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Prof. Dr. Zhe Chen
Aalborg University, Department of Energy Technology, Pontoppidanstraede 111, 9220 Aalborg, Denmark

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0 Power Systems
0 Renewable Energy
0 Wind Power
0 Integrated energy systems
0 Power electronic applications

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Wind Power
Power Systems
Renewable Energy
Integrated energy systems
Power electronic applications

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Journal article
Published: 26 July 2021 in IEEE Transactions on Applied Superconductivity
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A novel superconducting magnet energy storage (SMES) damp-ing controller is designed in this paper, which adopts the method of combining integral clearing loop with PI controller, to make sure that the net energy deviation of SMES is zero and avoid deep charging-discharging of SMES during an oscillation pro-cess. Moreover, in order to appropriately set the controller pa-rameters considering the uncertainty disturbances of power sys-tems, the paper uses a finite Markov decision process, and adopts a deep reinforcement learning (DRL) based agent to ob-tain the optimal parameters. After training, the trained agent can act as a decision maker, providing the controller with real-time parameters under different operating condition. Time-domain simulation results show the usefulness of the designed controller and the advantage of the DRL approach.

ACS Style

Tao Li; Weihao Hu; Bin Zhang; Guozhou Zhang; Zhenyuan Zhang; Zhe Chen. SMES Damping Controller Design and Real-Time Parameters Tuning for Low-Frequency Oscillation. IEEE Transactions on Applied Superconductivity 2021, 31, 1 -4.

AMA Style

Tao Li, Weihao Hu, Bin Zhang, Guozhou Zhang, Zhenyuan Zhang, Zhe Chen. SMES Damping Controller Design and Real-Time Parameters Tuning for Low-Frequency Oscillation. IEEE Transactions on Applied Superconductivity. 2021; 31 (8):1-4.

Chicago/Turabian Style

Tao Li; Weihao Hu; Bin Zhang; Guozhou Zhang; Zhenyuan Zhang; Zhe Chen. 2021. "SMES Damping Controller Design and Real-Time Parameters Tuning for Low-Frequency Oscillation." IEEE Transactions on Applied Superconductivity 31, no. 8: 1-4.

Journal article
Published: 22 June 2021 in Renewable Energy
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With increasing proportion of wind energy in power systems, the intermittence of such energy makes the system run a wide range of operating conditions. In this context, ordinary power system stabilizers (PSS) tuned based on the linearized model of the system at one operating condition may not be able to effectively damp low frequency oscillations (LFO), which brings great challenges to the stability of the system. To this end, this paper proposes a novel sparsity promoting adaptive control method for the online self-tuning of the PSS parameter settings. Different from the existing adaptive control methods, the proposed method combines deep deterministic policy gradient (DDPG) algorithm and sensitivity analysis theory to train an agent to learn the sparse coordinated control policy of multi-PSS. After training, the well-trained agent can be employed for online sparse coordinated adaptive control, and the control signal is only applied, when it is required and only to the key PSS parameters that have the maximum influence on the system stability. Simulation results verify that the proposed method can make the PSS achieve the better performance of damping oscillation and robustness against the change of wind energy in comparison with other methods.

ACS Style

Guozhou Zhang; Weihao Hu; Di Cao; Qi Huang; Zhe Chen; Frede Blaabjerg. A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration. Renewable Energy 2021, 178, 363 -376.

AMA Style

Guozhou Zhang, Weihao Hu, Di Cao, Qi Huang, Zhe Chen, Frede Blaabjerg. A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration. Renewable Energy. 2021; 178 ():363-376.

Chicago/Turabian Style

Guozhou Zhang; Weihao Hu; Di Cao; Qi Huang; Zhe Chen; Frede Blaabjerg. 2021. "A novel deep reinforcement learning enabled sparsity promoting adaptive control method to improve the stability of power systems with wind energy penetration." Renewable Energy 178, no. : 363-376.

Journal article
Published: 18 June 2021 in IEEE Transactions on Industrial Electronics
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With an increasing number of inverter-interfaced generators (IIGs), the power system is undergoing massive shifts towards the power electronic dominated power system. Such paradigm change would pose significant challenges to existing fault analysis theory and the protection system, as a result of the disparate short-circuit response. Given this, the fault analysis theory needs to be further investigated and expanded to address issues arising from the new grid paradigm. Under this context, this paper proposes an analytic model for short-circuit analysis of IIGs with decoupled sequence control (DSC) based on the Laplace transform. With the proposed model, the full time-scale fault current expression can be obtained and the fault characteristic can be analyzed. Compared with existing studies, the proposed model distinguishes itself by three key merits. First, the proposed model takes into account the delay feature of controller, which enables to conduct analysis in transient. Second, the model covers the impact of controller parameters and low voltage ride through (LVRT) strategy in detail which usually is missing in most existing literature. Third, the proposed model provides theoretical foundation of IIGs with DSC in the fault analysis, which makes it more applicable for the protection setup issues in reality.

