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Predictions of renewable energy (RE) generation and electricity load are critical to smart grid operation. However, the prediction task remains challenging due to the intermittent and chaotic character of RE sources, and the diverse user behavior and power consumers. This paper presents a novel method for the prediction of RE generation and electricity load using improved stacked gated recurrent unit-recurrent neural network (GRU-RNN) for both uni-variate and multi-variate scenarios. First, multiple sensitive monitoring parameters or historical electricity consumption data are selected according to the correlation analysis to form the input data. Second, a stacked GRU-RNN using a simplified GRU is constructed with improved training algorithm based on AdaGrad and adjustable momentum. The modified GRU-RNN structure and improved training method enhance training efficiency and robustness. Third, the stacked GRU-RNN is used to establish an accurate mapping between the selected variables and RE generation or electricity load due to its self-feedback connections and improved training mechanism. The proposed method is verified by using two experiments: prediction of wind power generation using multiple weather parameters and prediction of electricity load with historical energy consumption data. The experimental results demonstrate that the proposed method outperforms state-of-the-art methods of machine learning or deep learning in achieving an accurate energy prediction for effective smart grid operation.
Min Xia; Haidong Shao; Xiandong Ma; Clarence W. de Silva. A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation. IEEE Transactions on Industrial Informatics 2021, 17, 7050 -7059.
AMA StyleMin Xia, Haidong Shao, Xiandong Ma, Clarence W. de Silva. A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation. IEEE Transactions on Industrial Informatics. 2021; 17 (10):7050-7059.
Chicago/Turabian StyleMin Xia; Haidong Shao; Xiandong Ma; Clarence W. de Silva. 2021. "A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation." IEEE Transactions on Industrial Informatics 17, no. 10: 7050-7059.
This paper proposes a flux-weakening (FW) control for dual three-phase permanent magnet synchronous machine (DT-PMSM) based on vector space decomposition (VSD) control, where the output voltage in $\alpha\beta$ sub-plane is employed for voltage feedback in the flux-weakening control loop. As the fundamental components are mapped to $\alpha\beta$ sub-plane while the 5th and 7th harmonics are projected to harmonic z1z2 sub-plane, the flux-weakening current from this new control in $\alpha\beta$ sub-plane is sixth harmonic-free regardless of the 5th and 7th harmonics being resulted from the non-sinusoidal back EMF or inverter non-linearity. The proposed control is compared with the conventional FW feedback control extended for DT-PMSM, where the FW control is applied to the two sets of three-phase windings separately. The experimental results show that the proposed FW control based on VSD is superior to the conventional FW control in terms of reduction in current unbalance and harmonic currents.
Yashan Hu; Yonggang Li; Xiandong Ma; Xuefei Li; Shoudao Huang. Flux-Weakening Control of Dual Three-Phase PMSM Based on Vector Space Decomposition Control. IEEE Transactions on Power Electronics 2020, 36, 8428 -8438.
AMA StyleYashan Hu, Yonggang Li, Xiandong Ma, Xuefei Li, Shoudao Huang. Flux-Weakening Control of Dual Three-Phase PMSM Based on Vector Space Decomposition Control. IEEE Transactions on Power Electronics. 2020; 36 (7):8428-8438.
Chicago/Turabian StyleYashan Hu; Yonggang Li; Xiandong Ma; Xuefei Li; Shoudao Huang. 2020. "Flux-Weakening Control of Dual Three-Phase PMSM Based on Vector Space Decomposition Control." IEEE Transactions on Power Electronics 36, no. 7: 8428-8438.
The improved knowledge of wave height and period conditions has considerably influenced on ocean navigation, marine fishery and engineering, especially in the polar regions. The methods of predicting ocean wave height which involve field measurements, numerical simulation, physical models and analytical solutions have been gradually developed with intelligent functions. Despite numerical wave models being dominant for recent decades, wave forecasting is still facing many challenges such as small region forecasting and large amounts of data needed. This paper presents a novel deep learning algorithm, namely Long Short Term Memory (LSTM), incorporating with Principal Component Analysis (PCA) to predict the wave height by using data from four wave buoys as deployed in the polar westerlies for two and half months. The PCA method is used to extract principal components from a set of input signals while LSTM is adopted to avoid long term independences during the forecasting. The novelty of this paper is to investigate an artificial intelligence (AI) based model in the field of time sequence forecasting in order to determine the performance of wave conditions by using AI technology. The result from this integrated method demonstrates that the LSTM model has the potential to better predict wave height in the polar condition based on time-space domain information. The PCA is proved essential for selection of input signals and for correlation analysis. For comparison, different data-driven models are applied and the results also show the purposed model achieves the highest scores in terms of R-squared value. Besides, the paper also discusses the challenges for long term and high-value prediction which needs to be optimized in the future work.
