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JunYou Yang
Shenyang University of Technology, Shenyang, 110870, China

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Journal article
Published: 05 July 2021 in Energy
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The utilization of combined heat and power (CHP) units is a key way to enhance renewable energy accommodation. However, carbon emissions of CHP challenge the pollution and economic optimization of integrated energy system (IES). To tackle this challenge, we propose a novel model and optimal dispatch for CHP with power-to-gas (P2G) and carbon capture system (CCS), which solves the problems of the carbon source required for P2G and the CHP's carbon emissions by the optimal dispatch in IES. The model takes the CHP, P2G, and CCS as a whole system. The operation rule of the model is developed, and its coupling characteristics of electric power, heat power, gas power and carbon are analyzed. Correspondingly, the optimization strategy of IES considers the proposed model and carbon trading mechanism. The optimal dispatch model of IES is established and solved by YALMIP, and GUROBI. Our method is verified by simulation. Compared with other conventional models, the accommodation capacity of renewable energy with the proposed method is enhanced, and the carbon emissions and operating costs of IES are also reduced.

ACS Style

Yiming Ma; Haixin Wang; Feng Hong; JunYou Yang; Zhe Chen; Haoqian Cui; Jiawei Feng. Modeling and optimization of combined heat and power with power-to-gas and carbon capture system in integrated energy system. Energy 2021, 236, 121392 .

AMA Style

Yiming Ma, Haixin Wang, Feng Hong, JunYou Yang, Zhe Chen, Haoqian Cui, Jiawei Feng. Modeling and optimization of combined heat and power with power-to-gas and carbon capture system in integrated energy system. Energy. 2021; 236 ():121392.

Chicago/Turabian Style

Yiming Ma; Haixin Wang; Feng Hong; JunYou Yang; Zhe Chen; Haoqian Cui; Jiawei Feng. 2021. "Modeling and optimization of combined heat and power with power-to-gas and carbon capture system in integrated energy system." Energy 236, no. : 121392.

Journal article
Published: 21 June 2021 in IEEE Transactions on Applied Superconductivity
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In practical application of microgrid cluster, the lack of full detailed information cause the failure of dynamic modeling. Although some data-driven black-box modeling method can tackle this problem, insufficient usage of prior known physical information may reduce the modeling accuracy. To tackle this challenge, a hybrid physical-data-driven method is proposed for the dynamic behavior modeling of microgrid cluster. Motivated by the equivalence of recurrent neural network (RNN) and differential equations, the differential-algebraic equations (DAEs) of unknown part are represented by gate recurrent unit (GRU) based neural network. The DAEs of prior known physical stage are embedded into the proposed neural network, which avoid unnecessary model training of prior known section and improving the modeling efficiency. At first, the basic idea of RNN based dynamic modeling is explained. Then, the modeling guidelines including data preparation and parameter design are suggested. Finally, the effectiveness of the proposed method is confirmed by a test system formed by three microgrids under grid fault and operating point changing conditions.

ACS Style

Yunlu Li; Zizhao Wang; JunYou Yang; Xian Wang; Jiawei Feng. Dynamic Equivalence Modeling for Microgrid Cluster by Using Physical-Data-Driven Method. IEEE Transactions on Applied Superconductivity 2021, PP, 1 -1.

AMA Style

Yunlu Li, Zizhao Wang, JunYou Yang, Xian Wang, Jiawei Feng. Dynamic Equivalence Modeling for Microgrid Cluster by Using Physical-Data-Driven Method. IEEE Transactions on Applied Superconductivity. 2021; PP (99):1-1.

Chicago/Turabian Style

Yunlu Li; Zizhao Wang; JunYou Yang; Xian Wang; Jiawei Feng. 2021. "Dynamic Equivalence Modeling for Microgrid Cluster by Using Physical-Data-Driven Method." IEEE Transactions on Applied Superconductivity PP, no. 99: 1-1.