ACS Style

Qi Zhang; Dong Liu; Zhou Liu; Zhe Chen. Fault Modeling and Analysis of Grid-connected Inverters with Decoupled Sequence Control. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Qi Zhang, Dong Liu, Zhou Liu, Zhe Chen. Fault Modeling and Analysis of Grid-connected Inverters with Decoupled Sequence Control. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Qi Zhang; Dong Liu; Zhou Liu; Zhe Chen. 2021. "Fault Modeling and Analysis of Grid-connected Inverters with Decoupled Sequence Control." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Journal article
Published: 18 June 2021 in Renewable Energy
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In the context of “zero-waste city” and energy internet, the interaction and connection between the energy supply of biomass waste disposal and multi-energy systems are becoming increasingly close. On such a basis, this paper proposes a multi-energy microgrid (MEMG) framework for the comprehensive utilization of biomass waste energy. Firstly, the energy supply and demand model of biomass waste of domestic residual waste and kitchen waste is studied, and biomass disposal facilities are taken as the flexible resource of MEMG. Then, a multi-energy storage system is established for flexible regulation according to the fluctuating demand of multi-energy in MEMG. Considering the uncertainties of biomass waste production by residents, renewable energy output, and multi-energy load, a MEMG multi-objective robust optimal scheduling model is established with the objectives of minimizing operating costs and maximizing waste disposal. Finally, a numerical example simulation is carried out with a MEMG in Northeast China. The simulation results verify the effectiveness and economics of the MEMG integrated biomass waste treatment facility through different comparison scenarios, which can realize the effective integration of energy consumption and environmental friendliness.

ACS Style

Peng Sun; Teng Yun; Zhe Chen. Multi-objective robust optimization of multi-energy microgrid with waste treatment. Renewable Energy 2021, 178, 1198 -1210.

AMA Style

Peng Sun, Teng Yun, Zhe Chen. Multi-objective robust optimization of multi-energy microgrid with waste treatment. Renewable Energy. 2021; 178 ():1198-1210.

Chicago/Turabian Style

Peng Sun; Teng Yun; Zhe Chen. 2021. "Multi-objective robust optimization of multi-energy microgrid with waste treatment." Renewable Energy 178, no. : 1198-1210.

Journal article
Published: 10 June 2021 in Energy Conversion and Management
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As an essential development direction of energy internet, integrated energy system with interdisciplinary techniques is of great significance to promote multi-energy cooperation, realize low-carbon economic operation, and improve the flexible scheduling potential. This paper presents the study of an integrated power, heat and natural-gas system consisting of energy coupling units and wind power generation interconnected via a power grid. A deep reinforcement learning –based energy scheduling strategy is proposed to optimize multiple targets, including minimizing operational costs and ensuring power supply reliability. By scheduling the output of energy units, the economy and reliability of the considered system are improved. Taken diversified uncertainties into account, like intermittent of wind power and flexibility of load demand, the stochastic dynamic optimization problem is modeled as Markov decision process, and a soft actor-critic algorithm is introduced to solve the complex scheduling problem. The optimized decision-making action can be identified by the soft actor-critic algorithm through empirical learning without prediction information and prior knowledge. In the simulation, the proposed SAC-based agent has robust performance on solving optimization problems of different scenarios. Besides, the comparison study is carried out among benchmark reinforcement learning and heuristic algorithms, parameters of which are specifically given. The results demonstrate that when optimizing the comprehensive profits, the developed strategy reduced costs by up to 21.66%, compared to other algorithms.

ACS Style

Bin Zhang; Weihao Hu; Di Cao; Tao Li; Zhenyuan Zhang; Zhe Chen; Frede Blaabjerg. Soft actor-critic –based multi-objective optimized energy conversion and management strategy for integrated energy systems with renewable energy. Energy Conversion and Management 2021, 243, 114381 .

AMA Style

Bin Zhang, Weihao Hu, Di Cao, Tao Li, Zhenyuan Zhang, Zhe Chen, Frede Blaabjerg. Soft actor-critic –based multi-objective optimized energy conversion and management strategy for integrated energy systems with renewable energy. Energy Conversion and Management. 2021; 243 ():114381.