Chenhua Ni; Xiandong Ma. An integrated long-short term memory algorithm for predicting polar westerlies wave height. Ocean Engineering 2020, 215, 107715 .
AMA StyleChenhua Ni, Xiandong Ma. An integrated long-short term memory algorithm for predicting polar westerlies wave height. Ocean Engineering. 2020; 215 ():107715.
Chicago/Turabian StyleChenhua Ni; Xiandong Ma. 2020. "An integrated long-short term memory algorithm for predicting polar westerlies wave height." Ocean Engineering 215, no. : 107715.
Today’s electricity grid is rapidly evolving, with increased penetration of renewable energy sources (RES). Conventional Optimal Power Flow (OPF) has non-linear constraints that make it a highly non-linear, non-convex optimisation problem. This complex problem escalates further with the integration of RES, which are generally intermittent in nature. In this article, an optimal power flow model combines three types of energy resources, including conventional thermal power generators, solar photovoltaic generators (SPGs) and wind power generators (WPGs). Uncertain power outputs from SPGs and WPGs are forecasted with the help of lognormal and Weibull probability distribution functions, respectively. The over and underestimation output power of RES are considered in the objective function i.e. as a reserve and penalty cost, respectively. Furthermore, to reduce carbon emissions, a carbon tax is imposed while formulating the objective function. A grey wolf optimisation technique (GWO) is employed to achieve optimisation in modified IEEE-30 and IEEE-57 bus test systems to demonstrate its feasibility. Hence, novel contributions of this work include the new objective functions and associated framework for optimising generation cost while considering RES; and, secondly, computational efficiency is improved by the use of GWO to address the non-convex OPF problem. To investigate the effectiveness of the proposed GWObased approach, it is compared in simulation to five other nature-inspired global optimisation algorithms and two well-established hybrid algorithms. For the simulation scenarios considered in this article, the GWO outperforms the other algorithms in terms of total cost minimisation and convergence time reduction.
Inam Ullah Khan; Nadeem Javaid; Kelum A. A. Gamage; C. James Taylor; Sobia Baig; Xiandong Ma. Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources. IEEE Access 2020, 8, 148622 -148643.
AMA StyleInam Ullah Khan, Nadeem Javaid, Kelum A. A. Gamage, C. James Taylor, Sobia Baig, Xiandong Ma. Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources. IEEE Access. 2020; 8 (99):148622-148643.
Chicago/Turabian StyleInam Ullah Khan; Nadeem Javaid; Kelum A. A. Gamage; C. James Taylor; Sobia Baig; Xiandong Ma. 2020. "Heuristic Algorithm Based Optimal Power Flow Model Incorporating Stochastic Renewable Energy Sources." IEEE Access 8, no. 99: 148622-148643.
The torque enhancement of the dual three-phase permanent magnet synchronous machine (DT-PMSM) drive system by full exploitation of flux-linkage and current harmonics is comparatively studied in this study. The torque capability of DT-PMSM is previously evaluated with strategies of harmonics utilisation, i.e. strategy 1 of third harmonic utilisation and strategy 2 of fifth and seventh harmonic utilisation, which can extend the torque capability by 18.2 and 9.0%, respectively. However, the full exploitation of harmonics including third, fifth, and seventh harmonics in the dual three-phase system is not addressed. In this study, the strategy 3 of third, fifth, and seventh harmonic utilisation is also included. Its corresponding harmonic current control is proposed and the average torque and harmonic torque are analysed in detail. Based on a test rig with existing prototype DT-PMSM, the torque with strategy 3 is increased up to 26.5%, which is superior to the previous strategies.