Journal article
Published: 16 June 2021 in Energy
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Solid electric thermal storage (SETS) can convert electricity into heat energy, which is scheduled to alleviate wind power curtailment during the heating period. However, different consumer behavior characteristics of SETSs cause the scheduled results to be inconsistent with expectations by the existing methods, which is crucial to schedule distributed SETSs in combined electricity and heat networks (CEHN). To tack this challenge, we propose an optimal schedule framework based on consumer behavior characteristics of distributed SETSs and evaluation indices. Firstly, a SETS power prediction model combining the cyber-physical approach (CPA) with iterative dichotomiser3 (ID3) is proposed, and the prediction results are used to obtain the consumer behavior characteristics of various types of distributed SETSs. Secondly, an optimal schedule model of CEHN is developed considering different consumer behavior characteristics of SETSs. Thirdly, to evaluate the performance of the proposed schedule model, two evaluation indices are developed to enhance the consistency of scheduled results and expectations of electric and heat power. The proposed framework is verified by numerical simulations. Compared with the two traditional methods, the wind power curtailment by the proposed methods is reduced by 5.49% and 0.34%, and electric and heat power evaluation indices are enhanced by 24.52% and 20.08%, respectively.

ACS Style

Huichao Ji; Haixin Wang; JunYou Yang; Jiawei Feng; Yongyue Yang; Martin Onyeka Okoye. Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks. Energy 2021, 234, 121237 .

AMA Style

Huichao Ji, Haixin Wang, JunYou Yang, Jiawei Feng, Yongyue Yang, Martin Onyeka Okoye. Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks. Energy. 2021; 234 ():121237.

Chicago/Turabian Style

Huichao Ji; Haixin Wang; JunYou Yang; Jiawei Feng; Yongyue Yang; Martin Onyeka Okoye. 2021. "Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks." Energy 234, no. : 121237.

Journal article
Published: 13 April 2021 in Energy Reports
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One of the major concerns of the utility companies is to ensure that the generation capacity (GC) is maintained above the load growth. The demand for assessment of the GC at non-distant time intervals is thus crucial. In general, the rising load is linearly proportional to the generation deficiency. A linear regression approach had been successfully developed to predictively accommodate this demand to avert the issue of deficit supply arising from an unforeseen delay. The Monte Carlo (MC) technique was used in the generation system (GS) modeling. However, due to the inherent stochasticity associated with the MC algorithm, the emerging graphical relationship between the load and the generation deficiency is generally linear but always maintains nonlinearity at various intervals along the gradient curve. This paper integrates the artificial neural network (ANN) nonlinear feature using the Levenberg–Marquardt training algorithm with the MC simulation to accommodate the MC-associated nonlinearities to improve the generation system reliability (GSR) prediction. The generalization performance of the prediction obtained on the test data was found to have been greatly improved.

ACS Style

Martin Onyeka Okoye; JunYou Yang; Yunlu Li. The nonlinearity property accommodation in the Monte Carlo method of generation system reliability prediction by the neural network model. Energy Reports 2021, 7, 505 -510.

AMA Style

Martin Onyeka Okoye, JunYou Yang, Yunlu Li. The nonlinearity property accommodation in the Monte Carlo method of generation system reliability prediction by the neural network model. Energy Reports. 2021; 7 ():505-510.

Chicago/Turabian Style

Martin Onyeka Okoye; JunYou Yang; Yunlu Li. 2021. "The nonlinearity property accommodation in the Monte Carlo method of generation system reliability prediction by the neural network model." Energy Reports 7, no. : 505-510.

Short communication
Published: 13 April 2021 in Energy Reports
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The internal parameters and topology of the power converter are unknown in some practical cases. Existing modeling methods based on impedance frequency scanning method can only guarantee that the dynamic modeling is effective at a single working point. To make the established dynamic model effective in a wide range, an equivalent modeling method for power converter based LSTM Neural Network is presented. At first, the equivalence of black-box modeling problem and deep loop neural network is studied. Then, dynamic modeling method black-box power converter on wide operating range by using LSTM neural network is proposed. Finally, the simulation results under large disturbance and multi-operating points show that the proposed method is effective under wide operation range.

ACS Style

Yunlu Li; Guiqing Ma; JunYou Yang; Haixin Wang; Jiawei Feng; Yihua Ma. Dynamic equivalent modeling for power converter based on LSTM neural network in wide operating range. Energy Reports 2021, 7, 477 -484.

AMA Style

Yunlu Li, Guiqing Ma, JunYou Yang, Haixin Wang, Jiawei Feng, Yihua Ma. Dynamic equivalent modeling for power converter based on LSTM neural network in wide operating range. Energy Reports. 2021; 7 ():477-484.

Chicago/Turabian Style

Yunlu Li; Guiqing Ma; JunYou Yang; Haixin Wang; Jiawei Feng; Yihua Ma. 2021. "Dynamic equivalent modeling for power converter based on LSTM neural network in wide operating range." Energy Reports 7, no. : 477-484.

