Article
Energy & Fuels
Mahmoud Ibrahim, Anton Rassolkin, Toomas Vaimann, Ants Kallaste, Janis Zakis, Van Khang Hyunh, Raimondas Pomarnacki
Summary: Digital twins are used to create virtual replicas of wind turbine generators, enabling operators to monitor and optimize their performance by simulating their behavior in real time. A DT-based sensing methodology is proposed to overcome the limitations of physical wind speed sensors by augmenting them with virtual sensor arrays.
Article
Engineering, Electrical & Electronic
Yang Li, Xiaojun Shen
Summary: Wind speed sensors of wind turbines often suffer from performance degradation or failures due to inherent issues or environmental influence, which directly impact the measurement quality and safe operation. To address the inaccuracy and missing data issues of physical sensors, this study proposes a digital twin-driven sensing methodology that transforms the physical sensor platform into virtual sensor arrays, identifying faulty sensors and providing estimated data. The proposed method utilizes estimators, verifiers, setters, and selectors to focus on fault identification, data verification, and reconstruction, improving the reliability of sensors in engineering applications.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Geosciences, Multidisciplinary
Eduardo Utrabo-Carazo, Cesar Azorin-Molina, Enric Aguilar, Manola Brunet
Summary: This study analyzes the observed surface mean wind speed (SWS) and gusts over the Iberian Peninsula (IP) in the frequency domain for 1961-2019, aiming to explore sources of predictability in the interannual and decadal scales. The main finding is the strong correlation between surface winds and the stratospheric polar vortex, with a time lag of 2-3 months, indicating that the polar vortex modulates winds in the region. Additionally, the study reveals a decoupling of SWS and gusts on a 9-11 year timescale, showing a marked seasonal dependence in intensity. There are also discrepancies between observed data and simulated data, suggesting inaccuracies in reproducing the variability of surface wind speeds.
GEOPHYSICAL RESEARCH LETTERS
(2023)
Article
Computer Science, Information Systems
J. Enrique Sierra-Garcia, Matilde Santos
Summary: This study focuses on the wind measurement issue with floating offshore wind turbines and proposes a novel pitch neuro-control architecture based on neuro-estimators of the effective wind. The control system includes a PID controller, a lookup table, a neuro-estimator, and a virtual sensor, with neural networks trained online to adapt to changes in the environment. Intensive simulations validate the effectiveness of this neuro control approach, showing an improvement of 16% for sinusoidal wind and an average improvement of 8% compared to a PID controller.
Article
Green & Sustainable Science & Technology
Lin Pan, Yong Xiong, Ze Zhu, Leichong Wang
Summary: This study proposes a modeling method of soft measurement of offshore wind speed using Kernal Extreme Learning Machine (KELM) and improves the measurement accuracy through optimization algorithm. The established measurement model is verified by simulation and the effectiveness of the research is demonstrated with the design of a rotor controller.
Article
Mathematics
Xiang Ying, Keke Zhao, Zhiqiang Liu, Jie Gao, Dongxiao He, Xuewei Li, Wei Xiong
Summary: In this paper, a new wind speed prediction method based on collaborative filtering and the virtual edge expansion graph structure is proposed, which can effectively learn and utilize the spatial correlation of wind speed sequences, and shows superior predictive performance compared to traditional methods.
Article
Chemistry, Physical
Qinghao Xu, Yuting Lu, Shiyu Zhao, Ning Hu, Yawei Jiang, Hang Li, Yue Wang, Haiqi Gao, Yi Li, Ming Yuan, Liang Chu, Jiahui Li, Yannan Xie
Summary: This paper proposes a novel wind vector sensor system that can synchronously perceive wind speed and direction based on self-powered technology and photoelectric technology, achieving accurate monitoring of wind direction and paving the way for ultra-low power consumption for IoTs-based environmental sensors.
Article
Engineering, Multidisciplinary
Cunyou Su, Jianxin Wu, Guangjun Jiang, Nan Zhang, Xiaowen Song
Summary: The uncertainty of wind speed has a significant impact on the reliability of wind turbines. Previous studies often neglected the influence of time and space distribution on wind speed and relied on average or rated wind speed. This study analyzed the spatial distribution of wind speed based on ground observations from four meteorological stations and found that both spatial and temporal distribution are important factors affecting wind speed uncertainty.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL
(2023)
Article
Engineering, Electrical & Electronic
Danyal Bustan, Hoda Moodi
Summary: An adaptive interval type-2 fuzzy controller is proposed for variable-speed and variable-pitch wind turbines. The controller is independent of wind speed estimation, resulting in cost and computational burden savings. Simulations demonstrate that the proposed controller enhances power generation and reduces mechanical loads compared to the baseline controller.
JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY
(2022)
Article
Engineering, Electrical & Electronic
Joakim Bjork, Daniel Pombo, Karl Henrik Johansson
Summary: This study presents a wind turbine model useful for fast frequency reserves (FFR) and tests it in a simulation of the Nordic synchronous grid. The results demonstrate that the combination of wind and hydro turbines not only meets the latest regulations but also provides a smooth response and avoids overshoot during frequency recovery.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2022)
Article
Engineering, Marine
Izleena Md Iqbar, Masdi Muhammad, Syed Ihtsham Ul-Haq Gilani, Frank Adam
Summary: This study explores the feasibility of using wind energy as a micro-grid solution for offshore oil and gas platforms in low wind speed regions. A hybrid power generation concept is proposed, which replaces one gas turbine generator with a floating horizontal axis wind turbine system. The economic evaluation shows that the hybrid approach has a net present value difference of 22% to 37% compared to conventional generation, and the levelized cost of energy for wind turbine is 39% lower than for gas turbine-only operations.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Construction & Building Technology
Mohamed M. Takeyeldein, I. S. Ishak, Tholudin M. Lazim
Summary: This research proposes a new design of wind-lens turbine that can startup and operate at low wind speeds, and it can significantly improve the performance of the turbine.
WIND AND STRUCTURES
(2022)
Article
Energy & Fuels
YuPeng Wu, WenBing Wu
Summary: The wavelet packet decomposition method can divide signals into multiple frequency bands, which helps improve the accuracy of fault diagnosis for mechanical vibration signals. Signals with obvious differences in specific frequency bands are easier to distinguish, leading to a more effective engine fault diagnosis rate.
Article
Thermodynamics
Peng Chen, Dezhi Han
Summary: This paper proposes an effective wind speed estimation method for wind turbines, which considers the variation in blade radius and reconstructs the aerodynamic mapping. The method utilizes neural network models to estimate and reconstruct wind speed, effective radius, and aerodynamic mapping, and employs LSTM neural network for real-time estimation and prediction. Experimental results demonstrate that the proposed method has high accuracy and anti-interference capability.
Article
Energy & Fuels
Zimo Zhu, Jian Zhang, Songye Zhu, Jun Yang
Summary: This paper introduces a unified linear input and state estimator algorithm that eliminates the need for full-rank feedthrough matrix. The algorithm is experimentally tested on a scaled wind turbine model and can accurately estimate unknown excitations and unmonitored dynamic responses. A comprehensive structural health monitoring system is deployed to measure the tower's dynamic responses.
Article
Automation & Control Systems
Tiange Wang, Zijun Zhang, Kwok-Leung Tsui
Summary: This article proposes a deep generative approach for detecting foreign objects on railway tracks. The approach involves training a model using an autoencoder and discriminator, detecting abnormal images based on anomaly scores obtained from the trained autoencoder, and filtering normal areas to highlight abnormal areas for foreign object detection.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Interdisciplinary Applications
Haike Qiao, Zijun Zhang, Qin Su
Summary: This study investigates the adoption of blockchain technology (BCT) for verifying product recycling information (VPRI) in the presence of original and green consumers. Three modes of the manufacturer are analyzed: no adoption of BCT, adopting own BCT, and adopting a third-party BCT platform for VPRI. The results show that the manufacturer's adoption mode depends on the scaling cost and the proportion of BCT verified to all recycling information. Furthermore, the study finds that BCT adoption can have negative environmental impacts, especially when the carbon emission rate of recycled products is higher.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Zhong Zheng, Zijun Zhang
Summary: In this article, a stochastic recurrent encoder-decoder neural network (SREDNN) is developed for generative multistep probabilistic wind power predictions. The SREDNN considers latent random variables in its recurrent structures and provides two critical advantages compared to conventional RNN-based methods: it models wind power distribution using an infinite Gaussian mixture model (IGMM) and describes complex patterns across wind speed and power sequences by updating hidden states in a stochastic way. Computational experiments demonstrate the advantages and effectiveness of the SREDNN for wind power prediction.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Luoxiao Yang, Long Wang, Zijun Zhang
Summary: The study presents a novel method, called DITU-net, for automating wind power curve (WPC) modeling without data pre-processing. The proposed approach incorporates a data-synthesis-informed-training (DIT) process to generate diverse training samples, which are then used to train a U-net model for the generation of neat WPC. Additionally, a pixel mapping and correction process is developed to derive a mathematical form depicting the neat WPC. The method eliminates the need for data preprocessing and achieves superior performance compared to classical WPC modeling methods.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Automation & Control Systems
Zhenling Mo, Zijun Zhang, Qiang Miao, Kwok-Leung Tsui