Yashan Hu; Keyuan Huang; Xuefei Li; Derong Luo; Shoudao Huang; Xiandong Ma. Torque enhancement of dual three‐phase PMSM by harmonic injection. IET Electric Power Applications 2020, 14, 1735 -1744.
AMA StyleYashan Hu, Keyuan Huang, Xuefei Li, Derong Luo, Shoudao Huang, Xiandong Ma. Torque enhancement of dual three‐phase PMSM by harmonic injection. IET Electric Power Applications. 2020; 14 (9):1735-1744.
Chicago/Turabian StyleYashan Hu; Keyuan Huang; Xuefei Li; Derong Luo; Shoudao Huang; Xiandong Ma. 2020. "Torque enhancement of dual three‐phase PMSM by harmonic injection." IET Electric Power Applications 14, no. 9: 1735-1744.
The high penetration of renewable power generators and various loads have brought a great challenge for dispatching energy in a microgrid system. Heating ventilation air conditioning (HVAC) system, as a household appliance with high popularity, can be considered as an effective technology to alleviate energy dispatch issues. This paper presents novel distributed algorithms based on HVAC to solve the demand side management problem, where the microgrid system with HVAC units is considered as a multi-agent system (MAS). The approach provides a desirable operating frequency signal for each HVAC based on the power mismatch value occurring on each local bus. It utilizes demand response of the HVAC units to minimize the supply-demand mismatch, thus reducing the quantity and capacity of energy storage devices potentially to be required. Compared with existing approaches focusing on the distributed algorithms under a fixed communication network, this paper addresses a consensus problem under a switching topology by using the Lyapunov argument. It is verified that a jointly strong and connected topology is a sufficient condition in order to achieve an average consensus for a time-varying topology. A number of cases are studied to evaluate the effectiveness of the algorithms by taking into account its power constraints, dynamic behaviors, anti-damage characteristics and time-varying communication topology. Modelling these system interactions has demonstrated the feasibility of the proposed microgrid system.
Jie Ma; Xiandong Ma; Suzana Ilic; Ma; Ilic. HVAC-Based Cooperative Algorithms for Demand Side Management in a Microgrid. Energies 2019, 12, 4276 .
AMA StyleJie Ma, Xiandong Ma, Suzana Ilic, Ma, Ilic. HVAC-Based Cooperative Algorithms for Demand Side Management in a Microgrid. Energies. 2019; 12 (22):4276.
Chicago/Turabian StyleJie Ma; Xiandong Ma; Suzana Ilic; Ma; Ilic. 2019. "HVAC-Based Cooperative Algorithms for Demand Side Management in a Microgrid." Energies 12, no. 22: 4276.
Due to the high penetration of renewable power system with variable generation profiles, the need for flexible demand and flexible energy storage increases. In this paper, a hierarchical energy dispatch scheme incorporating energy storage system is presented to address the uncontrollability of renewable power generation. Statistical-based forecasting techniques are preformed and compared in order to accurately predict solar radiance and estimate solar power generation. Battery energy storage system (BESS) is often deployed as a flexible power supplier to reduce the peak power, emissions and cost. This paper elaborates a multiagent system (MAS) based distributed algorithm to investigate an energy dispatch scheme for BESS, based on the renewable energy forecasting results. A 24-hour prescheduled energy dispatch scheme is assigned to individual BESSs based on IEEE 5-bus system and IEEE 14-bus system. Simulation results are shown to demonstrate the feasibility and scalability of the algorithm.
Jie Ma; Xiandong Ma. Distributed Control of Battery Energy Storage System in a Microgrid. 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) 2019, 320 -325.
AMA StyleJie Ma, Xiandong Ma. Distributed Control of Battery Energy Storage System in a Microgrid. 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA). 2019; ():320-325.
Chicago/Turabian StyleJie Ma; Xiandong Ma. 2019. "Distributed Control of Battery Energy Storage System in a Microgrid." 2019 8th International Conference on Renewable Energy Research and Applications (ICRERA) , no. : 320-325.
Chenhua Ni; Xiandong Ma; Ji Wang. Integrated deep learning model for predicting electrical power generation from wave energy converter. 2019 25th International Conference on Automation and Computing (ICAC) 2019, 1 .
AMA StyleChenhua Ni, Xiandong Ma, Ji Wang. Integrated deep learning model for predicting electrical power generation from wave energy converter. 2019 25th International Conference on Automation and Computing (ICAC). 2019; ():1.
