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Partial Discharge Fault Diagnosis in power Transformer Based on Symplectic Geometry Mode Decomposition and Optimized Convolutional Neural Networks

SSRN

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SSRN 2023年

作者： Shang, Haikun Liu, Zhidong Huang, Tao Zhang, Ranzhe Li, Feng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Electric Power Research Institute Xinjiang830011 China

To address challenges in feature extraction, such as difficulty in accurately extracting valuable information and low recognition accuracy during pattern recognition, this paper proposes a novel method for power transformer PD fault diagnosis based on symplectic geometry mode decomposition (SGMD) and optimized Convolutional Neural Networks (CNN). First, three different types of power transformer PD fault signals are extracted, namely floating discharge (FD), corona discharge (CD), and bubble discharge (BD), in a laboratory environment. Second, PD signals are decomposed using SGMD to obtain different symplectic geometric components (SGCs). Afterwards, approximate entropy (ApEn) values of each SGC are calculated as PD feature parameters. Subsequently, the sparrow search algorithm (SSA) algorithm is utilized to optimize and configure the CNN parameters to enhance the diagnostic accuracy and stability. Finally, the obtained feature parameters are fed into the optimized CNN for fault diagnosis. simulation and experimental analysis verify the effectiveness of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Mode decomposition

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Long-Foresight-Term power Prediction Considering the Evolution of Significant Weather Information for Wind Farm Cluster

SSRN

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SSRN 2023年

作者： Yang, Mao Jiang, Renxian Zhang, Xiaobin Huang, Yutong Guo, Yunfeng Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Gansu Electric Power Company Gansu730030 China

The current wind power prediction horizon extends up to 7 days, and the research on power prediction for the 8 to 15 day-ahead is still lacking. However, with the increasing proportion of renewable energy in novel power systems, breaking through the long-foresight-term power prediction barrier is crucial support for guaranteeing the secure and stable operation of the power system. To address the aforementioned issues, this study presents a power prediction method for wind farm cluster (WFC) in long-foresight-term, based on the poor availability of existing numerical weather prediction (NWP) in long-foresight-term. Firstly, a significant wind process identification method considering autocorrelation analysis is employed to predict future power trends, and an optimal method is explored for power generation (PG) prediction in the 8 to 15 day-ahead. Secondly, under the constraint of predicted PG, the power corresponding to the historical similar wind process is selected as the predicted value. Finally, the proposed methodology is applied to a WFC in Gansu Province, China. The results indicate that the proposed method reduces the average RMSE and MAE by 4.15% and 3.3%, respectively, effectively tracking the changing trends of wind power. Moreover, it achieves long-foresight-term prediction and enhances the reliability of the prediction. © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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PreAdaptFWI: Pretrained-Based Adaptive Residual Learning for Full-Waveform Inversion Without Dataset Dependency

arXiv

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arXiv 2025年

作者： Dong, Xintong Yuan, Zhengyi Lin, Jun Dong, Shiqi Tong, Xunqian Li, Yue College of Instrumentation and Electrical Engineering Jilin University Changchun130026 China Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology Northeast Electric Power University Jilin132012 China College of Communication Engineering Jilin University Changchun130012 China

Full-waveform inversion (FWI) is a method that utilizes seismic data to invert the physical parameters of subsurface media by minimizing the difference between simulated and observed waveforms. Due to its ill-posed nature, FWI is susceptible to getting trapped in local minima. Consequently, various research efforts have attempted to combine neural networks with FWI to stabilize the inversion process. This study presents a simple yet effective training framework that is independent of dataset reliance and requires only moderate pre-training on a simple initial model to stabilize network outputs. During the transfer learning phase, the conventional FWI gradients will simultaneously update both the neural network and the proposed adaptive residual learning module, which learns the residual mapping of large-scale distribution features in the network’s output, rather than directly fitting the target mapping. Through this synergistic training paradigm, the proposed algorithm effectively infers the physically-informed prior knowledge into a global representation of stratigraphic distribution, as well as capturing subtle variations in inter-layer velocities within local details, thereby escaping local optima. Evaluating the method on two benchmark models under various conditions, including absent low-frequency data, noise interference, and differing initial models, along with corresponding ablation experiments, consistently demonstrates the superiority of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Stratigraphy

