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This paper focuses on the asymptotic stability problems of the generalized neural networks (GNNs) with two additive time-varying delays (ATDs) and general activation function. First of all, a simple Lyapunov-Krasovskii functional (LKF) is established. Then not only the generalized free-weighting-matrix-based (GFWM-based) inequality together with its optimization strategy on choosing an arbitrary vector is utilized to estimate the single integral terms in derivative of the constructed LKF, but also the general activation function method is applied to introduce more information on cross terms of neuron activation function. Finally, a less conservative stability criterion together with its corollary is derived with convex combination technique, whose feasibility and superiority can be demonstrated with two numerical examples.
Fang Liu; Haitao Liu; Kangzhi Liu. New asymptotic stability analysis for generalized neural networks with additive time-varying delays and general activation function. Neurocomputing 2021, 463, 437 -443.
AMA StyleFang Liu, Haitao Liu, Kangzhi Liu. New asymptotic stability analysis for generalized neural networks with additive time-varying delays and general activation function. Neurocomputing. 2021; 463 ():437-443.
Chicago/Turabian StyleFang Liu; Haitao Liu; Kangzhi Liu. 2021. "New asymptotic stability analysis for generalized neural networks with additive time-varying delays and general activation function." Neurocomputing 463, no. : 437-443.
Harmonic amplification and interaction of the PV plant which contains multiple parallel inverters influence the renewable power generation and operation seriously. In this article, the resonance mechanism and characteristics of a real large-scale PV plant are explored based on its plant-level circuit model. The component and system models are established first. The distribution rules of wideband resonance frequency from the perspective of the point of common coupling (PCC) and the inverter are respectively determined. Two different resonance bands excited by LCL filter and underground cable are further identified. At last, the practical case of harmonic resonance in the studied PV plant is investigated. Both the measured data and the simulation results support the analytical conclusions. This article combines the theoretical study with the engineering practice, which provides beneficial guidance on the resonance prevention.
QianYi Liu; Fang Liu; Runmin Zou; Yong Li. Harmonic Resonance Characteristic of Large-scale PV Plant: Modelling, Analysis and Engineering Case. IEEE Transactions on Power Delivery 2021, PP, 1 -1.
AMA StyleQianYi Liu, Fang Liu, Runmin Zou, Yong Li. Harmonic Resonance Characteristic of Large-scale PV Plant: Modelling, Analysis and Engineering Case. IEEE Transactions on Power Delivery. 2021; PP (99):1-1.
Chicago/Turabian StyleQianYi Liu; Fang Liu; Runmin Zou; Yong Li. 2021. "Harmonic Resonance Characteristic of Large-scale PV Plant: Modelling, Analysis and Engineering Case." IEEE Transactions on Power Delivery PP, no. 99: 1-1.
Automatic battery equalizers require no sensing circuits, which reduces their cost, size, and complexity. However, among existing methods, additional resistors or diodes are used in the balancing paths to ensure safe operation, which degrades the conversion efficiency and balancing speed. To overcome this problem, this paper proposes an equalizer architecture based on the half-bridge LLC converter. The inherent current limitation characteristic of the proposed equalizer improves balancing speed. High conversion efficiency is achieved due to the soft-switching operations of the switches. The circuit topology and working modes are presented first, then the features of voltage equalization, inherent current limitation, and soft-switching conditions are analyzed in detail. Finally, the proposed equalizer is validated by simulation and experiment.
Runmin Zou; Fulin Liu; Yonglu Liu; Guo Xu; Fang Liu. An LLC-Based Battery Equalizer with Inherent Current Limitation. IEEE Transactions on Power Electronics 2021, PP, 1 -1.
AMA StyleRunmin Zou, Fulin Liu, Yonglu Liu, Guo Xu, Fang Liu. An LLC-Based Battery Equalizer with Inherent Current Limitation. IEEE Transactions on Power Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleRunmin Zou; Fulin Liu; Yonglu Liu; Guo Xu; Fang Liu. 2021. "An LLC-Based Battery Equalizer with Inherent Current Limitation." IEEE Transactions on Power Electronics PP, no. 99: 1-1.