Short communication
Published: 22 December 2020 in Energy Reports
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The dynamic behaviors of microgrid become more complicated due to the increasing implementation of distributed energy generation, energy storage. It is significant to study the dynamic response for power planning, analysis, and control of microgrid. Hence, an accurate dynamic equivalent model is essential, since it can help to evaluate the performance by simulation to avoid the loss and danger in practical test. However, the dynamic model based on differential equation cannot be established because of the lack of information in most of time. To build dynamic model when microgrid is a black-box system, a gated recurrent unit based neural network is proposed in this paper. The proposed neural network can be treated as a black-box differential–algebraic equations. The structure design and model training procedure are presented in detail. Study cases are implemented to evaluate the performance of modeling method. The comparison results show that the proposed dynamic modeling method can precisely estimate the dynamic response of microgrid.

ACS Style

Yunlu Li; JunYou Yang; Haixin Wang; Jia Cui; Yihua Ma; Siyu Huang. Dynamic equivalent modeling for microgrid based on GRU. Energy Reports 2020, 6, 1291 -1297.

AMA Style

Yunlu Li, JunYou Yang, Haixin Wang, Jia Cui, Yihua Ma, Siyu Huang. Dynamic equivalent modeling for microgrid based on GRU. Energy Reports. 2020; 6 ():1291-1297.

Chicago/Turabian Style

Yunlu Li; JunYou Yang; Haixin Wang; Jia Cui; Yihua Ma; Siyu Huang. 2020. "Dynamic equivalent modeling for microgrid based on GRU." Energy Reports 6, no. : 1291-1297.

Journal article
Published: 17 November 2020 in Physical Communication
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In practical engineering, the communication networks of industrial systems are complex, and system models are generally unavailable. To overcome the requirement of mathematical models, several artificial-intelligence-based algorithms in multi-access edge computing are introduced for the performance optimization control of a benchmark microgrid in this paper. First, a neural-network-based identification scheme is proposed to combine with the online adaptive dynamic programming learning method, which avoids the requirement of system models. However, the identification errors are not taken into consideration. Next, to realize the model-free purpose without using the identification schemes, an online dual-network-based action-dependent heuristic dynamic programming method and a critic-only Q-learning approach are presented. Finally, the optimal control strategy is applied to a benchmark microgrid system to demonstrate the effectiveness of performance optimization.

ACS Style

Tie Li; JunYou Yang; Dai Cui. Artificial-intelligence-based algorithms in multi-access edge computing for the performance optimization control of a benchmark microgrid. Physical Communication 2020, 44, 101240 .

AMA Style

Tie Li, JunYou Yang, Dai Cui. Artificial-intelligence-based algorithms in multi-access edge computing for the performance optimization control of a benchmark microgrid. Physical Communication. 2020; 44 ():101240.

Chicago/Turabian Style

Tie Li; JunYou Yang; Dai Cui. 2020. "Artificial-intelligence-based algorithms in multi-access edge computing for the performance optimization control of a benchmark microgrid." Physical Communication 44, no. : 101240.

Research article
Published: 09 November 2020 in Mathematical Problems in Engineering
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The virtual synchronous generator (VSG) technology of inverter is widely used to provide the inertia and damping support for power system. However, an additional measurement device PLL (phase-locked loop) is required in the virtual synchronous generator grid connection to track the voltage phase, amplitude, and frequency, which restricts the flexible output of the distributed power generation system. To tackle this challenge, a method for grid-connected control of virtual synchronous generator based on virtual impedance is proposed. It is assumed that there is a virtual power exchange between the synchronous machine and the power grid when the virtual synchronous generator is off-grid, the virtual impedance is developed to calculate the virtual current, and when the virtual current is zero, the output voltage of the VSG can be synchronized with the voltage of the power grid, thereby seamlessly switching between off-grid and grid-connected VSG. A semiphysical simulation platform is built based on RT-LAB; simulation and experimental results show that the proposed grid synchronization control strategy of the VSG can achieve seamless transform between different VSG modes, which is simpler than the conventional synchronization control, while having a good active and reactive power tracing performance.

ACS Style

Guanfeng Zhang; JunYou Yang; Haixin Wang; Jia Cui. Presynchronous Grid-Connection Strategy of Virtual Synchronous Generator Based on Virtual Impedance. Mathematical Problems in Engineering 2020, 2020, 1 -9.