Chicago/Turabian Style

Bin Zhang; Weihao Hu; Di Cao; Tao Li; Zhenyuan Zhang; Zhe Chen; Frede Blaabjerg. 2021. "Soft actor-critic –based multi-objective optimized energy conversion and management strategy for integrated energy systems with renewable energy." Energy Conversion and Management 243, no. : 114381.

Journal article
Published: 28 May 2021 in Applied Energy
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The heat system has the larger inertia in the multi-energy system (MES) integrated electricity, heat and gas, and its uncertainty caused by the heating parameters variation of the practical project, which will make the scheduling and operation of the MES more difficult and complicated. In order to improve the energy efficiency and flexibility of MES, it is essential to explore the quantitative impact of thermal inertia on the regulation capacity of the MES. To tackle this problem, this paper proposes a robust coordinated optimization method for MES considering thermal inertia uncertainty (TIU). First, the multiple thermal inertia (MTI) of solid thermal storage electric boiler (STSEB), heating network (HN) and buildings (BD) are modeled, and the thermal inertia of physical models are simulated based on ANSYS finite element software. The quantitative method of the difference between the monitoring data of the heating network operation status and the thermal energy flow model is studied, and the TIU model is established. By discretizing the uncertainty domain, an improved two-stage robust optimal scheduling model for MES considering TIU is constructed. Finally, part of real MES in the Northeastern China is used in the case study. Simulation results show that the operating cost of the MES considering the uncertainty of wind power will increase by 26.9%. On this basis, the operating cost considering TIU is reduced by 16.3%. It can be seen that considering the TIU will enhance heat storage capacity of heating network and has positive benefits for MES regulation, which can balance the operating cost and robustness of the system, and promote wind power integration.

ACS Style

Peng Sun; Yun Teng; Zhe Chen. Robust coordinated optimization for multi-energy systems based on multiple thermal inertia numerical simulation and uncertainty analysis. Applied Energy 2021, 296, 116982 .

AMA Style

Peng Sun, Yun Teng, Zhe Chen. Robust coordinated optimization for multi-energy systems based on multiple thermal inertia numerical simulation and uncertainty analysis. Applied Energy. 2021; 296 ():116982.

Chicago/Turabian Style

Peng Sun; Yun Teng; Zhe Chen. 2021. "Robust coordinated optimization for multi-energy systems based on multiple thermal inertia numerical simulation and uncertainty analysis." Applied Energy 296, no. : 116982.

Journal article
Published: 25 May 2021 in Energies
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Inter-Turn Short Circuit (ITSC) fault in stator winding is a common fault in Doubly-Fed Induction Generator (DFIG)-based Wind Turbines (WTs). Improper measures in the ITSC fault affect the safety of the faulty WT and the power output of the Wind Farm (WF). This paper combines derating WTs and the power optimization of the WF to diminish the fault effect. At the turbine level, switching the derating strategy and the ITSC Fault Ride-Through (FRT) strategy is adopted to ensure that WTs safely operate under fault. At the farm level, the Particle Swarm Optimization (PSO)-based active power dispatch strategy is used to address proper power references in all of the WTs. The simulation results demonstrate the effectiveness of the proposed method. Switching the derating strategy can increase the power limit of the faulty WT, and the ITSC FRT strategy can ensure that the WT operates without excessive faulty current. The PSO-based power optimization can improve the power of the WF to compensate for the power loss caused by the faulty WT. With the proposed method, the competitiveness and the operational capacity of offshore WFs can be upgraded.

ACS Style

Kuichao Ma; Mohsen Soltani; Amin Hajizadeh; Jiangsheng Zhu; Zhe Chen. Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault. Energies 2021, 14, 3072 .

AMA Style

Kuichao Ma, Mohsen Soltani, Amin Hajizadeh, Jiangsheng Zhu, Zhe Chen. Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault. Energies. 2021; 14 (11):3072.

Chicago/Turabian Style

Kuichao Ma; Mohsen Soltani; Amin Hajizadeh; Jiangsheng Zhu; Zhe Chen. 2021. "Wind Farm Power Optimization and Fault Ride-Through under Inter-Turn Short-Circuit Fault." Energies 14, no. 11: 3072.

Journal article
Published: 18 May 2021 in IEEE Transactions on Industrial Informatics
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The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This paper proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining excellent performance of capturing the forecasting uncertainties.

ACS Style

Di Cao; Junbo Zhao; Weihao Hu; Yingchen Zhang; Qishu Liao; Zhe Chen; Frede Blaabjerg. Robust Deep Gaussian Process-based Probabilistic Electrical Load Forecasting against Anomalous Events. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.

