Hao Liu

Nanjing University of Aeronautics and Astronautics

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Honghai Zhang

Magnetic Resonance Imaging Institute for Biomedical Research

Yongxin Yuan

Hubei Normal University

Honghai Zhang

Nanjing University of Aeronautics and Astronautics

Gang Zhong

Nanjing University of Aeronautics and Astronautics

Yi Jia

Nanjing University of Aeronautics and Astronautics

Tianzhi Fang

Nanjing University of Aeronautics and Astronautics

Shan Li

Zhejiang University of Technology

Bin-Xin He

Nanjing University of Aeronautics and Astronautics

Jinlun Zhou

Nanjing University of Aeronautics and Astronautics

Zhiheng Lin

University of Alberta

Cooperative Institutions

Nanjing University of Aeronautics and Astronautics

Chinese Academy of Sciences

Hohai University

Xi'an Jiaotong University

North China Electric Power University

Harbin Institute of Technology

Beijing Institute of Technology

Beihang University

China Electric Power Research Institute

Southeast University

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An Energy Entropy-Based Diagnostic Method for Voltage Abnormality in Real-World Battery Systems

Ping Yu Yong Li Xiaozhong Geng Shihao Li Baoshu Zong

Abnormality

10.2139/ssrn.4998085

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Optimization of Battery Energy Storage System Capacity for Wind Farm with Considering Auxiliary Services Compensation

Applied Sciences (2018)

Xin Jiang Nan Guo-liang Hao Liu Guo Zhi-min Qingshan Zeng

An optimal sizing model of the battery energy storage system (BESS) for large-scale wind farm adapting to the scheduling plan is proposed in this paper. Based on the analysis of the variability and uncertainty of wind output, the cost of auxiliary services of systems that are eased by BESS is quantized and the constraints of BESS accounting for the effect of wind power on system dispatching are proposed. Aiming to maximum the benefits of wind-storage union system, an optimal capacity model considering BESS investment costs, wind curtailment saving, and auxiliary services compensation is established. What’s more, the effect of irregular charge/discharge process on the life cycle of BESS is considered into the optimal model by introducing an equivalent loss of the cycle life. Finally, based on the typical data of a systems, results show that auxiliary services compensation can encourage wind farm configuration BESS effectively. Various sensitivity analyses are performed to assess the effect of the auxiliary services compensation, on-grid price of wind power, investment cost of BESS, cycle life of BESS, and wind uncertainty reserve level of BESS on this optimal capacity.

10.3390/app8101957

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Citations (26)

Method of Vertiport Capacity Assessment Based on Queuing Theory of Unmanned Aerial Vehicles

Sustainability (2022)

Honghai Zhang Yuhan Fei Jingyu Li Bowen Li Hao Liu

Urban air traffic has gradually attracted attention in recent years, which will bring endless vitality to future urban development. An objective and accurate method of assessing vertiport capacity is the basis for the air traffic flow management of UAVs, which plays an important role in improving the efficiency of urban airspace resources. First of all, this paper establishes a theoretical capacity calculation model for ground facilities such as takeoff and landing platforms, taxiways and aprons, respectively. Next, this paper analyzes the service characteristics of each ground facility and establishes different types of UAV queuing systems to obtain UAV delay curves based on a UAV Poisson flow arrival model. Subsequently, a suitable acceptable delay level is selected to obtain the corresponding UAV flow, which means the actual capacity of UAV operations. Eventually, the validity of the model is verified through actual drone data arithmetic examples. The calculation results show that the combination of “1 landing + 2 takeoffs” can achieve better capacity results and that the landing platform is more prone to congestion than the takeoff platform. Change in average service time has the greatest impact on the apron capacity, and the takeoff platform is the most sensitive to changes in the acceptable delay level.

Takeoff

Takeoff and landing

Service level

10.3390/su15010709

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Citations (5)

UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction

Sustainability (2021)

Honghai Zhang Yongjie Yan Shan Li Yuxin Hu Hao Liu

Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.