AMA Style

Guanfeng Zhang, JunYou Yang, Haixin Wang, Jia Cui. Presynchronous Grid-Connection Strategy of Virtual Synchronous Generator Based on Virtual Impedance. Mathematical Problems in Engineering. 2020; 2020 ():1-9.

Chicago/Turabian Style

Guanfeng Zhang; JunYou Yang; Haixin Wang; Jia Cui. 2020. "Presynchronous Grid-Connection Strategy of Virtual Synchronous Generator Based on Virtual Impedance." Mathematical Problems in Engineering 2020, no. : 1-9.

Journal article
Published: 22 August 2020 in Energies
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Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

ACS Style

Martin Onyeka Okoye; JunYou Yang; Zhenjiang Lei; Jingwei Yuan; Huichao Ji; Haixin Wang; Jiawei Feng; Tunmise Ayode Otitoju; Weidong Li. Predictive Reliability Assessment of Generation System. Energies 2020, 13, 4350 .

AMA Style

Martin Onyeka Okoye, JunYou Yang, Zhenjiang Lei, Jingwei Yuan, Huichao Ji, Haixin Wang, Jiawei Feng, Tunmise Ayode Otitoju, Weidong Li. Predictive Reliability Assessment of Generation System. Energies. 2020; 13 (17):4350.

Chicago/Turabian Style

Martin Onyeka Okoye; JunYou Yang; Zhenjiang Lei; Jingwei Yuan; Huichao Ji; Haixin Wang; Jiawei Feng; Tunmise Ayode Otitoju; Weidong Li. 2020. "Predictive Reliability Assessment of Generation System." Energies 13, no. 17: 4350.

Journal article
Published: 27 July 2020 in IEEE Access
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In the power sector, microgrids play a supportive role in bridging the adequacy gap in the conventional electricity supply. Trading of the generated energy has recently been improved by blockchain technology which offers a new cheap, secure, and decentralized transaction approach. Its operation is however associated with an undesired inherent delay during energy transactions initiated by the prosumers, thus, failure to timely attend to incidences of urgent demand could end up in catastrophe at the consumer’s side. This paper thus proposes a cyber-enhanced transactive microgrid model using blockchain technology with optimized participants’ permission protocol to ameliorate this challenge. It is demonstrated that the optimized blockchain participants’ permission model leads to improved transaction speed and greater convenience. The transaction speed simulation is thereafter performed and it was also demonstrated that the node population has a greater effect than the transaction block size on the transaction speed improvement.

ACS Style

Martin Onyeka Okoye; JunYou Yang; Jia Cui; Zhenjiang Lei; Jingwei Yuan; Haixin Wang; Huichao Ji; Jiawei Feng; Chinenye Ezeh. A Blockchain-Enhanced Transaction Model for Microgrid Energy Trading. IEEE Access 2020, 8, 143777 -143786.

AMA Style

Martin Onyeka Okoye, JunYou Yang, Jia Cui, Zhenjiang Lei, Jingwei Yuan, Haixin Wang, Huichao Ji, Jiawei Feng, Chinenye Ezeh. A Blockchain-Enhanced Transaction Model for Microgrid Energy Trading. IEEE Access. 2020; 8 (99):143777-143786.

Chicago/Turabian Style

Martin Onyeka Okoye; JunYou Yang; Jia Cui; Zhenjiang Lei; Jingwei Yuan; Haixin Wang; Huichao Ji; Jiawei Feng; Chinenye Ezeh. 2020. "A Blockchain-Enhanced Transaction Model for Microgrid Energy Trading." IEEE Access 8, no. 99: 143777-143786.

Journal article
Published: 03 July 2020 in Energies
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The volatility and uncertainty of high-penetration renewable energy (RE) challenge the stability of the power system. To tackle this challenge, an optimal dispatch of high-penetration RE based on flexible resources (FRs) is proposed to enhance the ability of the power system to cope with uncertain disturbances. Firstly, the flexibility of a high-penetration RE integrated power system is analyzed. The flexibility margin of power supply and flexible adaptability of RE are then introduced as the evaluation indices for optimal operation. Finally, a multi-objective optimal dispatch model for power system flexibility enhancement based on FRs under the constraint of flexibility indices is proposed. The simulation results show that the proposed optimal dispatch can effectively enhance the flexibility of the power system and the penetration of RE and reduce pollutant emissions. Compared with the conventional method, the daily average emissions of CO2, SO2, and NOx with the proposed method are reduced by about 83,600 kg, 870 kg, and 370 kg, respectively, the maximum allowable volatility of net load is increased by 7.63%, and the average volatility of net load is reduced by 2.67%.