AMA Style

Di Cao, Junbo Zhao, Weihao Hu, Yingchen Zhang, Qishu Liao, Zhe Chen, Frede Blaabjerg. Robust Deep Gaussian Process-based Probabilistic Electrical Load Forecasting against Anomalous Events. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.

Chicago/Turabian Style

Di Cao; Junbo Zhao; Weihao Hu; Yingchen Zhang; Qishu Liao; Zhe Chen; Frede Blaabjerg. 2021. "Robust Deep Gaussian Process-based Probabilistic Electrical Load Forecasting against Anomalous Events." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.

Journal article
Published: 13 May 2021 in IEEE Transactions on Power Systems
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This letter presents new perspectives on power control for AC microgrid considering operation cost and efficiency, simultaneously. A multi-objective optimization model is first established, and optimal operation conditions are derived by Lagrange Multiplier Method. Furthermore, a self-optimization droop control strategy with subject to optimal operation conditions is proposed to improve the overall operation performance. Simulation results validate the effectiveness of the proposed optimization method and self-optimization droop control strategy.

ACS Style

Wenbin Yuan; Yanbo Wang; Zhe Chen. New Perspectives on Power Control of AC Microgrid Considering Operation Cost and Efficiency. IEEE Transactions on Power Systems 2021, 36, 4844 -4847.

AMA Style

Wenbin Yuan, Yanbo Wang, Zhe Chen. New Perspectives on Power Control of AC Microgrid Considering Operation Cost and Efficiency. IEEE Transactions on Power Systems. 2021; 36 (5):4844-4847.

Chicago/Turabian Style

Wenbin Yuan; Yanbo Wang; Zhe Chen. 2021. "New Perspectives on Power Control of AC Microgrid Considering Operation Cost and Efficiency." IEEE Transactions on Power Systems 36, no. 5: 4844-4847.

Journal article
Published: 30 March 2021 in International Journal of Electrical Power & Energy Systems
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The controlled fault characteristics of power electronic-based power plant during unbalanced grid faults can heavily affect the proper operation of distance relay. In order to solve this issue, a fault control scheme is developed for the power electronic-based power plant, which is compliant with grid protection during unbalanced grid faults. Firstly, we analyze the essential problem of the distance relay on the transmission line connecting the power electronic-based power plant. Subsequently, we build the equivalent fault models of power electronic-based power plant in the sequence systems based on the reactive power supporting requirements defined in the modern fault-ride-through rules and the protection demands. From the analytic equation of apparent impedance in different fault loops, a novel protection collaborative fault control scheme to determine the proper current command angle of the power electronic-based power plant during unbalanced grid faults is deduced. Simulation results prove that the proposed fault control of power electronic-based power plant can improve the accuracy of reactance measurements in distance relay, which in turn reduces the malfunction risk of the relay.

ACS Style

Kaiqi Ma; Zhe Chen; Zhou Liu; Claus Leth Bak; Manuel Castillo. Protection collaborative fault control for power electronic-based power plants during unbalanced grid faults. International Journal of Electrical Power & Energy Systems 2021, 130, 107009 .

AMA Style

Kaiqi Ma, Zhe Chen, Zhou Liu, Claus Leth Bak, Manuel Castillo. Protection collaborative fault control for power electronic-based power plants during unbalanced grid faults. International Journal of Electrical Power & Energy Systems. 2021; 130 ():107009.

Chicago/Turabian Style

Kaiqi Ma; Zhe Chen; Zhou Liu; Claus Leth Bak; Manuel Castillo. 2021. "Protection collaborative fault control for power electronic-based power plants during unbalanced grid faults." International Journal of Electrical Power & Energy Systems 130, no. : 107009.

Journal article
Published: 18 March 2021 in IEEE Transactions on Power Systems
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To better damp out the multi-mode oscillations in an uncertain environment, a novel multi-band power system stabilizer (MBPSS) is proposed. Compared with other MBPSSs, the proposed controller has a well-balanced structure, and each band is designed to address a target low-frequency oscillation (LFO) mode. A deep reinforcement learning-enabled agent is developed to effectively tune the control parameters that are adaptive to system uncertainties and different operating conditions. Comparative results with other types of PSSs on the IEEE 68-bus system demonstrate that the proposed method has better performance of damping out LFO and robustness against unseen operating conditions and faults.

ACS Style

Guozhou Zhang; Weihao Hu; Junbo Zhao; Di Cao; Zhe Chen; Frede Blaabjerg. A Novel Deep Reinforcement Learning Enabled Multi-Band PSS for Multi-Mode Oscillation Control. IEEE Transactions on Power Systems 2021, 36, 3794 -3797.