Chicago/Turabian StyleChenhua Ni; Xiandong Ma; Ji Wang. 2019. "Integrated deep learning model for predicting electrical power generation from wave energy converter." 2019 25th International Conference on Automation and Computing (ICAC) , no. : 1.
Jie Ma; Xiandong Ma. Consensus-based Hierachical Demand Side Management in Microgrid. 2019 25th International Conference on Automation and Computing (ICAC) 2019, 1 .
AMA StyleJie Ma, Xiandong Ma. Consensus-based Hierachical Demand Side Management in Microgrid. 2019 25th International Conference on Automation and Computing (ICAC). 2019; ():1.
Chicago/Turabian StyleJie Ma; Xiandong Ma. 2019. "Consensus-based Hierachical Demand Side Management in Microgrid." 2019 25th International Conference on Automation and Computing (ICAC) , no. : 1.
Wind power is playing an increasingly significant role in daily life. However, wind farms are usually far away from cities especially for offshore wind farms, which brought inconvenience for maintenance. Two conventional maintenance strategies, namely corrective maintenance and preventive maintenance, cannot provide condition-based maintenance to identify potential anomalies and predicts turbines' future operation trend. In this study, a model based data-driven condition monitoring method is proposed for fault detection of the wind turbines (WTs) with SCADA data acquired from an operational wind farm. Due to the nature of the alarm signals, the alarm data can be used as an intermedium to link the normal data and fault data. First, KPCA is employed to select principal components (PCs) to retain the dominant information from the original dataset to reduce the computation load for further modelling. Then the selected PCs are processed for normal-abnormal condition classification to extract those abnormal condition data that are classified further into false alarms and true alarms related to the faults. This two-stage classification approach is implemented based on the KSVM algorithm. The results demonstrate that the two-stage fault detection method can identify the normal, alarm and fault conditions of the WTs accurately and effectively.
Yueqi Wu; Xiandong Ma. Alarms‐related wind turbine fault detection based on kernel support vector machines. The Journal of Engineering 2019, 2019, 4980 -4985.
AMA StyleYueqi Wu, Xiandong Ma. Alarms‐related wind turbine fault detection based on kernel support vector machines. The Journal of Engineering. 2019; 2019 (18):4980-4985.
Chicago/Turabian StyleYueqi Wu; Xiandong Ma. 2019. "Alarms‐related wind turbine fault detection based on kernel support vector machines." The Journal of Engineering 2019, no. 18: 4980-4985.
This paper presents data-driven approaches to improving active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust active power output of individual turbines according to their health condition and can thus optimize the total energy output of wind farm. In the paper, extreme learning machine (ELM) algorithm and bonferroni interval are applied to estimate fault degree while analytic hierarchy process (AHP) is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reduce the fatigue loading on the faulty wind turbines.
Peng Qian; Xiandong Ma; Dahai Zhang; Junheng Wang. Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines. IEEE Transactions on Industrial Electronics 2018, 66, 6012 -6020.
AMA StylePeng Qian, Xiandong Ma, Dahai Zhang, Junheng Wang. Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines. IEEE Transactions on Industrial Electronics. 2018; 66 (8):6012-6020.
Chicago/Turabian StylePeng Qian; Xiandong Ma; Dahai Zhang; Junheng Wang. 2018. "Data-Driven Condition Monitoring Approaches to Improving Power Output of Wind Turbines." IEEE Transactions on Industrial Electronics 66, no. 8: 6012-6020.
Inam Ullah Khan; Xiandong Ma; C. James Taylor; Nadeem Javaid; Kelum A.A. Gamage. Heuristic Algorithm Based Dynamic Scheduling Model of Home Appliances in Smart Grid. 2018 24th International Conference on Automation and Computing (ICAC) 2018, 1 .
AMA StyleInam Ullah Khan, Xiandong Ma, C. James Taylor, Nadeem Javaid, Kelum A.A. Gamage. Heuristic Algorithm Based Dynamic Scheduling Model of Home Appliances in Smart Grid. 2018 24th International Conference on Automation and Computing (ICAC). 2018; ():1.