The rapid development of modern vessel leads to higher demands on the optimization and integration of shipboard power supply system (SPSS). In this article, a compact-design oriented 12-pulse parallel operating transformer employing shared integrated filter is presented for power quality improvement of SPSS with reduced installation space. The proposed SPSS is featured with harmonic-free power supply and high integration of power equipment. The system topology is introduced first, and the distinctive compensation principle is then analyzed. Taking into account both of the winding impedance matching and transformer size optimization, minimum radial dimension is searched and determined in the interval of approximative zero-impedance. Moreover, the inductance calculation method of integrated reactor together with its design domain is further given out. At last, the prototype of the proposed SPSS is tested in laboratory. The experimental results verify the feasibility and effectiveness of the proposal.
QianYi Liu; Fang Liu; Runmin Zou; Shaoyang Wang; Ye Tian; Yun Wang; Liang Yuan; Yong Li. A Compact-design Oriented Shipboard Power Supply System with Transformer Integrated Filtering Method. IEEE Transactions on Power Electronics 2021, PP, 1 -1.
AMA StyleQianYi Liu, Fang Liu, Runmin Zou, Shaoyang Wang, Ye Tian, Yun Wang, Liang Yuan, Yong Li. A Compact-design Oriented Shipboard Power Supply System with Transformer Integrated Filtering Method. IEEE Transactions on Power Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleQianYi Liu; Fang Liu; Runmin Zou; Shaoyang Wang; Ye Tian; Yun Wang; Liang Yuan; Yong Li. 2021. "A Compact-design Oriented Shipboard Power Supply System with Transformer Integrated Filtering Method." IEEE Transactions on Power Electronics PP, no. 99: 1-1.
This letter proposes a new push-pull DC/DC converter topology with complementary active clamped. Firstly, a clamping capacitor is added between the active switches to maintain a clamping voltage and recover the energy stored in the leakage inductors. Secondly, the leakage inductors of transformer are effectively utilized to buffer the energy transfer, and greater leakage inductors are expected. Thirdly, the active switches are mutually clamped via the added capacitor, and the natural soft-switching characteristics are given. Fourthly, the rectifier diodes commutate naturally without reverse recovery. Finally, an experimental prototype is designed, and the correctness of the theoretical analysis is verified.
Li Jiang; Jianghu Wan; Yong Li; Chun Huang; Fang Liu; Hui Wang; Yao Sun; Yijia Cao. A New Push-Pull DC/DC Converter Topology with Complementary Active Clamped. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.
AMA StyleLi Jiang, Jianghu Wan, Yong Li, Chun Huang, Fang Liu, Hui Wang, Yao Sun, Yijia Cao. A New Push-Pull DC/DC Converter Topology with Complementary Active Clamped. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.
Chicago/Turabian StyleLi Jiang; Jianghu Wan; Yong Li; Chun Huang; Fang Liu; Hui Wang; Yao Sun; Yijia Cao. 2021. "A New Push-Pull DC/DC Converter Topology with Complementary Active Clamped." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.
In this paper, the speed tracking problem of the interior permanent magnet synchronous motor (IPMSM) of an electric vehicle is studied. A cascade speed control strategy based on active disturbance rejection control (ADRC) and a current control strategy based on improved duty cycle finite control set model predictive control (FCSMPC) are proposed, both of which can reduce torque ripple and current ripple as well as the computational burden. First of all, in the linearization process, some nonlinear terms are added into the control signal for voltage compensation, which can reduce the order of the prediction model. Then, the dq-axis currents are selected by maximum torque per ampere (MTPA). Six virtual vectors are employed to FCSMPC, and a novel way to calculate the duty cycle is adopted. Finally, the simulation results show the validity and superiority of the proposed method.