AMA Style

Guozhou Zhang, Weihao Hu, Junbo Zhao, Di Cao, Zhe Chen, Frede Blaabjerg. A Novel Deep Reinforcement Learning Enabled Multi-Band PSS for Multi-Mode Oscillation Control. IEEE Transactions on Power Systems. 2021; 36 (4):3794-3797.

Chicago/Turabian Style

Guozhou Zhang; Weihao Hu; Junbo Zhao; Di Cao; Zhe Chen; Frede Blaabjerg. 2021. "A Novel Deep Reinforcement Learning Enabled Multi-Band PSS for Multi-Mode Oscillation Control." IEEE Transactions on Power Systems 36, no. 4: 3794-3797.

Journal article
Published: 18 February 2021 in IEEE Transactions on Power Electronics
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With the aim of reducing the reactive power for the Dual-Active-Bridge (DAB) converter, this letter proposes an artificial intelligence (AI) aided minimum reactive power control method based on harmonic analysis method. Specifically, As an advanced algorithm of the deep reinforcement learning (DRL), the Deep-Deterministic-Policy-Gradient (DDPG) is used to train an agent off-line. During the training of DDPG algorithm, the Three-Phase-Shift (TPS) modulation is adopted and the Zero-Voltage-Switching (ZVS) constraints are considered. Thus, the trained agent of the DDPG which likes an implicit function, can provide optimal control strategies for the DAB converter in real-time with the minimum reactive power and soft switching performance in the continuous operation range. Finally, experimental results validate the feasibility and correctness of the proposed AI based optimized method.

ACS Style

Yuanhong Tang; Weihao Hu; Di Cao; Nie Hou; Yunwei Ryan Li; Zhe Chen; Frede Ge Blaabjerg. Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method. IEEE Transactions on Power Electronics 2021, 36, 9704 -9710.

AMA Style

Yuanhong Tang, Weihao Hu, Di Cao, Nie Hou, Yunwei Ryan Li, Zhe Chen, Frede Ge Blaabjerg. Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method. IEEE Transactions on Power Electronics. 2021; 36 (9):9704-9710.

Chicago/Turabian Style

Yuanhong Tang; Weihao Hu; Di Cao; Nie Hou; Yunwei Ryan Li; Zhe Chen; Frede Ge Blaabjerg. 2021. "Artificial Intelligence-Aided Minimum Reactive Power Control for the DAB Converter Based on Harmonic Analysis Method." IEEE Transactions on Power Electronics 36, no. 9: 9704-9710.

Journal article
Published: 04 February 2021 in IEEE Transactions on Sustainable Energy
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This paper proposes an attention enabled multi-agent deep reinforcement learning (MADRL) framework for active distribution network decentralized Volt-VAR control. Using the unsupervised clustering, the whole distribution system can be decomposed into several sub-networks according to the voltage and reactive power sensitivity relationships. Then, the distributed control problem of each sub-network is modeled as Markov games and solved by the improved MADRL algorithm, where each sub-network is modeled as an adaptive agent. An attention mechanism is developed to help each agent focus on specific information that is mostly related to the reward. All agents are centrally trained offline to learn the optimal coordinated Volt-VAR control strategy and executed in a decentralized manner to make online decisions with only local information. Compared with other distributed control approaches, the proposed method can effectively deal with uncertainties, achieve fast decision makings, and significantly reduce the communication requirements. Comparison results with model-based and data-driven methods on IEEE 33-bus and 123-bus systems demonstrate the benefits of the proposed approach.

ACS Style

Di Cao; Junbo Zhao; Weihao Hu; Fei Ding; Qi Huang; Zhe Chen. Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs. IEEE Transactions on Sustainable Energy 2021, 12, 1582 -1592.

AMA Style

Di Cao, Junbo Zhao, Weihao Hu, Fei Ding, Qi Huang, Zhe Chen. Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs. IEEE Transactions on Sustainable Energy. 2021; 12 (3):1582-1592.

Chicago/Turabian Style

Di Cao; Junbo Zhao; Weihao Hu; Fei Ding; Qi Huang; Zhe Chen. 2021. "Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs." IEEE Transactions on Sustainable Energy 12, no. 3: 1582-1592.

