Jawad Hameed^*, Jiefeng Hu, Md Liton Hossain, Syed Islam

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2026.074998 - 27 May 2026

Abstract This paper presents a dynamic energy management strategy for a community-scale campus hybrid microgrid integrating photovoltaic (PV) generation, aggregated wind power, a proton exchange membrane fuel cell, and battery energy storage to support electric vehicle (EV) charging infrastructure under variable environmental and load conditions. The system configuration is inspired by existing renewable energy installations and planned developments at the Federation University Mt Helen Campus, enabling realistic modeling of aggregated demand and coordinated multi-source operation. To enhance physical realism, power electronic conversion efficiencies and hierarchical control dynamics are incorporated, while the wind subsystem is represented using… More >

Peng Wang^1,2, Zhiying Gao^1,2,*, Yongyan Chen¹, Rina Su^1,2, Yefei Bai², Jianlong Ma^1,2, Tianhao Zhang¹

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.073862 - 27 May 2026

Abstract We propose a novel procedure, Time-Domain De-Dopplerized Orthogonal Matching Pursuit deconvolution approach for the mapping of acoustic sources (TD-OMP-DAMAS), for separating aerodynamic noise sources distributed across wind turbine blades (WTB), a task that is typically hindered by mutual interference and spatial mixing. The proposed procedure is a two-stage, hybrid de-Doppler/sparse-reconstruction algorithm based on time-domain de-Doppler (TD, Stage 1) and an orthogonal matching pursuit (OMP)-based deconvolution scheme (Stage 2), enabling sparse-reconstruction techniques to be effectively applied in rotating-source scenarios. The method is validated using both simulated rotating-source data and wind-tunnel measurements, and its performance is systematically… More >

Wei Chen, Zhi Wei^*, Tingting Pei, Jianghao Zhu, Yang Wu

Energy Engineering, Vol.123, No.6, 2026, DOI:10.32604/ee.2025.073843 - 27 May 2026

Abstract Wind turbine gearboxes are critical components in large-scale power generation systems, and their unexpected failures often result in significant economic losses, long downtime, and decreased energy efficiency. Accurate prediction of their Remaining Useful Life (RUL) is therefore vital for enhancing operational reliability, implementing condition-based maintenance, and optimizing lifecycle management. However, existing approaches often neglect the memory effect in degradation processes and fail to establish an effective interaction between stochastic degradation modeling and RUL prediction. To address these challenges, this study proposes a novel fusion method that integrates a stochastic degradation model with an intelligent prediction framework.… More >

Mohammed Yahya^1,*, Safaaldeen A. Sulyman², Joban Sahota¹, Gursingh Aikum Dhugga¹, Saiakash Shunmugavel¹

Structural Durability & Health Monitoring, Vol.20, No.3, 2026, DOI:10.32604/sdhm.2026.074828 - 18 May 2026

Abstract The structural integrity and longevity of wind turbine blades are critical determinants of the efficiency and reliability of wind energy systems. As the primary components responsible for converting kinetic wind energy into mechanical torque and subsequently electrical power, the aerodynamic, structural, and material characteristics of rotor blades directly influence turbine performance and operational lifespan. This research addresses the limitations of conventional blade designs, often characterized by stress concentration, fatigue damage, and dynamic instability by introducing a novel diamond-lattice internal support structure aimed at improving mechanical strength, fatigue resistance, and dynamic stability. Finite element simulations performed… More >

Segundo Esteban^1,*, Matilde Santos²

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.077663 - 27 April 2026

Abstract Wind turbines are highly efficient energy converters that exploit locally available renewable resources across many regions. In modern floating offshore wind turbines (FOWTs), strong aerodynamic and hydrodynamic loads give rise to nonlinear and tightly coupled dynamics, which typically require dedicated—and computationally demanding—simulation tools for analysis and control design. This work introduces a simplified, control-oriented mathematical model of a FOWT, derived directly from fundamental force and torque balances and explicitly incorporating the gyroscopic effect, which is often neglected in onshore wind turbines due to its comparatively lower significance. Model parameters are identified for the NREL 5-MW… More > Graphic Abstract