ACS Style

Jiawei Feng; JunYou Yang; Haixin Wang; Huichao Ji; Martin Onyeka Okoye; Jia Cui; Weichun Ge; Bo Hu; Gang Wang. Optimal Dispatch of High-Penetration Renewable Energy Integrated Power System Based on Flexible Resources. Energies 2020, 13, 3456 .

AMA Style

Jiawei Feng, JunYou Yang, Haixin Wang, Huichao Ji, Martin Onyeka Okoye, Jia Cui, Weichun Ge, Bo Hu, Gang Wang. Optimal Dispatch of High-Penetration Renewable Energy Integrated Power System Based on Flexible Resources. Energies. 2020; 13 (13):3456.

Chicago/Turabian Style

Jiawei Feng; JunYou Yang; Haixin Wang; Huichao Ji; Martin Onyeka Okoye; Jia Cui; Weichun Ge; Bo Hu; Gang Wang. 2020. "Optimal Dispatch of High-Penetration Renewable Energy Integrated Power System Based on Flexible Resources." Energies 13, no. 13: 3456.

Journal article
Published: 06 April 2020 in Applied Energy
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The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the auto-correlation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively.

ACS Style

Haixin Wang; JunYou Yang; Zhe Chen; Gen Li; Jun Liang; Yiming Ma; Henan Dong; Huichao Ji; Jiawei Feng. Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks. Applied Energy 2020, 267, 114879 .

AMA Style

Haixin Wang, JunYou Yang, Zhe Chen, Gen Li, Jun Liang, Yiming Ma, Henan Dong, Huichao Ji, Jiawei Feng. Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks. Applied Energy. 2020; 267 ():114879.

Chicago/Turabian Style

Haixin Wang; JunYou Yang; Zhe Chen; Gen Li; Jun Liang; Yiming Ma; Henan Dong; Huichao Ji; Jiawei Feng. 2020. "Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks." Applied Energy 267, no. : 114879.

Journal article
Published: 31 March 2020 in Electronics
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The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user’s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS.

ACS Style

Donghui Zhao; JunYou Yang; Dianchun Bai; Martin Onyeka Okoye; Yokoi Hiroshi. Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer. Electronics 2020, 9, 594 .

AMA Style

Donghui Zhao, JunYou Yang, Dianchun Bai, Martin Onyeka Okoye, Yokoi Hiroshi. Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer. Electronics. 2020; 9 (4):594.

Chicago/Turabian Style

Donghui Zhao; JunYou Yang; Dianchun Bai; Martin Onyeka Okoye; Yokoi Hiroshi. 2020. "Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer." Electronics 9, no. 4: 594.

Journal article
Published: 12 December 2019 in Energies
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This paper proposes a cyber–physical approach to enhance the prediction accuracy of electricity consumption of solid electric thermal storage (SETS) system, which integrates a physical model and a data-based cyber model. In the cyber–physical model, the prediction error of the physical model is used as an input of the cyber model to further calibrate the prediction error. Firstly, customers’ behavior characteristics are extracted by the integration of K-means and one-versus-one support vector machine. Secondly, based on the behavior characteristics and ambient temperature, the physical model is developed to predict daily electricity consumption. Finally, the error levels of physical model are classified, together with the temperature and prediction values of the physical model, are selected as the inputs of the cyber model using the back propagation (BP) neural network to calibrate the results of the physical model. The effectiveness of the proposed cyber–physical model (CPM) is verified by a 1 MW SETS system. The simulation results show that, compared with the physical model (PM) and cyber model (CM), the maximum relative errors (MRE) with the CPM are reduced to 25.4% and 4.8%, respectively.

ACS Style

Huichao Ji; JunYou Yang; Haixin Wang; Kun Tian; Martin Onyeka Okoye; Jiawei Feng. Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach. Energies 2019, 12, 4744 .

AMA Style

Huichao Ji, JunYou Yang, Haixin Wang, Kun Tian, Martin Onyeka Okoye, Jiawei Feng. Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach. Energies. 2019; 12 (24):4744.