Journal article
Published: 02 February 2021 in IEEE Transactions on Industrial Electronics
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This paper presents a novel integrated cascade bidirectional dc-dc converter for optimizing a centralized charge equalization system. Through the integrated cascade structure, the polarity switches and the bidirectional dc-dc converter in the traditional centralized system are integrated, and the cells with different voltage polarities are equalized by controlling the operating state of the converter. Compared with the conventional methods, the number of active switches is significantly reduced, leading to a more compact size and a higher reliability. Moreover, with a phase-shifted PWM modulation strategy, the peak value of the transformer current is reduced and the soft-switching operation is realized, thus greatly reducing the transformer and switching losses. Finally, the experimental results on 13 series-connected battery cells show that the proposed scheme exhibits the excellent performance in terms of equalizing efficiency and speed. The performance improvement of the proposed equalizer is further validated by a systematic comparison with the conventional centralized equalization methods.

ACS Style

Xianbin Qi; Yi Wang; Yanbo Wang; Zhe Chen. Optimization of Centralized Equalization Systems Based on an Integrated Cascade Bidirectional DC-DC Converter. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Xianbin Qi, Yi Wang, Yanbo Wang, Zhe Chen. Optimization of Centralized Equalization Systems Based on an Integrated Cascade Bidirectional DC-DC Converter. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Xianbin Qi; Yi Wang; Yanbo Wang; Zhe Chen. 2021. "Optimization of Centralized Equalization Systems Based on an Integrated Cascade Bidirectional DC-DC Converter." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Journal article
Published: 28 January 2021 in Energy
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To deal with the high penetration of renewable energy, modern energy systems strive to introduce flexible resources to provide more flexible and higher quality services. This paper focuses on the coordination of flexible resources across different energy carriers under the market environment to accommodate different levels of wind power. The integration of gas, heat and electricity systems providing customers with multiple options for satisfying their energy demands is described. Considering that energy system operators are independent or have limited communication based on the existing market mechanism, an equilibrium problem is first formulated for the optimal scheduling strategy, where each subsystem operator pursues its own benefit. Since there is energy conversion between different energy subsystems, each subsystem operator has to consider the actions of other operators and coordinate with each other until an equilibrium. An illustrative case study is then analyzed to show that the proposed model allows each subsystem operator to make an optimal action for maximizing its profit, and reflects prices and volumes of the energy transaction among energy subsystems. Furthermore, the simulation results indicate that the coordination of flexible resources has significant benefits in the integrated energy system to reduce wind curtailment and improve total social welfare.

ACS Style

Yufei Xi; Jiakun Fang; Zhe Chen; Qing Zeng; Henrik Lund. Optimal coordination of flexible resources in the gas-heat-electricity integrated energy system. Energy 2021, 223, 119729 .

AMA Style

Yufei Xi, Jiakun Fang, Zhe Chen, Qing Zeng, Henrik Lund. Optimal coordination of flexible resources in the gas-heat-electricity integrated energy system. Energy. 2021; 223 ():119729.

Chicago/Turabian Style

Yufei Xi; Jiakun Fang; Zhe Chen; Qing Zeng; Henrik Lund. 2021. "Optimal coordination of flexible resources in the gas-heat-electricity integrated energy system." Energy 223, no. : 119729.

Journal article
Published: 23 December 2020 in IEEE Access
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In this article, a new soft starting control scheme based on an artificial neural network (ANN) is presented for a three-phase induction motor (IM) drive system. The main task of the control scheme is to keep the accelerating torque constant at a level based on the value of reference acceleration. This is accomplished by the proper choice of the firing angles of thyristors in the soft starter. Using the ANN approach, the complexity of the online determination of the thyristors firing angles is resolved. The IM torque-speed characteristic curves are firstly used to train the ANN model. Secondly, the IM- soft starter system is modeled using MATLAB/SIMULINK. To prove the effectiveness of the proposed ANN-based acceleration control scheme, different reference accelerations and loading conditions are applied and investigated. Finally, a laboratory prototype of 3 kW soft starter is implemented. The proposed control scheme is executed in a real-time environment using a digital signal processor (Model: TMS320F28335). The simulation and real-time results significantly confirm that the proposed controller can efficiently reduce the IM starting current and torque pulsations. This in turn ensures a smooth acceleration of the IM during the starting process. Moreover, the proposed control scheme has the superiority over several soft starting control schemes since it has a simple control circuit configuration, less required sensors, and low computational burden of the control algorithm.

ACS Style

Amir Abdel Menaem; Mohamed Elgamal; Abdel-Haleem Abdel-Aty; Emad E. Mahmoud; Zhe Chen; Mohamed A. Hassan. A Proposed ANN-Based Acceleration Control Scheme for Soft Starting Induction Motor. IEEE Access 2020, 9, 4253 -4265.

