Table of Content

28 February 2025, Volume 59 Issue 2 Previous Issue    Next Issue

New Type Power System and the Integrated Energy

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Frequency-Domain Modeling and Synchronization Perspective Interaction Mechanism of GFL-GFM Converter System ZONG Haoxiang, ZHANG Chen, BAO Yanhong, WU Feng, CAI Xu 2025, 59 (2):  151-164.  doi: 10.16183/j.cnki.jsjtu.2023.231 Abstract ( 322 )   HTML ( 15 )   PDF (4555KB) ( 719 )   Save Aimed at the small-signal synchronization instability of grid-following (GFL) and grid-forming (GFM) converter system, a synchronization perspective frequency-domain modeling and analysis method is proposed, which can intuitively reveal mechanism and accurately judge multi-machine stability. Specifically, a node admittance matrix considering GFL, GFM converters, and the transmission network is established. Then, the frequency domain modal analysis (FMA) method is adopted to evaluate system instability characteristics. Afterwards, synchronization forward and feedback paths are partitioned at the oscillation source to formulate a synchronization perspective stability model incorporating dynamics of each converter and transmission network. Finally, the proposed method is validated by using a typical two-machine GFL-GFM system. With such method, the stability judgment failure caused by the feedback path aggregation is addressed, and the interaction mechanism between GFL and GFM synchronization dynamics as well as their parameter influences are revealed. Figures and Tables | References | Related Articles | Metrics

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Robustly Coordinated Operation for Flexible Resources in Low-Carbon Park with High Penetration of Wind Power MENG Yu, GUO Rui, SHI Zichuan, XUE Junyi, LÜ Jiawen, FAN Feilong 2025, 59 (2):  165-174.  doi: 10.16183/j.cnki.jsjtu.2023.262 Abstract ( 176 )   HTML ( 8 )   PDF (2164KB) ( 167 )   Save In order to meet the challenges of operational reliability and economy in the low-carbon park caused by the uncertainty of wind power output, a multi-timescale robustly coordinated operation scheme is proposed to minimize the integrated electricity-carbon operation cost in the low-carbon park, where the diverse regulation capabilities of the flexible resources, such as hydrogen, natural gas, and electrochemical energy storages are fully utilized. The first stage of the proposed operation scheme is the day-ahead decision stage, which considers the multi-timescale fluctuation of wind power output and load demand, and formulates the robust scheduling commands for hydrogen, natural gas, and electrochemical energy storage. The second stage is the intra-day decision stage, which combines the short-term forecast results of wind power output and load demand to dynamically adjust the wind turbine power scheduling commands. The day-ahead decision is an adaptive robust optimization problem, which is solved by the column-and-constraint generation (C&CG) algorithm, while the intra-day decision is a deterministic optimization problem, which is solved by the linear programming algorithm. Finally, this paper proposes an operation simulation model to validate the effectiveness of the operation scheme by combining the hourly and daily operation data of wind power and the load demand in a practical low-carbon park. The simulation results show that the proposed method can effectively improve the operational economy and reliability of the low carbon park with a high penetration of wind power. Figures and Tables | References | Related Articles | Metrics

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Multi-Objective Robustness of Integrated Energy System Considering Source-Load Uncertainty LI Jianlin, ZHANG Zedong, LIANG Ce, ZENG Fei 2025, 59 (2):  175-185.  doi: 10.16183/j.cnki.jsjtu.2023.238 Abstract ( 287 )   HTML ( 6 )   PDF (4800KB) ( 500 )   Save Under the construction of a new power system, the integrated energy system with electricity-thermal-hydrogen interconnection will become one of the important development directions, but its whole life cycle operation economy and energy supply reliability are affected by the initial equipment capacity and daily operation scheme of the system. Therefore, a multi-objective two-stage robust optimization method is proposed, taking into account the source-load uncertainty. A grid-connected electric-thermal-hydrogen operation model including fuel cells and electrolytic cells is developed, and the source-load uncertainty is added by using the hierarchical Latin hypercube sampling and Euclidean distance scenario reduction methods, and solved by a two-stage robust optimization algorithm. The results show that the proposed method can effectively mitigate the impact of source-load uncertainty on the configuration and operation planning of the integrated energy system, which is expected to provide new ideas for the construction and operation of integrated energy systems in the future. Figures and Tables | References | Related Articles | Metrics