Fang Liu; Haotian Li; Ling Liu; Runmin Zou; Kangzhi Liu. A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control. Mathematics 2021, 9, 760 .
AMA StyleFang Liu, Haotian Li, Ling Liu, Runmin Zou, Kangzhi Liu. A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control. Mathematics. 2021; 9 (7):760.
Chicago/Turabian StyleFang Liu; Haotian Li; Ling Liu; Runmin Zou; Kangzhi Liu. 2021. "A Control Method for IPMSM Based on Active Disturbance Rejection Control and Model Predictive Control." Mathematics 9, no. 7: 760.
The paper presents a multiaspect analysis of multivalues and the broadband nature of system oscillation. By analyzing the ambient signal caused by random small disturbances during the normal operation of interconnected power grids, many system operation characteristics can be obtained. The traditional signal processing method cannot extract the information from ambient signals effectively. Aiming at the problem of broadband oscillation mode superposition and the difficulty of extracting information from ambient signals, an iterative adaptive variational mode decomposition (IA-VMD) method is proposed based on frequency domain analysis and signal energy. Additionally, the IA-VMD method, combined with a bandpass filter and the Prony algorithm, is used to realize the modal identification of broadband oscillation and ambient signals. Simulation experiments show that the IA-VMD method has good adaptability, antinoise characteristics, and a certain significant engineering application value as well.
Fang Liu; Sisi Lin; Chonggang Chen; Kangzhi Liu; Runmin Zou; Denis Sidorov. Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method. Applied Sciences 2021, 11, 530 .
AMA StyleFang Liu, Sisi Lin, Chonggang Chen, Kangzhi Liu, Runmin Zou, Denis Sidorov. Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method. Applied Sciences. 2021; 11 (2):530.
Chicago/Turabian StyleFang Liu; Sisi Lin; Chonggang Chen; Kangzhi Liu; Runmin Zou; Denis Sidorov. 2021. "Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method." Applied Sciences 11, no. 2: 530.
The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.
Denis Sidorov; Fang Liu; Yonghui Sun. Machine Learning for Energy Systems. Energies 2020, 13, 4708 .
AMA StyleDenis Sidorov, Fang Liu, Yonghui Sun. Machine Learning for Energy Systems. Energies. 2020; 13 (18):4708.
Chicago/Turabian StyleDenis Sidorov; Fang Liu; Yonghui Sun. 2020. "Machine Learning for Energy Systems." Energies 13, no. 18: 4708.
The evolutionary integral dynamical models of storage systems are addressed. Such models are based on systems of weakly regular nonlinear Volterra integral equations with piecewise smooth kernels. These equations can have non-unique solutions that depend on free parameters. The objective of this paper was two-fold. First, the iterative numerical method based on the modified Newton–Kantorovich iterative process is proposed for a solution of the nonlinear systems of such weakly regular Volterra equations. Second, the proposed numerical method was tested both on synthetic examples and real world problems related to the dynamic analysis of microgrids with energy storage systems.
Denis Sidorov; Aleksandr Tynda; Ildar Muftahov; Aliona Dreglea; Fang Liu. Nonlinear Systems of Volterra Equations with Piecewise Smooth Kernels: Numerical Solution and Application for Power Systems Operation. Mathematics 2020, 8, 1257 .
AMA StyleDenis Sidorov, Aleksandr Tynda, Ildar Muftahov, Aliona Dreglea, Fang Liu. Nonlinear Systems of Volterra Equations with Piecewise Smooth Kernels: Numerical Solution and Application for Power Systems Operation. Mathematics. 2020; 8 (8):1257.
Chicago/Turabian StyleDenis Sidorov; Aleksandr Tynda; Ildar Muftahov; Aliona Dreglea; Fang Liu. 2020. "Nonlinear Systems of Volterra Equations with Piecewise Smooth Kernels: Numerical Solution and Application for Power Systems Operation." Mathematics 8, no. 8: 1257.
