models, highlighting their potential as a powerful alternative. Full article

Renewable energy systems like solar and wind power are the main source of sustainable energy production; however, their intermittent nature produces challenges for grid integration, so they require realistic forecast models. This study developed a Long Short-Term Memory (LSTM) neural network model to predict solar irradiance and wind power over a 24 h horizon using a 240 h (10-day) dataset. The dataset, being hourly measurements of solar irradiance (W/m²) and wind speed (m/s), was divided and normalized into 193 sequences of 24 h each, with 80% for training and 20% for validation. Two LSTM models, each consisting of 100 hidden units, were trained using the Adam optimizer to predict the next 24 h for each of the variables using forget, input, and output gates to capture temporal dependencies. The results have shown that the model accurately forecasted solar irradiance with a clear day–night cycle, while forecasts of wind speed revealed higher variability, although the PV system was better than the wind system due to low wind speeds. The results reveal that the LSTM model can effectively predict renewable energy output by predicting the wind speed and Solar Irradiance, which are the main parameters that control the output power of wind turbines and PV power, respectively. Full article

Wind turbine radar echoes exhibit significant scattering power and Doppler spectrum broadening effects, which can interfere with the detection of meteorological targets and subsequently impact weather prediction and disaster warning decisions. In operational weather radar applications, the influence of wind farm on radar observations must be fully considered by meteorological departments and related institutions. In this paper, a Wind Turbine Clutter Classification Algorithm based on Random Forest (WTCDA-RF) classification is proposed. The level-II radar data is processed in blocks, and the spatial position invariance of wind farm clutter is leveraged for feature extraction. Samples are labeled based on position information, and valid samples are screened and saved to construct a vector sample set of wind farm clutter. Through training and optimization, the proposed WTCDA-RF model achieves an ACC of 90.92%, a PRE of 89.37%, a POD of 92.89%, and an F1-score of 91.10%, with a CSI of 83.65% and a FAR of only 10.63%. This not only enhances the accuracy of weather forecasts and ensures the reliability of radar data but also provides operational conditions for subsequent clutter removal, improves disaster warning capabilities, and ensures timely and accurate warning information under extreme weather conditions. Full article

With the increasing integration of renewable energy, virtual power plants (VPPs) have emerged as key market participants by aggregating distributed energy resources. However, their involvement in electricity markets is increasingly challenged by two major uncertainties: price volatility and the intermittency of renewable generation. This study presents the first application of Information Gap Decision Theory (IGDT) within a two-stage cooperative scheduling framework for VPPs. A novel bidding strategy model is proposed, incorporating both robust and opportunistic optimization methods to explicitly account for decision-making behaviors under different risk preferences. In the day-ahead stage, a risk-responsive bidding mechanism is designed to address price uncertainty. In the real-time stage, the coordinated dispatch of micro gas turbines, energy storage systems, and flexible loads is employed to minimize adjustment costs arising from wind and solar forecast deviations. A case study using spot market data from Shandong Province, China, shows that the proposed model not only achieves an effective balance between risk and return but also significantly improves renewable energy integration and system flexibility. This work introduces a new modeling paradigm and a practical optimization tool for precision trading under uncertainty, offering both theoretical and methodological contributions to the coordinated operation of flexible resources and the design of electricity market mechanisms. Full article

To mitigate the risks posed by uncertainties in renewable energy output and Electric Vehicle (EV) travel patterns on the scheduling of Virtual Power Plants (VPPs), this paper proposes an optimal scheduling model for a VPP incorporating EVs based on Information Gap Decision Theory (IGDT). First, a Monte Carlo load forecasting model is established based on the behavioral characteristics of EV users, and a Sigmoid function is introduced to quantify the dynamic relationship between user response willingness and VPP incentive prices. Second, within the VPP framework, an economic optimal scheduling model considering multi-source collaboration is developed by integrating wind power, photovoltaics, gas turbines, energy storage systems, and EV clusters with Vehicle-to-Grid (V2G) capabilities. Subsequently, to address the uncertain parameters within the model, IGDT is employed to construct a bi-level decision-making mechanism that encompasses both risk-averse and opportunity-seeking strategies. Finally, a case study on a VPP is conducted to verify the correctness and effectiveness of the proposed model and algorithm. The results demonstrate that the proposed method can effectively achieve a 7.94% reduction in the VPP’s comprehensive dispatch cost under typical scenarios, exhibiting superiority in terms of both economy and stability. Full article

Wind turbine power curve modeling is essential for wind power forecasting, turbine performance monitoring, and predictive maintenance. However, SCADA data often contain anomalies (e.g., curtailment, sensor faults), degrading the accuracy of power curve predictions. This paper presents a parameter-transfer learning strategy within a preprocessing and modeling framework that jointly optimizes anomaly detection (iForest, LOF, DBSCAN) and WTPC regressors (MLP, RF, GP) via a multi-metric objective adaptable to specific modeling requirements. In the source domain, hyperparameters are explored with randomized search, and in the target domain, transferred settings are refined with Bayesian optimization. Applied to real SCADA from different locations and turbine models, the approach achieves a 90% reduction in optimization iterations and consistently improves target domain performance according to the objective, with no observed loss when comparable source and target turbines differ in site or rated power. Gains are larger for more similar source–target pairs. Overall, the approach yields a practical model-agnostic pipeline that accelerates preprocessing and modeling while preserving or improving fit, particularly for newly installed turbines with limited data. Full article