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Exergic Perspective of Low Carbon Economy Dispatch of Carbon Capture power Plant and power to Gas Synergistic Operation

SSRN

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SSRN 2023年

作者： Wang, Xiuyun Zhang, Wanyu Wang, Rutian Wen, Xiangyun Cui, Benwang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China State Grid Tianjin Electric Power Company Tianjin300010 China

In order to improve the system economy and low carbon, the power to gas-carbon capture power plants joint operation system is introduced in the integrated energy system to reduce carbon emissions while increasing the consumption of renewable energy. From the perspective of exergy, both the quantity and quality of energy were taken into account, the calculation methods of different exergy values of energy flow were refined, and the sub-model of exergy flow calculation was established. To further achieve low carbon emission reduction, the costs of carbon emission trading, carbon capture energy consumption and carbon storage were included in the non-energy costs, and the exergoeconomic cost distribution model was established. Compared with the traditional pricing method, this paper considers the impact of carbon cost and energy grade difference on unit exergic cost of cooling, heating and electricity in the system, reflecting the principle of higher quality and higher price. Finally, the constructed model is solved with the total cost as the objective function. The simulation example shows that the proposed scheduling method can improve the low-carbon emission reduction capacity of the system and achieve reasonable cost distribution. © 2023, The Authors. All rights reserved.

关键词： Carbon capture

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A Data-driven Method for Transient Stability Margin Prediction Based on Security Region

引用

Journal of modern power systems and Clean energy 2020年第6期8卷 1060-1069页

作者： Jun An Jiachen Yu Zonghan Li Yibo Zhou Gang Mu the Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Northeast Electric Power University) Ministry of EducationJilin 132012China China Electric Power Research Institute Beijing 100192China

Transient stability assessment(TSA)based on security region is of great significance to the security of power *** this paper,we propose a novel methodology for the assessment of online transient stability *** with a geographic information system(GIS)and transformation rules,the topology information and pre-fault power flow characteristics can be extracted by 2 D computer-vision-based power flow images(CVPFIs).Then,a convolutional neural network(CNN)-based comprehensive network is constructed to map the relationship between the steady-state power flow and the generator stability indices under the anticipated contingency *** network consists of two components:the classification network classifies the input samples into the credibly stable/unstable and uncertain categories,and the prediction network is utilized to further predict the generator stability indices of the categorized samples,which improves the network ability to distinguish between the samples with similar *** proposed methodology can be used to quickly and quantitatively evaluate the transient stability margin of a power system,and the simulation results validate the effectiveness of the method.

关键词： Security region computer-vision-based power flow image(CVPFI) transient stability margin convolutional neural network(CNN) comprehensive network

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Design of Planar Ultra-wideband Notch Antenna 19

Design of Planar Ultra-wideband Notch Antenna

引用

19th International Conference on Optical Communications and Networks, ICOCN 2021

作者： Zheng, Xuemei Xu, Xiuming Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Northeast Electric Power University Jilin China College of Information & Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781665424462

Ultra-wideband technology determines the development trend of the future society. As the core technology of ultra-wideband technology, ultra-wideband antennas are the most important structure in the radio system. When it is working, it will generate electromagnetic waves in free space and be converted into radio waves. There are many narrow-band frequency bands in the ultra-wideband frequency range (3.1GHz-10.6GHz). Use possible ways to make the antenna filter out other narrow-band interference in the working frequency band to realize the notch function. © 2021 IEEE.

关键词： Ultra-wideband (UWB)

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Resilience assessment and improvement for electric power transmission systems against typhoon disasters: A data-model hybrid driven approach

arXiv

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arXiv 2022年

作者： Yang, Rui Li, Yang Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education [Northeast Electric Power University Jilin132012 China