This paper focuses on the stability problems of continuous linear systems with two additive time-varying delay components. Firstly, an effective and simple Lyapunov–Krasovskii functional (LKF) is established, which not only takes adequate information of delay components and their upper bounds into consideration but also establishes a simpler form to decrease the computational complexity. Secondly, an improved delay-dependent stability criterion together with its corollary is obtained by employing the generalized free-weighting-matrix-based (GFWM-based) inequality and some other techniques to calculate the derivative of the constructed LKF, which will further reduce the introduced estimation error and make the criteria less conservative. Lastly, a numerical example is presented to illustrate the less conservatism and lower computational complexity of the derived results.
Haitao Liu; Fang Liu. New Stability Analysis Results for Linear System with Two Additive Time-Varying Delay Components. Complexity 2020, 2020, 1 -12.
AMA StyleHaitao Liu, Fang Liu. New Stability Analysis Results for Linear System with Two Additive Time-Varying Delay Components. Complexity. 2020; 2020 ():1-12.
Chicago/Turabian StyleHaitao Liu; Fang Liu. 2020. "New Stability Analysis Results for Linear System with Two Additive Time-Varying Delay Components." Complexity 2020, no. : 1-12.
Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting of four power grids with renewable generation units and energy storage systems are proposed using the advanced methods based on deep reinforcement learning and integral equations. First, the wind and solar irradiance potential of several sites on the lake Baikal’s banks is analyzed as well as the electric load as a function of the climatic conditions. The optimal selection of the energy storage system components is supported in online mode. The approach is justified using the retrospective meteorological datasets. Such a formulation will allow us to develop a number of valuable recommendations related to the optimal control of several autonomous AC/DC hybrid power systems with different structures, equipment composition and kind of AC or DC current. Developed approach provides the valuable information at different stages of AC/DC hybrid power systems projects development with stand-alone hybrid solar-wind power generation systems.
Denis Sidorov; Daniil Panasetsky; Nikita Tomin; Dmitriy Karamov; Aleksei Zhukov; Ildar Muftahov; Aliona Dreglea; Fang Liu; Yong Li. Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region. Energies 2020, 13, 1226 .
AMA StyleDenis Sidorov, Daniil Panasetsky, Nikita Tomin, Dmitriy Karamov, Aleksei Zhukov, Ildar Muftahov, Aliona Dreglea, Fang Liu, Yong Li. Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region. Energies. 2020; 13 (5):1226.
Chicago/Turabian StyleDenis Sidorov; Daniil Panasetsky; Nikita Tomin; Dmitriy Karamov; Aleksei Zhukov; Ildar Muftahov; Aliona Dreglea; Fang Liu; Yong Li. 2020. "Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region." Energies 13, no. 5: 1226.
Neutral-Point clamped (NPC) three-level inverters have a broad application prospect. However, the voltage imbalance of the capacitors and the drafting of its neutral-point voltage will generate voltage stresses on the switches and even increase the total harmonic distortion (THD) rate in their output. In this paper, an improved repetitive control method based on the idea of equivalent input disturbance (EID) compensation is adopted. By adding an additional neutral-point branch, the neutral-point voltage control problem is transformed into a disturbance suppression problem. The case studied are tested using MATLAB/SIMULINK software and the experiment results show that this proposed control strategy can overcome both periodic and non-periodic disturbance, restraining the voltage fluctuation at the neutral point to a lower level.
Kailiang. Zhang; Fang. Liu; Yong. Li. Repetitive Control of Neutral-Point Voltage in NPC Three-Level Inverters Based on EID Compensation. IFAC-PapersOnLine 2020, 53, 12429 -12434.
AMA StyleKailiang. Zhang, Fang. Liu, Yong. Li. Repetitive Control of Neutral-Point Voltage in NPC Three-Level Inverters Based on EID Compensation. IFAC-PapersOnLine. 2020; 53 (2):12429-12434.