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Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs PENG Chaoyi, CHEN Wenzhe, XU Suyue, LI Jianshe, ZHOU Huafeng, GU Huijie, NIE Yongquan, SUN Haishun 2025, 59 (2):  186-199.  doi: 10.16183/j.cnki.jsjtu.2023.240 Abstract ( 226 )   HTML ( 4 )   PDF (3279KB) ( 541 )   Save Due to its load time shifting and power regulation capabilities, virtual power plants (VPPs) have the potential to participate in the electricity market and provide flexible ramping products (FRPs). However, it is hard for VPPs to make accurate bidding in the market, due to the uncertainty of their dispatching capability and system requirements. Therefore, a cloud-edge collaborated market architecture supporting VPPs participation in the electricity market and providing FRPs services is proposed, and the corresponding distributed optimization trading model is established. The market clearing process is completed through the collaborative interaction between the independent system operator and VPPs, which can accurately guide VPPs to optimize the electricity consumption and provide flexible climbing services. The distributed optimization model is iteratively solved using the analytical target cascading (ATC) method, and heuristic constraints are introduced to improve the convergence of the algorithm. Finally, the proposed method is evaluated by the simulation results of typical cases featuring the “duck-curve” net load, which demonstrate that the cloud-edge collaborated market can effectively reduce operating costs and promote the consumption of renewable energy. Figures and Tables | References | Supplementary Material | Related Articles | Metrics

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Charging and Discharging Scheduling Mechanism of Electric Vehicles in Park Based on User Credit Index SUN Xin, JIANG Hailin, XIE Jingdong, WANG Simin, WANG Sen 2025, 59 (2):  200-207.  doi: 10.16183/j.cnki.jsjtu.2023.196 Abstract ( 150 )   HTML ( 5 )   PDF (1363KB) ( 293 )   Save The default behavior of electric vehicle (EV) users to end charging in advance is easy to cause default losses to load aggregators and users themselves. Therefore,it is crucial to dispatch charging behavior rationally considering default behavior. This paper quantifies EV user credit based on Wu’s three-dimensional credit theory, and revises EV charging and discharging plan in the dimensions of discharge depth and charging priority. In addition, it establishes a multi-objective optimization model considering the load fluctuation of regional distribution network and the user cost. Using a park as an example for simulation, it compares and analyzes the effects of different user default rates and EV permeability on the proposed multi-dimensional modified charging and discharging strategy. The results show that the charging and discharging scheduling mechanism based on the user credit index has advantages in calming the load fluctuation of the distribution network in the park and improving the charging experience of users. Compared with the orderly charging and discharging strategy which does not consider the charging and discharging execution of users, the proposed mechanism can better adapt to the scenario of increasing penetration rate of EV and expanding default scale. Figures and Tables | References | Related Articles | Metrics

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Spot Market Clearing and Pricing Model of Regional Power Grid Considering Transmission Cost of Inter-Provincial Power Transactions GU Huijie, DONG Cheng, HE Xiqi, HU Rong, ZHANG Honglue, WEN Zhaoxin 2025, 59 (2):  208-220.  doi: 10.16183/j.cnki.jsjtu.2023.295 Abstract ( 196 )   HTML ( 7 )   PDF (4052KB) ( 565 )   Save In the regional power spot market, inter-provincial transmission power costs are typically calculated by multiplying the transmission price of the physical tie-line channel by the transmission power. However, this method fails to effectively account for the inter-provincial power transactions at different transmission prices. To address this issue, this paper first analyzes the impact of inter-provincial power trading on regional power spot clearing. It then proposes an optinization mechanism for inter-provincial power trading network loss handling, transmission cost processing, tie-line channel flow matching, and point-to-network power trading alignment. Based on this, it proposes a regional power grid spot clearing and pricing model, which incorporates inter-provincial power trading transmission costs into a standardized regional power spot clearing model. It derives the mathematical relationship between the system marginal prices of different provincial power grids. The proposed clearing and pricing model aims to achieve optimal allocation of resources, while effectively stimulating market players to bid reasonably. Finally, it validates the correctness and effectiveness of the proposed model through specific numerical examples. Figures and Tables | References | Supplementary Material | Related Articles | Metrics

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A Strategy for Smoothing Power Fluctuations of New Energy Based on Improved Power Prediction Accuracy and Market Transaction LIANG Yiheng, YANG Dongmei, LIU Gang, YE Wenjie, YANG Yize, QIAN Tao, HU Qinran 2025, 59 (2):  221-229.  doi: 10.16183/j.cnki.jsjtu.2023.312 Abstract ( 134 )   HTML ( 6 )   PDF (3161KB) ( 22 )   Save The uncertainty of new energy results in power prediction errors, causing new energy producers to bear high wind curtailment losses and deviation penalties due to bidding deviations. To address these issues, this paper proposes a feature-constrained multi-layer perception (MLP) power prediction algorithm, combined with storage-based bilateral transactions, to provide power support and reduce bidding deviations. First, the MLP model is enhanced by improving the relevancy of the hidden layers through adaptive learning, which strengthens its ability to capture nonlinear rules in input data and improves power prediction accuracy. Then, the algorithm allows for bilateral transactions between new energy producers and storage enterprise before entering the day-ahead market, helping mitigate the penalties associated with prediction errors including deviation and curtailment costs. Finally, the case study demonstrates that the feature-constrained MLP effectively improves the power prediction accuracy. Additionally, engaging in bilateral transactions with storage enterprise significantly reduces the costs incurred by new energy producers due to bid deviations. Figures and Tables | References | Related Articles | Metrics