Summary: This article introduces a new dynamic bandit tree (DBT) algorithm to help achieve more adaptive filters and reduce the burden of parameter tuning in frequency band searching. By optimizing the boundaries of Meyer wavelet filters, this method can better identify demodulated fault frequencies and outperform other optimization algorithms and fault diagnosis methods in tests.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2023)
Article
Computer Science, Artificial Intelligence
Lanlan Zheng, Xin Liu, Feng Wu, Zijun Zhang
Summary: This paper addresses the two-dimensional shelf space allocation problem (2DSSAP) in the retail field by proposing a data-driven model assisted hybrid genetic algorithm (DMA-HGA). The proposed DMA-HGA applies an improved genetic algorithm (GA) to optimize solutions and a two-stage search assistance module to enhance the search process. Experimental results demonstrate that the DMA-HGA outperforms benchmarking methods in terms of solution quality and accuracy. The extended discussion of parameters also provides valuable management insights for the 2DSSAP.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Energy & Fuels
Zicheng Fei, Zijun Zhang, Fangfang Yang, Kwok-Leung Tsui
Summary: This paper proposes a new approach to predict the remaining useful life (RUL) of lithium-ion batteries using a limited number of incomplete cycles. The attention-assisted temporal convolutional memory-augmented network (ATCMN) is developed to achieve accurate and rapid RUL prediction under this challenging scenario. Experimental results demonstrate the effectiveness and generalizability of the proposed ATCMN compared to state-of-the-art methods.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Computer Science, Artificial Intelligence
Zhong Zheng, Zijun Zhang
Summary: This paper proposes a temporal convolutional recurrent autoencoder framework for more effective time series compression. Experimental results show that the proposed method outperforms benchmarking models in terms of lower reconstruction errors with the same compression ratio, indicating its promising potential for various applications involving long time series data.
APPLIED SOFT COMPUTING
(2023)
Article
Thermodynamics
Hong Liu, Luoxiao Yang, Bingying Zhang, Zijun Zhang
Summary: This paper presents a pioneering attempt of studying a two-channel deep network modeling method for wind power predictions that leverages both wind farm data and farm geoinformation. Through comprehensive computational experiments and comparison with benchmarking models, the value of this modeling approach is confirmed, achieving a new state-of-the-art prediction performance.
Article
Engineering, Civil
Tiange Wang, Zijun Zhang, Kwok-Leung Tsui
Summary: Predicting vehicle crashes accurately is challenging due to the dominance of non-crash data. Existing studies have limitations in predicting crashes in advance. This paper proposes a crash alarm model for vehicles (CAMV) that uses vehicle operational data. The proposed model utilizes a non-crash learning block (NCLB) and a coarse-to-fine strategy to generate and calibrate alarms. Experimental results show the effectiveness of CAMV and its superiority over benchmarking methods.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Hong Liu, Zijun Zhang
Summary: This paper presents a pioneering study on using numerical weather predictions to predict future renewable power output while preserving data privacy. It introduces a novel bi-party engaged data-driven modeling framework (BEDMF) that learns local and global latent features and captures spatial-temporal patterns among multiple sites. Experimental results show that the BEDMF achieves at least 3% improvement on average compared to famous baselines.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Green & Sustainable Science & Technology
Zhong Zheng, Luoxiao Yang, Zijun Zhang
Summary: In this article, a conditional variational autoencoder based method is proposed for the probabilistic wind power curve modeling task. Experimental results show that the proposed method has high performance and reliability in simulating wind power curves.
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
(2023)
Article
Energy & Fuels
Zicheng Fei, Zijun Zhang, Fangfang Yang, Kwok-Leung Tsui
Summary: This study develops a deep learning powered method for rapid lifetime classification of lithium-ion batteries using limited early-cycle data. The method integrates spatial, temporal, and physical battery information, extracts high-level latent features, and classifies batteries accurately.
Article
Automation & Control Systems
Zhenling Mo, Zijun Zhang, Qiang Miao, Kwok-Leung Tsui
Summary: This work introduces a sparsity-constrained invariant risk minimization (SCIRM) framework for machinery fault diagnosis. By integrating sparsity constraints, SCIRM develops machine learning models with better generalization capacities and achieves higher accuracy and generalization performance on real datasets.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Liuyin Chen, Haoyang Qi, Di Lu, Jianxue Zhai, Kaican Cai, Long Wang, Guoyuan Liang, Zijun Zhang
Summary: In this study, a bilateral-branch network with a knowledge distillation procedure (KDBBN) was developed for the auxiliary diagnosis of lung adenocarcinoma. The method can automatically identify adenocarcinoma categories and detect the lesion area that most likely contributes to the identification of specific types of adenocarcinoma based on lung CT images. The results confirm that the proposed method provides a reliable basis for adenocarcinoma diagnosis and highly coincides with clinical diagnosis.