AMA Style

Amir Abdel Menaem, Mohamed Elgamal, Abdel-Haleem Abdel-Aty, Emad E. Mahmoud, Zhe Chen, Mohamed A. Hassan. A Proposed ANN-Based Acceleration Control Scheme for Soft Starting Induction Motor. IEEE Access. 2020; 9 ():4253-4265.

Chicago/Turabian Style

Amir Abdel Menaem; Mohamed Elgamal; Abdel-Haleem Abdel-Aty; Emad E. Mahmoud; Zhe Chen; Mohamed A. Hassan. 2020. "A Proposed ANN-Based Acceleration Control Scheme for Soft Starting Induction Motor." IEEE Access 9, no. : 4253-4265.

Journal article
Published: 22 December 2020 in IEEE Transactions on Energy Conversion
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With the steady development of technology, electric motors (EMs) have become one of the most important components in modern industry. To ensure stable industrial production, detecting and classifying the EM faults is crucial. A novel intelligent deep-learning-based multi-fault detection method for EMs under varying working conditions is proposed in this paper. This method involves two steps: first, a 2D convolution network without pooling layer is proposed to extract features from raw EM data. In addition, a long short-term memory (LSTM) network is applied to extract the fault features for comparison. Second, a capsule network (Caps-Net) based on a dynamic routing algorithm is used as a classifier to realize intelligent multi-fault detection and improve the generalization performance of the proposed model. The proposed method is applicable to raw physical signals of EMs, which improves the overall efficiency of the fault detection. Moreover, the proposed method has a strong generalization ability. The simulation results demonstrate that the proposed approach can achieve higher accuracy than various benchmark methods. Moreover, its accuracy is at least 3% and 10% higher than those of other state-of-the-art models under two working conditions, in which the load type and size of the EM are changed, respectively.

ACS Style

Jianjun Chen; Weihao Hu; Di Cao; Man Zhang; Qi Huang; Zhe Chen; Frede Blaabjerg. Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors. IEEE Transactions on Energy Conversion 2020, 36, 2173 -2184.

AMA Style

Jianjun Chen, Weihao Hu, Di Cao, Man Zhang, Qi Huang, Zhe Chen, Frede Blaabjerg. Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors. IEEE Transactions on Energy Conversion. 2020; 36 (3):2173-2184.

Chicago/Turabian Style

Jianjun Chen; Weihao Hu; Di Cao; Man Zhang; Qi Huang; Zhe Chen; Frede Blaabjerg. 2020. "Novel Data-Driven Approach Based on Capsule Network for Intelligent Multi-Fault Detection in Electric Motors." IEEE Transactions on Energy Conversion 36, no. 3: 2173-2184.

Journal article
Published: 17 December 2020 in IEEE Open Journal of the Industrial Electronics Society
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Existing impedance-based stability criterion (IBSC) for electromagnetic stability assessment of multiple-grid-connected-inverter (GCI)-based power systems suffers from several limitations. First, global stability feature is hard to be obtained if Nyquist-criterion-based IBSC is used. Second, heavy computational burdens caused by either right-half-plane (RHP) poles calculation of impedance ratios or nodal admittance matrix construction can be involved. Third, it's not easy to locate the oscillation origin, since the dynamics of individual components are missing in the aggregated load and source sub-modules. This article aims to overcome the aforementioned three limitations of the existing IBSC. First, frequency responses of the load impedance and source admittance defined at each node in a selected components aggregation path are obtained by aggregating individual components (e.g., GCIs and transmission lines), from which imaginary parts of RHP poles of these load impedances and source admittances are directly identified without knowing analytical expressions of these load impedances and source admittances. Then, based on the Nyquist plots of minor loop gains (defined as the ratios of the impedance frequency responses of these load and source sub-modules), stability features of these selected nodes are obtained. Finally, if some nodes are unstable, the oscillation origin is located based on numbers of the RHP poles of these load impedances and source admittances. Compared to the existing IBSC, the presented method can assess global stability and locate oscillation origin more efficiently. The local circulating current issue, as a main obstacle of the existing IBSC, can also be identified. Time-domain simulation results in Matlab/Simulink platform and real-time verification results in OPAL-RT platform of a four-GCI-based radial power plant validate the effectiveness of the presented electromagnetic oscillation origin location method.

ACS Style

Weihua Zhou; Raymundo E. Torres-Olguin; Mehdi Karbalaye Zadeh; Behrooz Bahrani; Yanbo Wang; Zhe Chen. Electromagnetic Oscillation Origin Location in Multiple-Inverter-Based Power Systems Using Components Impedance Frequency Responses. IEEE Open Journal of the Industrial Electronics Society 2020, 2, 1 -20.