Chicago/Turabian Style

Huichao Ji; JunYou Yang; Haixin Wang; Kun Tian; Martin Onyeka Okoye; Jiawei Feng. 2019. "Electricity Consumption Prediction of Solid Electric Thermal Storage with a Cyber–Physical Approach." Energies 12, no. 24: 4744.

Journal article
Published: 23 October 2019 in Energies
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With the increasing penetration of renewable energy, a weak grid with declining inertia and distorted voltage conditions becomes a significant problem for wind and solar energy integration. Grid frequency is prone to deviate from its nominal value. Grid voltages become more easily polluted by unbalanced and harmonic components. Grid synchronization technique, as a significant method used in wind and solar energy grid-connected converters, can easily become ineffective. As probably the most widespread grid synchronization technique, phase-locked loop (PLL) is required to detect the grid frequency and phase rapidly and precisely even under such undesired conditions. While the amount of filtering techniques can remove disturbances, they also deteriorate the dynamic performance of PLL, which may not meet the standard requirements of grid codes. The objective of this paper is to propose an effective PLL to tackle this challenge. The proposed PLL is based on quasi-type-1 PLL (QT1-PLL), which provides a good filtering capability by using a moving average filter (MAF). To accelerate the transient behavior when disturbance occurs, a modified delay signal cancellation (DSC) operator is proposed and incorporated into the filtering stage of QT1-PLL. By using modified DSCs and MAFs in a cascaded way, the settling time of the proposed method is reduced to around one cycle of grid fundamental frequency without degrading any disturbance rejection capability. To verify the performance, several test cases, which usually happen in high renewable penetrated power systems, are carried out to demonstrate the effectiveness of the proposed PLL.

ACS Style

Li; JunYou Yang; Weichun Ge; Bo Hu; Yang; Ge; Hu; Tie Li; Yunlu Li. A Modified DSC-Based Grid Synchronization Method for a High Renewable Penetrated Power System Under Distorted Voltage Conditions. Energies 2019, 12, 4040 .

AMA Style

Li, JunYou Yang, Weichun Ge, Bo Hu, Yang, Ge, Hu, Tie Li, Yunlu Li. A Modified DSC-Based Grid Synchronization Method for a High Renewable Penetrated Power System Under Distorted Voltage Conditions. Energies. 2019; 12 (21):4040.

Chicago/Turabian Style

Li; JunYou Yang; Weichun Ge; Bo Hu; Yang; Ge; Hu; Tie Li; Yunlu Li. 2019. "A Modified DSC-Based Grid Synchronization Method for a High Renewable Penetrated Power System Under Distorted Voltage Conditions." Energies 12, no. 21: 4040.

Journal article
Published: 26 May 2019 in Energies
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This work develops an indoor carrier robot for people with disabilities, where the precise tracking of designated route is crucial. The parameter uncertainties and disturbances of the robot impose significant challenges for tracking. The present paper first investigates the dynamic of mechanical structure and modeling of actuator motors and constructs a new dynamic model by considering all main parameter uncertainties and disturbances. A novel robust feedback tracking controller considering both the optimization of path tracking and the minimization of the power consumption energy is proposed. It is proved that the tracking errors e and e ˙ satisfy a H∞ performance indicator while the energy consumption is minimum. A simulation example was performed and the results show that this novel algorithm can effectively reduce the tracking error from 0.2 m to 0.006 m while guaranteeing the minimum energy consumption. Furthermore, the effectiveness of the proposed method was validated by experiment compared with the non-robust one.

ACS Style

Yina Wang; Wenqiu Xiong; JunYou Yang; Yinlai Jiang; Shuoyu Wang. A Robust Feedback Path Tracking Control Algorithm for an Indoor Carrier Robot Considering Energy Optimization. Energies 2019, 12, 2010 .

AMA Style

Yina Wang, Wenqiu Xiong, JunYou Yang, Yinlai Jiang, Shuoyu Wang. A Robust Feedback Path Tracking Control Algorithm for an Indoor Carrier Robot Considering Energy Optimization. Energies. 2019; 12 (10):2010.

Chicago/Turabian Style

Yina Wang; Wenqiu Xiong; JunYou Yang; Yinlai Jiang; Shuoyu Wang. 2019. "A Robust Feedback Path Tracking Control Algorithm for an Indoor Carrier Robot Considering Energy Optimization." Energies 12, no. 10: 2010.