Chicago/Turabian StyleKailiang. Zhang; Fang. Liu; Yong. Li. 2020. "Repetitive Control of Neutral-Point Voltage in NPC Three-Level Inverters Based on EID Compensation." IFAC-PapersOnLine 53, no. 2: 12429-12434.
Forecasting problems exist widely in our life. Its purpose is to enable decision makers to make effective responses to future changes. The traditional prediction methods based on probability and statistics cannot guarantee the accuracy of multivariable dynamic prediction under the background of high randomness and big data. In recent years, with the improvement of hardware computing ability and the large-scale increase of training data, deep learning has been widely applied in the field of forecasting. This paper focuses on the analysis of the application of recurrent neural networks (RNN), an advanced algorithm in deep learning, in the forecasting task. The forecasting models based on long short-term memory (LSTM) and gated recurrent unit (GRU) were established respectively, and the real data of power load and air pollution were verified. Compared with traditional machine learning algorithms, the simulation proves the superiority of the forecasting model based on RNN.
Qing Tao; Fang Liu; Denis Sidorov. Recurrent Neural Networks Application to Forecasting with Two Cases: Load and Pollution. Advances in Intelligent Systems and Computing 2019, 369 -378.
AMA StyleQing Tao, Fang Liu, Denis Sidorov. Recurrent Neural Networks Application to Forecasting with Two Cases: Load and Pollution. Advances in Intelligent Systems and Computing. 2019; ():369-378.
Chicago/Turabian StyleQing Tao; Fang Liu; Denis Sidorov. 2019. "Recurrent Neural Networks Application to Forecasting with Two Cases: Load and Pollution." Advances in Intelligent Systems and Computing , no. : 369-378.
Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting.
Fang Liu; Ranran Li; Aliona Dreglea. Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model. Energies 2019, 12, 3551 .
AMA StyleFang Liu, Ranran Li, Aliona Dreglea. Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model. Energies. 2019; 12 (18):3551.
Chicago/Turabian StyleFang Liu; Ranran Li; Aliona Dreglea. 2019. "Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model." Energies 12, no. 18: 3551.
In this paper, a transient stability prediction method based on deep belief network (DBN) and long short-term memory (LSTM) network is proposed. DBN is utilized to embed the original transient stability data into a low-dimensional representation space and assess transient stability preliminarily. Owing to the good performance of LSTM networks in extracting the features of time series for a longtime span, it is utilized to predict generator rotor angle trajectory of instability samples, which can identify the unstable generator in advance. The proposed method is validated by IEEE New England 39-bus system. Simulation results show that the proposed method has the merits of quickness and accuracy criteria and can provide guidelines for online monitoring of power system stability.
Li Liu; Yong Li; Yijia Cao; Fang Liu; Weiyu Wang; Jian Zuo. Transient Rotor Angle Stability Prediction Based on Deep Belief Network and Long Short-term Memory Network. IFAC-PapersOnLine 2019, 52, 176 -181.
AMA StyleLi Liu, Yong Li, Yijia Cao, Fang Liu, Weiyu Wang, Jian Zuo. Transient Rotor Angle Stability Prediction Based on Deep Belief Network and Long Short-term Memory Network. IFAC-PapersOnLine. 2019; 52 (4):176-181.
Chicago/Turabian StyleLi Liu; Yong Li; Yijia Cao; Fang Liu; Weiyu Wang; Jian Zuo. 2019. "Transient Rotor Angle Stability Prediction Based on Deep Belief Network and Long Short-term Memory Network." IFAC-PapersOnLine 52, no. 4: 176-181.