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Arc Suppression and Fault Detection Method for Distribution Network Faults Based on Line Voltage Feeding XU Honghua, LIU Baowen, XU Ziqiang, LIU Delin, YU Baoqi 2025, 59 (2):  230-241.  doi: 10.16183/j.cnki.jsjtu.2023.216 Abstract ( 123 )   HTML ( 4 )   PDF (1871KB) ( 24 )   Save In order to improve the reliability and convenience of the voltage-reduction arc suppression device, a zero-sequence voltage flexible control method based on line voltage series modulation damping grounding is proposed, and its application in arc suppression and feeder protection is studied. First, based on the advantage that the line voltage of the distribution network is not affected by single-phase grounding fault, an active grounding device replacement scheme using isolation transformer, arc suppression coil, and other traditional passive devices is proposed, which has the advantage of flexibly regulating the amplitude and phase of zero sequence voltage. Then, the impact of the asymmetric distribution parameters on the active arc suppression is demonstrated, and an arc suppression control method based on full compensation of fundamental current is proposed. Furthermore, a fault phase voltage active lifting method with line voltage access is proposed to effectively improve the detection sensitivity of high resistance faults. Finally, the optimal selection scheme of the accessed line voltage and modulation damping is analyzed, and the integrated control scheme of fault arc suppression and feeder protection in distribution network is given, whose effectiveness is validated by simulation experiment. In practical application, the equipment cost can be reduced only by connecting the selected line voltage to the neutral point through the isolation transformer and cooperating with the resistive modulation damping, which improves the reliability and control convenience of equipment, and provides a new scheme for the grounding fault feeder protection and zero residual current fault arc elimination of the distribution network. Figures and Tables | References | Supplementary Material | Related Articles | Metrics

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Circulating Current Suppression Strategy of Modular Multilevel Matrix Converter Based on Quasi-Proportional Resonant Control JIANG Yafeng, LI Yibo, WU Qiuwei, LIU Shenquan, WU Xiaodan, ZHOU Qian 2025, 59 (2):  242-251.  doi: 10.16183/j.cnki.jsjtu.2023.193 Abstract ( 117 )   HTML ( 2 )   PDF (2259KB) ( 11 )   Save Modular multilevel matrix converter (M3C) is the core device of the fractional frequency transmission system. Due to the lack of direct current link, the electrical quantities of different frequencies are directly coupled inside the M3C, resulting in a complex harmonic condition, which makes it difficult to balance the capacitor voltage of the sub-module and affects the stable operation of the M3C. In this paper, a closed-loop control strategy based on quasi-proportional resonance (PR) controller is proposed to suppress the internal circulation of the M3C. First, the ripple voltage of the sub-module capacitor in steady state is studied. The mechanism of harmonic current generated by the coupling of sub-module ripple voltage and switching function is analyzed in detail. The analytical formula of harmonic circulating current is derived, and the necessity of suppressing the circulating current is pointed out. Then, a parallel quasi-PR circulation suppressor is designed for the harmonic circulation of four frequency components, which reduces the distortion of the bridge arm current while suppressing the circulating current, ensures the capacitor voltage balance between the bridge arms with a good dynamic performance. Finally, a simulation model is built in MATLAB / Simulink and the effectiveness of the control strategy is validated by simulation. Figures and Tables | References | Related Articles | Metrics

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Comprehensive Power Quality Management for Transformerless Unified Power Quality Conditioner Under Multiple Operating Conditions FU Zhe, ZHANG Qi, LIU Yang, WAN Bohao, WANG Ting, SUN Yanfei 2025, 59 (2):  252-265.  doi: 10.16183/j.cnki.jsjtu.2023.281 Abstract ( 183 )   HTML ( 5 )   PDF (4882KB) ( 405 )   Save To address the power quality problems caused by power electronic loads, a two-leg topology-based transformerless unified power quality conditioner (TLTT-UPQC) is proposed, which can overcome the volume, weight, and magnetic saturation of power frequency transformer in the existing UPQCs, and can improve the power quality of the distribution network in a lightweight and highly flexible form to meet the high-quality electricity demand of loads. First, the working principle of TLTT-UPQC is analyzed from the perspective of circuit topology. Then, combined with theoretical analysis, the operating mechanisms of various power quality management functions are studied in scenarios such as grid voltage drop, rise and distortion, as well as resistor-inductance and nonlinear loads, based on which, the system control strategy is designed. Finally, a MATLAB-based simulation model is developed to verify the multifunctional operation performances of the TLTT-UPQC through simulation results. Figures and Tables | References | Related Articles | Metrics