This publication presents a novel approach to predicting the amount of electricity generated by wind power plants. The research focuses on data-driven models such as XGBoost, Liquid Time-constant Networks, and covers both the analysis of properties of individual forecasting models as well as aspects of their integration into a hybrid model. By analyzing real-world weather scenarios, the approach aims to identify the highest accuracy forecasting model for the short-term 24-h forecast of wind farm power output. A more accurate forecast allows for more efficient resource planning and better distribution of resources on the electricity grids, thus ensuring a greener approach to energy production. The study shows that the proposed Hybrid (XGBoost + LTC) model predicts wind power generation with an nMAE of 0.0856, representing an improvement over standalone XGBoost and LTC models, and outperforming classical approaches such as LSTM and statistical models like ARIMAX in terms of forecasting accuracy. Full article

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article

Transitioning to sustainable energy systems is crucial for reducing greenhouse gas (GHG) emissions, especially in remote industrial operations where diesel generators remain the dominant power source. This study examines the feasibility of integrating a redox flow battery (RFB) storage system to optimize wind energy utilization at the Raglan mining site in northern Canada, with the goal of reducing diesel dependency, enhancing grid stability, and improving energy security. To evaluate the effectiveness of this hybrid system, a MATLAB R2024b-based simulation model was developed, incorporating wind energy forecasting, load demand analysis, and economic feasibility assessments across multiple storage and wind penetration scenarios. Results indicate that deploying 12 additional E-115 wind turbines combined with a 20 MW/160 MWh redox flow battery system could lead to diesel savings of up to 63.98%, reducing CO₂ emissions by 68,000 tonnes annually. However, the study also highlights a key economic challenge: the high Levelized Cost of Storage (LCOS) of CAD (Canadian dollars) 7831/MWh, which remains a barrier to large-scale implementation. For the scenario with high diesel economy, the LCOS was found to be CAD 6110/MWh, and the corresponding LCOE was CAD 590/MWh. While RFB integration improves system reliability, its economic viability depends on key factors, including reductions in electrolyte costs, advancements in operational efficiency, and supportive policy frameworks. This study presents a comprehensive methodology for evaluating energy storage in off-grid industrial sites and identifies key challenges in scaling up renewable energy adoption for remote mining operations. Full article

Wind power generation provides clean and renewable electricity for microgrids, but its intermittency and uncertainty pose challenges to the operation and power quality of microgrids. Accurate forecasting is conducive to maintaining the stability of microgrids and improving the efficiency of energy management. Therefore, this study proposes a dual-branch frequency transformer (DBFformer), which leverages multi-scale spectral transformation and the multi-head attention mechanism to improve the prediction accuracy of microgrid wind turbines. In the encoder, two parallel branches are designed to extract the global features and local dynamic features of meteorological data based on Fourier transform and wavelet transform, respectively. In the decoder, an exponential smoothing attention (ESA) mechanism and a frequency attention (FA) mechanism are introduced to extract multi-scale temporal features. ESA enhances the model’s ability to capture long-term growth trends, whereas FA focuses on periodic pattern recognition. Additionally, to further optimize the model’s performance, a periodic weight coefficient (PWC) mechanism is employed to dynamically adjust the fusion coefficients to further improve the fusion performance and prediction accuracy. The factors influencing wind turbine power are analyzed; then, the most relevant factors are selected for the experiment. According to the experimental results, the proposed DBFformer accurately predicts the output power of wind turbines and exhibits superior performance. It achieves lower mean squared error (MSE) and mean absolute error (MAE) values than other state-of-the-art models. Specifically, its MSE values are 0.195, 0.216, 0.457, and 0.583, and the corresponding MAE values are 0.318, 0.335, 0.474, and 0.503 for different rated wind turbines. Furthermore, comprehensive ablation experiments validate that the dual-branch structure, frequency transformations, dual-attention mechanisms, and PWC module have a positive impact on the proposed model. Therefore, this research offers a novel and effective approach for wind power forecasting and supports the broader goal of integrating clean energy into microgrids. Full article

Offshore wind farms play a pivotal role in the global transition to clean energy but remain susceptible to diverse meteorological hazards—ranging from highly variable wind speeds and temperature anomalies to severe oceanic disturbances—that can jeopardize both turbine safety and overall power output. Although Hidden Markov Models (HMMs) have a longstanding track record in operational forecasting, this study leverages and extends their capabilities by introducing a dynamic HMM framework tailored specifically for multi-risk offshore wind applications. Building upon historical datasets and expert assessments, the proposed model begins with initial transition and observation probabilities and then refines them adaptively through periodic or event-triggered recalibrations (e.g., Baum–Welch), thus capturing evolving weather patterns in near-real-time. Compared to static Markov chains, naive Bayes classifiers, and RNN (LSTM) baselines, our approach demonstrates notable accuracy gains, with improvements of up to 10% in severe weather conditions across three industrial-scale wind farms. Additionally, the model’s minutes-level computational overhead for parameter updates and state decoding proves feasible for real-time deployment, thereby supporting proactive scheduling and maintenance decisions. While this work focuses on the core dynamic HMM method, future expansions may incorporate hierarchical structures, Bayesian uncertainty quantification, and GAN-based synthetic data to further enhance robustness under high-dimensional measurements and rare, long-tail meteorological events. In sum, the multi-risk forecasting methodology presented here—though built on an established HMM concept—offers a practical, adaptive solution that significantly bolsters safety margins and operational reliability in offshore wind power systems. Full article