This paper presents an active disturbance rejection control (ADRC) technique for load frequency control of a wind integrated power system when communication delays are considered. To improve the stability of frequency control, equivalent input disturbances (EID) compensation is used to eliminate the influence of the load variation. In wind integrated power systems, two area controllers are designed to guarantee the stability of the overall closed-loop system. First, a simplified frequency response model of the wind integrated time-delay power system was established. Then the state-space model of the closed-loop system was built by employing state observers. The system stability conditions and controller parameters can be solved by some linear matrix inequalities (LMIs) forms. Finally, the case studies were tested using MATLAB/SIMULINK software and the simulation results show its robustness and effectiveness to maintain power-system stability.
Fang Liu; Kailiang Zhang; Runmin Zou; Liu; Zou. Robust LFC Strategy for Wind Integrated Time-Delay Power System Using EID Compensation. Energies 2019, 12, 3223 .
AMA StyleFang Liu, Kailiang Zhang, Runmin Zou, Liu, Zou. Robust LFC Strategy for Wind Integrated Time-Delay Power System Using EID Compensation. Energies. 2019; 12 (17):3223.
Chicago/Turabian StyleFang Liu; Kailiang Zhang; Runmin Zou; Liu; Zou. 2019. "Robust LFC Strategy for Wind Integrated Time-Delay Power System Using EID Compensation." Energies 12, no. 17: 3223.
Energy storage systems will play a key role in the power system of the twenty first century considering the large penetrations of variable renewable energy, growth in transport electrification and decentralisation of heating loads. Therefore reliable real time methods to optimise energy storage, demand response and generation are vital for power system operations. This paper presents a concise review of battery energy storage and an example of battery modelling for renewable energy applications and second details an adaptive approach to solve this load levelling problem with storage. A dynamic evolutionary model based on the first kind Volterra integral equation is used in both cases. A direct regularised numerical method is employed to find the least-cost dispatch of the battery in terms of integral equation solution. Validation on real data shows that the proposed evolutionary Volterra model effectively generalises conventional discrete integral model taking into account both SoH and the availability of generation/storage.
Denis Nikolaevich Sidorov; Ildar Rinatovich Muftahov; Nikita Tomin; Dmitriy Nikolaevich Karamov; Daniil Aleksandrovich Panasetsky; Aliona Dreglea; Fang Liu; Aoife Foley. A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations. IEEE Transactions on Industrial Informatics 2019, 16, 3451 -3459.
AMA StyleDenis Nikolaevich Sidorov, Ildar Rinatovich Muftahov, Nikita Tomin, Dmitriy Nikolaevich Karamov, Daniil Aleksandrovich Panasetsky, Aliona Dreglea, Fang Liu, Aoife Foley. A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations. IEEE Transactions on Industrial Informatics. 2019; 16 (5):3451-3459.
Chicago/Turabian StyleDenis Nikolaevich Sidorov; Ildar Rinatovich Muftahov; Nikita Tomin; Dmitriy Nikolaevich Karamov; Daniil Aleksandrovich Panasetsky; Aliona Dreglea; Fang Liu; Aoife Foley. 2019. "A Dynamic Analysis of Energy Storage With Renewable and Diesel Generation Using Volterra Equations." IEEE Transactions on Industrial Informatics 16, no. 5: 3451-3459.
Air pollution forecasting can provide the reliable information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short term forecasting model based on deep learning is proposed for PM2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 lm) concentration, and the convolutional-based bidirectional gated recurrent unit (CBGRU) method is presented, which combines 1D convnets (convolutional neural networks) and bidirectional GRU (gated recurrent unit) neural networks. The case is carried out by using the Beijing PM2.5 Data Set in UCI Machine Learning Repository. Comparing the prediction results with the traditional ones, it is proved that the error of CBGRU model is lower and the prediction performance is better.
Qing Tao; Fang Liu; Yong Li; Denis Sidorov. Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU. IEEE Access 2019, 7, 76690 -76698.
AMA StyleQing Tao, Fang Liu, Yong Li, Denis Sidorov. Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU. IEEE Access. 2019; 7 (99):76690-76698.
Chicago/Turabian StyleQing Tao; Fang Liu; Yong Li; Denis Sidorov. 2019. "Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU." IEEE Access 7, no. 99: 76690-76698.