In response to the damage to electric power transmission systems caused by typhoon disasters in coastal areas, a planning-targeted resilience assessment framework that considers the impact of multiple factors is established to accurately find the weak links of the transmission system and improve the system resilience. Firstly, this paper constructs the attenuation model of the wind field and the comprehensive failure model of the system, in which the model drive to establish the cumulative failure model of the transmission system is adopted, and multiple data-driven schemes to correct the cumulative failure rate by relying on the feature factor information of the transmission system is adopted. At the same time, an analytic hierarchy process-weighted arithmetic averaging (AHP-WAA) method is introduced to select the optimal data-driven evaluation scheme. Secondly, this paper adopts the impact-increment-based state enumeration (IISE) method to establish resilience indicators for systems and corridors separately. On this basis, the optimal promotion strategy is selected according to the construction difficulty, resilience improvement ability, and cost analysis. Finally, simulations on the IEEE RTS-79 system have been carried out considering the influence of the real typhoon scenarios, micro-topographic and transmission corridor information factors. The results demonstrate that the hybrid-driven system resilience assessment and improvement method can assist planners in accurately judging the resilience level of the system against typhoon disasters and selecting the best resilience improvement strategy based on the cost-effectiveness ratio. Copyright © 2022, The Authors. All rights reserved.

关键词： Analytic hierarchy process

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An Improved Virtual Impedance Droop control of Three-Port Converter Interfaced Hybrid energy Storage systems

An Improved Virtual Impedance Droop Control of Three-Port Co...

引用

International Conference on power Electronics systems and Applications (ICPESA)

作者： Sisi Liang Xiaolin Wang Yuanmao Ye Yao Ma School of Automation Guangdong University of Technology Guangzhou China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education (Northeast Electric Power University) Jilin China Hainan Electric Power School Hainan Power Grid Co. Ltd. Haikou China

ISBN: (数字)9798350365801

ISBN: (纸本)9798350365818

The power allocation of hybrid energy storage system (HESS) is crucial for the development of hydrogen fuel cell vehicles (HFCVs). To improve the operational efficiency, a triple active bridge (TAB) converter is designed as the interface circuit, which can transfer power bi-directionally and isolate the voltage in a wide range of transient conditions. This paper proposes a droop control strategy based on adjustable virtual impedance for HESS. The fuel cell (FC) side converter adopts the virtual resistance and inductance droop (VRID) control, while the Lithium battery side converter uses the virtual capacitance droop (VCD) control. The Lithium battery can quickly absorb the high-frequency component of the system power fluctuation, which improves the dynamic response of the system. Moreover, the value of the virtual resistance is adjusted according to the operating state of the circuit, which helps to enhance the power quality and eliminate the voltage deviations caused by conventional droop control. The proposed control strategy and analytical results are validated by both simulations and experiments.

关键词： Resistance Analytical models Fluctuations Fuel cells Lithium batteries Hybrid power systems Impedance

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Advanced Out-of-Step Splitting Protection simulation Platform with Considering High Frequency Generation Tripping and Low Frequency Load Shedding

Advanced Out-of-Step Splitting Protection Simulation Platfor...

引用

2022 IEEE International Conference on power systems and Electrical technology, PSET 2022

作者： Liu, Ren Zhu, Yukun Zhu, Yihua Chang, Dongxu Wu, Mingkang Huang, Libin a. State Key Laboratory of HVDC b. National Energy Power Grid Technology R&D Centre c. Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System d. CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9781665489157

This paper presents an advanced out-of-step (OOS) splitting protection simulation platform considering high frequency generation tripping and low frequency load shedding. In the proposed simulation platform, Real-Time Digital Simulator (RTDS) is utilized as the real-time power system simulator. Through the high-speed I/O card, real industry OOS splitting protection device is integrated into the proposed simulation platform. Based on the real China South power Grid (CSG) control strategy, high frequency generation tripping and low frequency load shedding components are modelled in the simulation. CSG real grid is modelled in the RTDS to perform the Real-time hardware-in-the-loop simulation. simulation results present that there is a clear difference between with high frequency generation tripping results and without high frequency generation tripping results. © 2022 IEEE.

关键词： simulation platform

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SECONDARY FREQUENCY control OF ISLANDED MICROGRID CONSIDERING WIND AND SOLAR STOCHASTICS

arXiv

引用

arXiv 2023年

作者： Zhong, Cheng Jiang, Zhifu Zhang, Xiangyu Chen, Jikai Li, Yang Key Laboratory of Modern Power System Simulation and Control &Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China Northeast Electric Power University College of Electrical Engineering Jilin132012 China

This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real- time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients，wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming (QP) optimal problem with variable constraints. Finally，a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reduce the frequency deviation，especially in severe photovoltaic and wind fluctuations scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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