10.3390/su132212528

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Citations (11)

An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace

Sustainability (2021)

Shan Li Honghai Zhang Zhuolun Li Hao Liu

Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is expected to be carried out by drones. Therefore, it is of great significance to construct a reasonable air route network for logistics UAV to ensure the safety and efficiency of operations. In this paper, a single route planning model and an air route network planning model for UAV were constructed by fully considering the complex urban low altitude environment, the flight performance of UAV and the characteristics of logistics tasks to regulate the flights of drones. Then, taking Jiangjun Road Campus of Nanjing University of Aeronautics and Astronautics as an example, the improved cellular automata (CA) was adopted to search for the optimal route between different waypoints, and the optimal spanning tree algorithm was used to construct the route network. The experimental results demonstrated that the improved CA could greatly reduce search time and obtain the optimal route while enhancing safety. With the satisfaction of the voyage, the needs of logistics and distribution constraints, a network that had smaller intersection points and redundancy was generated. The models and core ideas proposed in this paper can not only regulate operation of drones but also provide a solid foundation for the distribution of logistics UAV in the future.

Drone

Route Planning

10.3390/su132313079

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Citations (20)

Real-time Trajectory Prediction of Unmanned Aircraft Vehicles Based on Gated Recurrent Unit

Lecture notes in electrical engineering (2021)

Rong Tang Zhao Yang Jiahuan Lu Hao Liu Honghai Zhang

Longitude

10.1007/978-981-16-5429-9_45

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Citations (4)

High-Bandwidth Grid-Connected Inverter to Enhance System Robustness Against Weak Grid

IEEE Transactions on Industrial Electronics (2022)

Tianzhi Fang Yifan Zhang Shuheng Shen Hao Wu Hao Liu

The multiple-input multiple-output (MIMO) matrix of the multi-inverter paralleled system based on different parameters is established, and three criteria to ensure the stability of the total grid current and interactive current are put forward. Furthermore, the robustness of the system with different switching frequencies for modules is analyzed, and the introduction of a high-bandwidth inverter into the system is found to be effective in suppressing low-frequency resonance. Then, the interaction stability between the high-frequency and low-frequency module is analyzed, and the switching frequency of the high-bandwidth inverter is selected. Finally, a prototype of the high-bandwidth inverter based on GaN is built in the lab. The effectiveness of the high-bandwidth grid-connected inverter to improve the robustness of the system in weak grid is verified by the experiment.

Robustness

Switching frequency

10.1109/tie.2022.3201296

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The Small Signal and Amplitude of Alternating Current Measuring System

DEStech Transactions on Social Science Education and Human Science (2017)

Dan Chen Hao Liu

This thesis is based on the examination item of 2016 Beijing College-student Electronic Design Contest, thus designing the small signal and amplitude of alternating current measuring system. MSP430G2553 microcontrollers are the main engine of this system. FPGA Cyclone 1C12 is slave engine, and external circuits OPA211 circuit board as an amplifier system. Main engine receive and calculate the frequency and voltage value of OPA211 direct or alternating current small signal. And the signal connects with the input of OPA211, as well as it magnifies the small signal received more than 20 times, and the output of OPA211 connects with the corresponding pins of microcontrollers. Slave engine receive signal sent by main engine and show the numerical value through digital transistor.

SIGNAL (programming language)

10.12783/dtssehs/icesd2017/11658

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Data-driven identification model for associated fault propagation path

Measurement (2021)

Hao Liu Dechang Pi Shuyuan Qiu Xixuan Wang Chang Guo

10.1016/j.measurement.2021.110628

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Citations (17)

Gas Path Fault Diagnosis of Turboshaft Engine Based on Novel Transfer Learning Methods

Journal of Dynamic Systems Measurement and Control (2024)

Yong-Ping Zhao Hui-Jie Jin Hao Liu

Abstract Data-driven fault diagnosis method is widely used in the field of engine health management, which uses engine sensor data as input and engine faulty components as output for component-level fault diagnosis of the engine. The application premise of the general data-driven fault diagnosis method is that all data come from the same working conditions, that is, they belong to the same distribution. However, this assumption is not valid in the actual engine fault diagnosis, because the engine state will change with the increase of running time. In the meantime, collecting engine data is usually expensive, time-consuming, and laborious. To solve these problems, extreme learning machine (ELM)-based two transfer learning methods for fault diagnosis of turboshaft engines are proposed in this paper. One is joint solving ELM (JSELM), which regards the information of the target domain and source domain as similar and different parts, respectively, and knowledge is extracted from them at the same time. The other is model transfer-based ELM (MTELM), which uses the idea of pretraining. First, a general ELM classifier is trained with the source domain data and then fine-tuned with the target domain data. Both methods have a good real-time performance as the traditional ELM. When there are a few data in the target domain, they achieve much better classification accuracy than traditional ELM. Finally, experiments are carried out with turboshaft engine simulation data. The results show that both methods are effective, especially MTELM, which has better classification accuracy.

Aero engine

10.1115/1.4064846

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