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Short-Term Telephone-Traffic Prediction of Power Grid Customer Service Based on Adaboost-CNN QIN Hao, SU Liwei, WU Guangbin, JIANG Chongying, XU Zhipeng, KANG Feng, TAN Huochao, ZHANG Yongjun 2025, 59 (2):  266-273.  doi: 10.16183/j.cnki.jsjtu.2023.383 Abstract ( 161 )   HTML ( 4 )   PDF (1827KB) ( 299 )   Save The introduction of modern power supply service system has raised higher requirements for the service quality of electricity customer service. Accurate power supply service traffic prediction not only improves the quality of power customer service, but also effectively reduces the cost of customer service personnel. Therefore, this paper proposes a short-term traffic prediction method for power grid based on Adaboost and convolutional neural network (Adaboost-CNN) and a value-added service correction method. First, the isolated forest algorithm is used to identify the abnormal data, and the Lagrange interpolation function is applied to repair the abnormal data or missing data. Next, the analytic hierarchy process is employed to quantify user information, meteorological data, and power outage details. The grey correlation method is then used to analyze the influence factors of traffic volume, and these factors are incorporated as inputs to the traffic volume prediction model. An Adaboost algorithm is applied to integrate multiple CNN models, resulting in an Adaboost-CNN traffic prediction model. Finally, considering the value-added services within the power supply service system, the prediction results of the model are corrected to obtain the final traffic prediction value. The case analysis shows that the proposed forecasting model reduces prediction error by an average of 11.05 percentage points compared to a single forecasting model and by 5.32 percentage points compared to a combined forecasting model, demonstrating better forecasting accuracy. Figures and Tables | References | Related Articles | Metrics

Electronic Information and Electrical Engineering

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Landing State Recognition of Carrier-Based Aircraft Based on Adaptive Feature Enhancement and Fusion WANG Ke, LIU Yiyang, YANG Jie, LU Aiguo, LI Zhe, XU Mingliang 2025, 59 (2):  274-282.  doi: 10.16183/j.cnki.jsjtu.2023.263 Abstract ( 79 )   HTML ( 7 )   PDF (20611KB) ( 19 )   Save The recognition of engagement state aids landing signal officers in formulating command decisions promptly and precisely, which is crucial for guiding carrier-based aircraft landings. A method is proposed for recognizing the engagement state, leveraging adaptive feature enhancement and fusion, which includes an attention mechanism-based feature enhancement module and a multi-scale feature fusion module. The front module enhances visual representation by segmenting feature maps and concatenating spatial and channel domains, and the back module merges high-resolution shallow features with semantically rich deep features to fully utilize contextual information. A prototype system is developed to recognize landing engagement states based on the wearable augmented reality devices. To evaluate the performance of the method proposed, hybrid datasets of landing operations are constructed. The results show that the proposed algorithm outperforms baseline algorithms and meets the application requirements of engagement state recognition. Figures and Tables | References | Related Articles | Metrics

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Unsupervised Fabric Defect Detection Based on Under-Complete Dictionary Reconstruction LIU Jianxin, PAN Ruru, ZHOU Jian 2025, 59 (2):  283-292.  doi: 10.16183/j.cnki.jsjtu.2023.205 Abstract ( 169 )   HTML ( 5 )   PDF (13784KB) ( 228 )   Save To address the issue that most current automatic fabric defect detection methods still require manual selection of training sets and cannot achieve unsupervised learning, an automatic unsupervised defect detection method using the median robust extended local binary pattern (MRELBP) feature for flawless image screening and an under-complete dictionary reconstruction method for defect point detection are proposed. An adaptive dictionary size search algorithm is also proposed to automatically select a suitable dictionary size. First, the algorithm selects the flawless images from fabric images. Then, K-SVD is applied to obtain an under-complete dictionary from the normal image blocks as the training set. Finally, the reconstruction-base scheme is used to identify defects with the structural similarity index measure (SSIM) threshold. Experimental results of 334 plain fabrics with warp, weft, and block defects show that compared to the K-SVD method that uses residual segmentation for defect detection, the proposed method increases the correct detection rate up to 21.81%, reduces the false detection rate up to 0.72%, and a 50% increase in detection speed per image on average. The proposed algorithm achieved an average correct detection rate of 83.29% on the AITEX dataset, demonstrating its effectiveness. Figures and Tables | References | Related Articles | Metrics