Journal article
Published: 01 March 2019 in Energies
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In order to reduce the pollution caused by coal-fired generating units during the heating season, and promote the wind power accommodation, an electrical and thermal system dispatch model based on combined heat and power (CHP) with thermal energy storage (TES) and demand response (DR) is proposed. In this model, the emission cost of CO2, SO2, NOx, and the operation cost of desulfurization and denitrification units is considered as environmental cost, which will increase the proportion of the fuel cost in an economic dispatch model. Meanwhile, the fuel cost of generating units, the operation cost and investment cost of thermal energy storage and electrical energy storage, the incentive cost of DR, and the cost of wind curtailment are comprehensively considered in this dispatch model. Then, on the promise of satisfying the load demand, taking the minimum total cost as an objective function, the power of each unit is optimized by a genetic algorithm. Compared with the traditional dispatch model, in which the environmental cost is not considered, the numerical results show that the daily average emissions CO2, SO2, NOx, are decreased by 14,354.35 kg, 55.5 kg, and 47.15 kg, respectively, and the wind power accommodation is increased by an average of 6.56% in a week.

ACS Style

Weidong Li; Tie Li; Haixin Wang; Jian Dong; Yunlu Li; Dai Cui; Weichun Ge; JunYou Yang; Martin Onyeka Okoye. Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response. Energies 2019, 12, 817 .

AMA Style

Weidong Li, Tie Li, Haixin Wang, Jian Dong, Yunlu Li, Dai Cui, Weichun Ge, JunYou Yang, Martin Onyeka Okoye. Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response. Energies. 2019; 12 (5):817.

Chicago/Turabian Style

Weidong Li; Tie Li; Haixin Wang; Jian Dong; Yunlu Li; Dai Cui; Weichun Ge; JunYou Yang; Martin Onyeka Okoye. 2019. "Optimal Dispatch Model Considering Environmental Cost Based on Combined Heat and Power with Thermal Energy Storage and Demand Response." Energies 12, no. 5: 817.

Journal article
Published: 17 September 2018 in Energies
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In renewable energy generation applications, phase locked loop (PLL) is one of the most popular grid synchronization technique. The main objective of PLL is to rapidly and precisely extract phase and frequency especially when the grid voltage is under non-ideal conditions. This motivates the recent development of moving average filters (MAFs) based PLL in a quasi-type-1 system (i.e., QT1-PLL). Despite its success in certain applications, the transient response is still unsatisfactory, mainly due to the fact that the time delay caused by MAFs is still large. This has significantly limited the utilization of QT1-PLL, according to common grid codes such as German and Spanish grid codes. This challenge has been tackled in this paper. The basic idea is to develop a new hybrid filtering stage, consisting of adaptive notch filters (ANFs) and MAFs, arranged at the inner loop of QT1-PLL. Such an idea can greatly improve the transient response of QT1-PLL, owing to the fact that ANFs are utilized to remove the fundamental frequency negative voltage sequence (FFNS) component while other dominant harmonics can be removed by MAFs with a small time delay. By applying the proposed technique, the settling time is reduced to less than one cycle of grid frequency without any degradation in filtering capability. Moreover, the proposed PLL can be easily expanded to handle dc offset rejection. The effectiveness is validated by comprehensive experiments.

ACS Style

Yunlu Li; JunYou Yang; Haixin Wang; Weichun Ge; Yiming Ma. Leveraging Hybrid Filter for Improving Quasi-Type-1 Phase Locked Loop Targeting Fast Transient Response. Energies 2018, 11, 2472 .

AMA Style

Yunlu Li, JunYou Yang, Haixin Wang, Weichun Ge, Yiming Ma. Leveraging Hybrid Filter for Improving Quasi-Type-1 Phase Locked Loop Targeting Fast Transient Response. Energies. 2018; 11 (9):2472.

Chicago/Turabian Style

Yunlu Li; JunYou Yang; Haixin Wang; Weichun Ge; Yiming Ma. 2018. "Leveraging Hybrid Filter for Improving Quasi-Type-1 Phase Locked Loop Targeting Fast Transient Response." Energies 11, no. 9: 2472.