AMA Style

Weihua Zhou, Raymundo E. Torres-Olguin, Mehdi Karbalaye Zadeh, Behrooz Bahrani, Yanbo Wang, Zhe Chen. Electromagnetic Oscillation Origin Location in Multiple-Inverter-Based Power Systems Using Components Impedance Frequency Responses. IEEE Open Journal of the Industrial Electronics Society. 2020; 2 ():1-20.

Chicago/Turabian Style

Weihua Zhou; Raymundo E. Torres-Olguin; Mehdi Karbalaye Zadeh; Behrooz Bahrani; Yanbo Wang; Zhe Chen. 2020. "Electromagnetic Oscillation Origin Location in Multiple-Inverter-Based Power Systems Using Components Impedance Frequency Responses." IEEE Open Journal of the Industrial Electronics Society 2, no. : 1-20.

Journal article
Published: 15 December 2020 in IEEE Transactions on Smart Grid
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Due to the increasing penetration of gas-fired units and power to gas facilities, the electrical power system and natural gas system are more and more bi-directionally coupled. To tackle the challenges on the optimal scheduling operation of an integrated gas and power systems (IGPS), this paper focuses on developing a novel approach to build a continuous spatial-temporal optimal operation schedule model. In the light of different time constants of the electrical power and natural gas systems, the continuous spatial-temporal optimal operation schedule model of IGPS is formulated in function space. Additionally, Bernstein polynomials are used to reformulate the continuous spatial-temporal optimization problem of IGPS to mixed-integer linear programming. In the study cases, the simulation results of a simple integrated system and a combined IEEE 39-bus power system and Belgian natural gas network demonstrate the accuracy and effectiveness of the proposed model.

ACS Style

Chao Zheng; Jiakun Fang; Shaorong Wang; Xiaomeng Ai; Zhou Liu; Zhe Chen. Energy Flow Optimization of Integrated Gas and Power Systems in Continuous Time and Space. IEEE Transactions on Smart Grid 2020, 12, 2611 -2624.

AMA Style

Chao Zheng, Jiakun Fang, Shaorong Wang, Xiaomeng Ai, Zhou Liu, Zhe Chen. Energy Flow Optimization of Integrated Gas and Power Systems in Continuous Time and Space. IEEE Transactions on Smart Grid. 2020; 12 (3):2611-2624.

Chicago/Turabian Style

Chao Zheng; Jiakun Fang; Shaorong Wang; Xiaomeng Ai; Zhou Liu; Zhe Chen. 2020. "Energy Flow Optimization of Integrated Gas and Power Systems in Continuous Time and Space." IEEE Transactions on Smart Grid 12, no. 3: 2611-2624.

Journal article
Published: 28 November 2020 in Renewable Energy
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Extensive studies have been carried out on various hybrid energy systems (HESs) for providing electricity to off-grid areas. However, a standalone HES that is capable of providing power and gas, has been less studied. In this paper, a standalone Photovoltaic (PV)-battery-methanation HES is proposed to provide adequate, reliable and cost-effective electricity and gas to the local consumers. Identifying a potential solution to maximize the reliability of the system, asked by consumers, and to minimize costs required by the investors is challenging. Bi-level programming is adopted in this study to tackle the pre-mentioned issue. In the outer layer, an optimal design is obtained by means of particle swarm optimization. In the inner layer, an optimal operation strategy is found under the optimal design of the outer layer using sequential quadratic programming. The results indicate that 1) The bi-level programming used in this study can find the optimal solution; 2) The proposed HES is proved to be able to supply power and gas simultaneously. 3) Compared with the right most and leftmost points on Pareto set, the total costs are reduced by 17.77% and 2.16%.

ACS Style

Xiao Xu; Weihao Hu; Di Cao; Wen Liu; Qi Huang; Yanting Hu; Zhe Chen. Enhanced design of an offgrid PV-battery-methanation hybrid energy system for power/gas supply. Renewable Energy 2020, 167, 440 -456.

AMA Style

Xiao Xu, Weihao Hu, Di Cao, Wen Liu, Qi Huang, Yanting Hu, Zhe Chen. Enhanced design of an offgrid PV-battery-methanation hybrid energy system for power/gas supply. Renewable Energy. 2020; 167 ():440-456.

Chicago/Turabian Style

Xiao Xu; Weihao Hu; Di Cao; Wen Liu; Qi Huang; Yanting Hu; Zhe Chen. 2020. "Enhanced design of an offgrid PV-battery-methanation hybrid energy system for power/gas supply." Renewable Energy 167, no. : 440-456.