Chicago/Turabian StyleInam Ullah Khan; Xiandong Ma; C. James Taylor; Nadeem Javaid; Kelum A.A. Gamage. 2018. "Heuristic Algorithm Based Dynamic Scheduling Model of Home Appliances in Smart Grid." 2018 24th International Conference on Automation and Computing (ICAC) , no. : 1.
Chenhua Ni; Xiandong Ma; Yang Bai. Convolutional Neural Network based power generation prediction of wave energy converter. 2018 24th International Conference on Automation and Computing (ICAC) 2018, 1 .
AMA StyleChenhua Ni, Xiandong Ma, Yang Bai. Convolutional Neural Network based power generation prediction of wave energy converter. 2018 24th International Conference on Automation and Computing (ICAC). 2018; ():1.
Chicago/Turabian StyleChenhua Ni; Xiandong Ma; Yang Bai. 2018. "Convolutional Neural Network based power generation prediction of wave energy converter." 2018 24th International Conference on Automation and Computing (ICAC) , no. : 1.
Yueqi Wu; Xiandong Ma. Kullback-Leibler divergence based wind turbine fault feature extraction. 2018 24th International Conference on Automation and Computing (ICAC) 2018, 1 .
AMA StyleYueqi Wu, Xiandong Ma. Kullback-Leibler divergence based wind turbine fault feature extraction. 2018 24th International Conference on Automation and Computing (ICAC). 2018; ():1.
Chicago/Turabian StyleYueqi Wu; Xiandong Ma. 2018. "Kullback-Leibler divergence based wind turbine fault feature extraction." 2018 24th International Conference on Automation and Computing (ICAC) , no. : 1.
Successful development of a marine wave energy converter (WEC) relies strongly on the development of the power generation device, which needs to be efficient and cost-effective. An innovative multi-input approach based on the Convolutional Neural Network (CNN) is investigated to predict the power generation of a WEC system using a double-buoy oscillating body device (OBD). The results from the experimental data show that the proposed multi-input CNN performs much better at predicting results compared with the conventional artificial network and regression models. Through the power generation analysis of this double-buoy OBD, it shows that the power output has a positive correlation with the wave height when it is higher than 0.2 m, which becomes even stronger if the wave height is higher than 0.6 m. Furthermore, the proposed approach associated with the CNN algorithm in this study can potentially detect the changes that could be due to presence of anomalies and therefore be used for condition monitoring and fault diagnosis of marine energy converters. The results are also able to facilitate controlling of the electricity balance among energy conversion, wave power produced and storage.
Chenhua Ni; Xiandong Ma. Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. Energies 2018, 11, 2097 .
AMA StyleChenhua Ni, Xiandong Ma. Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs. Energies. 2018; 11 (8):2097.
Chicago/Turabian StyleChenhua Ni; Xiandong Ma. 2018. "Prediction of Wave Power Generation Using a Convolutional Neural Network with Multiple Inputs." Energies 11, no. 8: 2097.
An effective condition monitoring system of wind turbines generally requires installation of a high number of sensors and use of a high sampling frequency in particular for monitoring of the electrical components within a turbine, resulting in a large amount of data. This can become a burden for condition monitoring and fault detection systems. This paper aims to develop algorithms that will allow a reduced dataset to be used in wind turbine fault detection. The paper firstly proposes a variable selection algorithm based on principal component analysis (PCA) with multiple selection criteria in order to select a set of variables to target fault signals while still preserving the variation of data in the original dataset. With the selected variables, the paper then describes fault detection and identification algorithms, which can identify faults, determine the corresponding time and location where the fault occurs, and estimate its severity. The proposed algorithms are evaluated with simulation data from PSCAD/EMTDC, SCADA (Supervisory control and data acquisition) data from an operational wind farm, and experimental data from a wind turbine test rig. Results show that the proposed methods can select a reduced set of variables with minimal information lost whilst detecting faults efficiently and effectively.
Yifei Wang; Xiandong Ma; Peng Qian. Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection. IEEE Transactions on Sustainable Energy 2018, 9, 1627 -1635.
AMA StyleYifei Wang, Xiandong Ma, Peng Qian. Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection. IEEE Transactions on Sustainable Energy. 2018; 9 (4):1627-1635.