As one of the important renewable energies, wind power has been exploited worldwide. Modeling plays an important role in the high penetration of wind farms in smart grids. Aggregation modeling, whose benefits include low computational complexity and high computing speed, is widely used in wind farm modeling and simulation. To contribute to the development of wind power generation, a comprehensive survey of the aggregation modeling of wind farms is given in this article. A wind farm aggregation model consists of three parts, respectively, the wind speed model, the wind turbine generator (WTG) model, and the WTG transmission system model. Different modeling and aggregation methods, principles, and formulas for the above three parts are introduced. First, the features and emphasis of different wind speed models are discussed. Then, the aggregated wind turbine generator (WTG) models are divided into single WTG and multi-WTG aggregation models, considering the aggregation of wind turbines and generators, respectively. The calculation methods for the wind conditions and parameters of different aggregation models are discussed. Finally, the WTG transmission model of the wind farm from the aggregation bus is introduced. Some research directions are highlighted in the end according to the issues related to the aggregation modeling of wind farms in smart grids.
Fang Liu; Junjie Ma; Wendan Zhang; Min Wu. A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid. Applied Sciences 2019, 9, 769 .
AMA StyleFang Liu, Junjie Ma, Wendan Zhang, Min Wu. A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid. Applied Sciences. 2019; 9 (4):769.
Chicago/Turabian StyleFang Liu; Junjie Ma; Wendan Zhang; Min Wu. 2019. "A Comprehensive Survey of Accurate and Efficient Aggregation Modeling for High Penetration of Large-Scale Wind Farms in Smart Grid." Applied Sciences 9, no. 4: 769.
A coordinate control strategy is proposed to secure fault-ride through (FRT) for voltage source converter (VSC) based multi-terminal high voltage dc transmissions (MTDC) integrating the offshore wind farms (OWFs). The short circuit fault in the ac grid obviously reduces the voltage at the connection point which, in turn, block the power output of MTDC systems instantaneously. The resulting power imbalance in MTDC system correspondingly charge the DC capacitors. In the absence of suitable countermeasures, this would lead to increased dc voltage, which would further lead to potential equipment damage in the installation. Therefore, for the OWF-integrated MTDC systems, keep ing the dc voltage within a reasonable value is the key to achieving the fault-ride through. The grid side converters (GSCs), wind farm side converters (WFCs), and doubly fed induction generators (DFIGs) will operate under the fault operation mode when there is a three-phase symmetrical fault. An improved reactive power compensation method is applied to GSCs’ controllers to enhance the stability of the ac grid. The controllers of WFCs reduce the ac voltage amplitude of OWFs according to the dc voltage signal, which correspondingly decreases the power transmitted to the MTDC system. As a result, the increase in dc voltage is restrained. Since the degree of voltage decline for WFCs is limited by the over-current capability of the power electronics, an active power reduction control of DFIG is used to complement with this method. Different simulation scenarios are presented by DIgSILENT PowerFactory to verify the effectiveness of the proposed control strategy.
Jing Li; Yong Li; Weiyu Wang; Yijia Cao; Kwang Y. Lee; Fang Liu. Fault-ride Through Control Strategy of Multi-terminal High Voltage DC Systems. IFAC-PapersOnLine 2018, 51, 540 -545.
AMA StyleJing Li, Yong Li, Weiyu Wang, Yijia Cao, Kwang Y. Lee, Fang Liu. Fault-ride Through Control Strategy of Multi-terminal High Voltage DC Systems. IFAC-PapersOnLine. 2018; 51 (28):540-545.
Chicago/Turabian StyleJing Li; Yong Li; Weiyu Wang; Yijia Cao; Kwang Y. Lee; Fang Liu. 2018. "Fault-ride Through Control Strategy of Multi-terminal High Voltage DC Systems." IFAC-PapersOnLine 51, no. 28: 540-545.