Serhiy Serbin^1,*, Bohdan Lychko², Kateryna Burunsuz¹

Energy Engineering, Vol.123, No.5, 2026, DOI:10.32604/ee.2026.076966 - 27 April 2026

Abstract This study investigates the use of ammonia as a carbon-free fuel for gas turbines in decarbonized hybrid energy systems. The objective is to predict the emission characteristics of a gas turbine combustion chamber operating on gaseous ammonia by employing detailed combustion kinetics. The chamber is modeled as a network of chemical reactors to simulate the primary reaction zone and the secondary air-mixing zone. The model is based on solving mass and energy conservation equations for chemically reacting flows. Four high-temperature ammonia oxidation mechanisms, comprising 71 to 286 chemical reactions, were used as kinetic schemes. New More >

Yajuan Lu^*, Yongtao Hu, Jie Li, Jinping Zhang, Jingjing Si

Structural Durability & Health Monitoring, Vol.20, No.2, 2026, DOI:10.32604/sdhm.2025.071110 - 31 March 2026

Abstract To address challenges in wind turbine blade defect detection models, primarily due to insufficient feature extraction capabilities and the difficulty of deploying models on drone-type edge devices, this study proposes a wind turbine blade defect detection model, WtCS-YOLO11, that incorporates multiscale feature extraction and an attention mechanism. Firstly, the cross-stage partial with two kernels and a wavelet convolution module (C3k2_WTConv) is proposed by introducing wavelet convolution into the module. The cross-stage partial with two kernels (C3k2) module in the necking network is replaced with the C3k2_WTConv module to increase the model’s receptive field, enable multiscale… More >

Boyi Cui^1,2, Kai Long^1,*, Ayesha Saeed¹, Nianzhi Guo¹, Guangxing Wu¹, Hui Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.146, No.3, 2026, DOI:10.32604/cmes.2026.078151 - 30 March 2026

Abstract Lattice-type ultra-tall wind turbine towers are popular in China for their modular benefits in fabrication, transportation, and installation. Nonetheless, their conceptual design remains predominantly dependent on engineering experience, and a generally applicable approach is still absent. This study proposes a self-similar modular topology optimization framework for lattice-type wind turbine support structures and develops software for its application. A minimum weighted compliance formulation with a prescribed volume fraction is developed utilizing the variable density approach, wherein modular constraints and their corresponding sensitivity expressions are explicitly included. The method is applied to a reference wind turbine model More >

Zhixiang Zhang¹, Chao Luo², Chen Zhang^1,*, Zheng Li¹, Yihua Zhu², Xu Cai¹

Energy Engineering, Vol.123, No.4, 2026, DOI:10.32604/ee.2025.072678 - 27 March 2026

Abstract Fatigue loads on wind turbines are critical factors that significantly influence operational lifespan and reliability. The passive yaw control of wind turbines often fails to capture the dynamic gradient changes of wind speed and direction in the wind field, leading to an increased risk of load overload, severely affecting operational lifespan and reducing power generation efficiency. This impact is even more pronounced during the passage of a cold front. To address this issue, this paper proposes an independent variable-pitch control method that optimizes predictions by utilizing the spatiotemporal relationship between pre-observed cold front patterns and… More >

Samar Taha Yousif^1,2, Firas Basim Ismail^1,3,*, Ammar Al-Bazi⁴, Alaa Abdulhady Jaber⁵, Sivadass Thiruchelvam¹

Energy Engineering, Vol.123, No.3, 2026, DOI:10.32604/ee.2025.073955 - 27 February 2026

Abstract Thermal power plants are the main contributors to greenhouse gas emissions. The prediction of the emission supports the decision makers and environmental sustainability. The objective of this study is to enhance the accuracy of emission prediction models, supporting more effective real-time monitoring and enabling informed operational decisions that align with environmental compliance efforts. This paper presents a data-driven approach for the accurate prediction of gas emissions, specifically nitrogen oxides (NOx) and carbon monoxide (CO), in natural gas power plants using an optimized hybrid machine learning framework. The proposed model integrates a Feedforward Neural Network (FFNN)… More > Graphic Abstract