Journal article
Published: 21 June 2018 in Energies
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Frequency stability in an isolated grid can be easily impacted by sudden load or wind speed changes. Many frequency regulation techniques are utilized to solve this problem. However, there are only few studies designing torque compensation controllers based on power performances in different Speed Parts. It is a major challenge for a wind turbine generator (WTG) to achieve the satisfactory compensation performance in different Speed Parts. To tackle this challenge, this paper proposes a gain scheduled torque compensation strategy for permanent magnet synchronous generator (PMSG) based wind turbines. Our main idea is to improve the anti-disturbance ability for frequency regulation by compensating torque based on WTG speed Parts. To achieve higher power reserve in each Speed Part, an enhanced deloading method of WTG is proposed. We develop a new small-signal dynamic model through analyzing the steady-state performances of deloaded WTG in the whole range of wind speed. Subsequently, H∞ theory is leveraged in designing the gain scheduled torque compensation controller to effectively suppress frequency fluctuation. Moreover, since torque compensation brings about untimely power adjustment in over-rated wind speed condition, the conventional speed reference of pitch control system is improved. Our simulation and experimental results demonstrate that the proposed strategy can significantly improve frequency stability and smoothen power fluctuation resulting from wind speed variations. The minimum of frequency deviation with the proposed strategy is improved by up to 0.16 Hz at over-rated wind speed. Our technique can also improve anti-disturbance ability in frequency domain and achieve power balance.

ACS Style

Haixin Wang; JunYou Yang; Zhe Chen; Weichun Ge; Shiyan Hu; Yiming Ma; Yunlu Li; Guanfeng Zhang; Lijian Yang. Gain Scheduled Torque Compensation of PMSG-Based Wind Turbine for Frequency Regulation in an Isolated Grid. Energies 2018, 11, 1623 .

AMA Style

Haixin Wang, JunYou Yang, Zhe Chen, Weichun Ge, Shiyan Hu, Yiming Ma, Yunlu Li, Guanfeng Zhang, Lijian Yang. Gain Scheduled Torque Compensation of PMSG-Based Wind Turbine for Frequency Regulation in an Isolated Grid. Energies. 2018; 11 (7):1623.

Chicago/Turabian Style

Haixin Wang; JunYou Yang; Zhe Chen; Weichun Ge; Shiyan Hu; Yiming Ma; Yunlu Li; Guanfeng Zhang; Lijian Yang. 2018. "Gain Scheduled Torque Compensation of PMSG-Based Wind Turbine for Frequency Regulation in an Isolated Grid." Energies 11, no. 7: 1623.

Journal article
Published: 18 April 2018 in Energies
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In most grid-connected power converter applications, the phase-locked loop (PLL) is probably the most widespread grid synchronization technique, owing to its simple implementation. However, its phase-tracking performance tends to worsen when the grid voltage is under unbalanced and distorted conditions. Many filtering techniques are utilized to solve this problem, however, at the cost of slowing down the transient response. It is a major challenge for PLL to achieve a satisfactory dynamic performance without degrading its filtering capability. To tackle this challenge, a hybrid filtering technique is proposed in this paper. Our idea is to eliminate the fundamental frequency negative sequence (FFNS) and other harmonic sequences at the prefiltering stage and inner loop of PLL, respectively. Second-order generalized integrators (SOGIs) are used to remove FFNS before the Park transformation. This makes moving average filters (MAFs) eliminate other harmonics with a narrowed window length, which means the time delay that is caused by MAFs is reduced. The entire hybrid filtering technique is included in a quasi-type-1 PLL structure (QT1-PLL), which can provide a rapid dynamic behavior. The small-signal model of the proposed PLL is established. Based on this model, the parameter design guidelines targeting the fast transient response are given. Comprehensive experiments are carried out to confirm the effectiveness of our method. The results show that the settling time of the proposed PLL is less than one grid cycle, which is shorter than most of the widespread PLLs. The harmonic rejection capability is also better than other methods, under both nominal and adverse grid conditions.

ACS Style

Yunlu Li; JunYou Yang; Haixin Wang; Weichun Ge; Yiming Ma. A Hybrid Filtering Technique-Based PLL Targeting Fast and Robust Tracking Performance under Distorted Grid Conditions. Energies 2018, 11, 973 .

AMA Style

Yunlu Li, JunYou Yang, Haixin Wang, Weichun Ge, Yiming Ma. A Hybrid Filtering Technique-Based PLL Targeting Fast and Robust Tracking Performance under Distorted Grid Conditions. Energies. 2018; 11 (4):973.

Chicago/Turabian Style

Yunlu Li; JunYou Yang; Haixin Wang; Weichun Ge; Yiming Ma. 2018. "A Hybrid Filtering Technique-Based PLL Targeting Fast and Robust Tracking Performance under Distorted Grid Conditions." Energies 11, no. 4: 973.