Chicago/Turabian StyleYifei Wang; Xiandong Ma; Peng Qian. 2018. "Wind Turbine Fault Detection and Identification Through PCA-Based Optimal Variable Selection." IEEE Transactions on Sustainable Energy 9, no. 4: 1627-1635.
Jie Ma; Xiandong Ma. A review of forecasting algorithms and energy management strategies for microgrids. Systems Science & Control Engineering 2018, 6, 237 -248.
AMA StyleJie Ma, Xiandong Ma. A review of forecasting algorithms and energy management strategies for microgrids. Systems Science & Control Engineering. 2018; 6 (1):237-248.
Chicago/Turabian StyleJie Ma; Xiandong Ma. 2018. "A review of forecasting algorithms and energy management strategies for microgrids." Systems Science & Control Engineering 6, no. 1: 237-248.
A novel floating pendulum wave energy converter (WEC) with the ability of tide adaptation is designed and presented in this paper. Aiming to a high efficiency, the buoy’s hydrodynamic shape is optimized by enumeration and comparison. Furthermore, in order to keep the buoy’s well-designed leading edge always facing the incoming wave straightly, a novel transmission mechanism is then adopted, which is called the tidal adaptation mechanism in this paper. Time domain numerical models of a floating pendulum WEC with or without tide adaptation mechanism are built to compare their performance on various water levels. When comparing these two WECs in terms of their average output based on the linear passive control strategy, the output power of WEC with the tide adaptation mechanism is much steadier with the change of the water level and always larger than that without the tide adaptation mechanism.
Jing Yang; Da-Hai Zhang; Ying Chen; Hui Liang; Ming Tan; Wei Li; Xiandong Ma. Design, optimization and numerical modelling of a novel floating pendulum wave energy converter with tide adaptation. China Ocean Engineering 2017, 31, 578 -588.
AMA StyleJing Yang, Da-Hai Zhang, Ying Chen, Hui Liang, Ming Tan, Wei Li, Xiandong Ma. Design, optimization and numerical modelling of a novel floating pendulum wave energy converter with tide adaptation. China Ocean Engineering. 2017; 31 (5):578-588.
Chicago/Turabian StyleJing Yang; Da-Hai Zhang; Ying Chen; Hui Liang; Ming Tan; Wei Li; Xiandong Ma. 2017. "Design, optimization and numerical modelling of a novel floating pendulum wave energy converter with tide adaptation." China Ocean Engineering 31, no. 5: 578-588.
Condition Monitoring (CM) has been considered as an effective method to enhance the reliability of wind turbines and implement cost-effective maintenance. Thus, adopting an efficient approach for condition monitoring of wind turbines is desirable. This paper presents a data-driven model-based CM approach for wind turbines based on the online sequential extreme learning machine (OS-ELM) algorithm. A physical kinetic energy correction model is employed to normalize the temperature change to the value at the rated power output to eliminate the effect of variable speed operation of the turbines. The residual signal, obtained by comparing the predicted values and practical measurements, is processed by the physical correction model and then assessed with a Bonferroni interval method for fault diagnosis. Models have been validated using supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains various types of temperature data of the gearbox. The results show that the proposed method can detect more efficiently both the long-term aging characteristics and the short-term faults of the gearbox.
Peng Qian; Xiandong Ma; Dahai Zhang. Estimating Health Condition of the Wind Turbine Drivetrain System. Energies 2017, 10, 1583 .
AMA StylePeng Qian, Xiandong Ma, Dahai Zhang. Estimating Health Condition of the Wind Turbine Drivetrain System. Energies. 2017; 10 (10):1583.
Chicago/Turabian StylePeng Qian; Xiandong Ma; Dahai Zhang. 2017. "Estimating Health Condition of the Wind Turbine Drivetrain System." Energies 10, no. 10: 1583.
Jie Ma; Xiandong Ma. State-of-the-art forecasting algorithms for microgrids. 2017 23rd International Conference on Automation and Computing (ICAC) 2017, 1 .
AMA StyleJie Ma, Xiandong Ma. State-of-the-art forecasting algorithms for microgrids. 2017 23rd International Conference on Automation and Computing (ICAC). 2017; ():1.
Chicago/Turabian StyleJie Ma; Xiandong Ma. 2017. "State-of-the-art forecasting algorithms for microgrids." 2017 23rd International Conference on Automation and Computing (ICAC) , no. : 1.