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2024 2nd International Conference on Power, Grid and Energy Storage, PGES 2024

作者： Yang, Hang Yang, Runkai Li, Xiang Zhou, Wenyuan School of Control and Computer Engineering North China Electric Power University Beijing China

Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Energy utilization

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Equivalent modeling and simulation of cascading dynamics via neural network algorithm for A Microgrid Network 8

Equivalent modeling and simulation of cascading dynamics via...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Chen, Xingyuan Zhao, Jingwei Hu, Yang Yang, Han Shi, Zhentao Chen, Zuo North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331523558

Due to the complex structure of microgrids, there is currently a lack of an orderly modeling framework. This article proposes a dynamic equivalent modeling method for microgrids based on cascaded neural network models. Clustering the physical network of microgrids based on prior knowledge, and proposing a dynamic equivalent modeling method for each group using neural networks. The effectiveness of the method is verified through simulation using a 10-node microgrid clusters as the object. © 2024 IEEE.

关键词： Neural network models

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Electric and Heat Load Forecasting of Integrated Energy System 4

Electric and Heat Load Forecasting of Integrated Energy Syst...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Hou, Guolian Guo, Zhiqiang Zhang, Tianhao North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350349252

Accurate load forecasting is essential for optimizing the operation and energy management of integrated energy system (IES). The accuracy of load forecasting depends on both historical data and prediction methods. To address the limitations of traditional load forecasting methods such as inaccurate forecasting and single load, this study proposes a novel approach that combined digital twin (DT) with Transformer for accurate multiple loads forecasting of IES. Next, a virtual representation of the IES is constructed using DT to provide accurate data for load forecasting. The data is then processed and fed into the Transformer model for training, resulted in models with high forecasting performance. Lastly, simulations are used to assess the performance of the proposed model on a park level IES. The results indicate that the proposed model demonstrates high accuracy and can closely track the changes in actual load values. © 2024 IEEE.

关键词： Digital twin Integrated energy system Load forecasting Transformer

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A Dynamic Adaptive Boiler Efficiency Prediction Model Based on VMD-ILSSVM-LSTM 8

A Dynamic Adaptive Boiler Efficiency Prediction Model Based ...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Lin, Jianjie Sun, Yujian Zhang, Yi North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331523558

Accurate prediction of boiler efficiency is critical for the optimal operation of power plant combustion systems. This study presents a hybrid prediction model of boiler efficiency combining Variational Mode Decomposition (VMD), improved Least Squares Support Vector Machine (ILSSVM), and Long Short-Term Memory (LSTM) network. Initially, VMD is used to decompose and reconstruct the boiler efficiency sequence, effectively capturing both high- and low-frequency features. The LSTM network is then leveraged to model the low-frequency characteristics of the data, while the LSSVM is utilized for the accurate prediction of high-frequency components. In cases where the prediction error exceeds the predefined threshold, model update strategy is conducted by replacing, adding, or deleting LSSVM samples and updating corresponding model parameters. Finally, the low- and high-frequency components predicted by LSTM and improved LSSVM are linearly combined to form the final prediction result. The simulation results demonstrate that the proposed dynamic adaptive VMD-ILSSVM-LSTM model significantly improves prediction accuracy with the lowest MAE and RMSE values. © 2024 IEEE.

关键词： Variational mode decomposition

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Parameter Sensitivity Analysis of Co-Decisions

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International Journal of Crowd Science 2022年第2期6卷 63-74页

作者： Li, Li Sun, Hongbo Yao, Xia School of Computer and Control Engineering Yantai University Yantai264005 China

The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life. © The author(s) 2022.

关键词： Decision making

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Model Reference Adaptive control and Neural Network-Based Concentration control of CSTR 7

Model Reference Adaptive Control and Neural Network-Based Co...

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Haroon, Husam E. Elfadel North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350339161

The paper discusses the concentration control in Continuous Stirred Tank Reactors (CSTR) using another CSTR as a reference model. The PI controller regulates the reference model, which adjusts the model reference adaptive controller (MRAC). The neural controller is then tuned with the model identified from the second CSTR. The controllers' performance is assessed through the utilization of MATLAB/SIMULINK aiming to illustrate and enhance the system's concentration and stability. The simulation results are compared with the Model Reference Adaptive controller MIT and Lyapunov rules. © 2024 IEEE.

关键词： Model reference adaptive control

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Out-of-Distribution Robustness Forecasting for Offshore Wind Power via Matching Based Transformer 22

Out-of-Distribution Robustness Forecasting for Offshore Wind...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Lu, Jianmou Chen, Yuanye Fang, Fang Xiao, Feng Liu, Zhijie School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

Due to extreme external environments and changes in component health status, offshore wind turbines can generate out-of-distribution (OOD) data compared to normal operation. Traditional wind power forecasting (WPF) models are typically trained using historical data. Nevertheless, these models fail to maintain robustness with respect to OOD data. This paper introduces the matching based Transformer (MatTF) to mine shared knowledge from data across different distributions, thereby addressing the OOD problem. First, an algorithm for the detection of distribution boundaries is designed to partition the training data into subsets. Then, adaptive factors are added to the self-attention module in traditional transformer to solve the distribution matching problem. Finally, the effectiveness of the model is verified through case study comparing with other WPF models. © 2024 IEEE.

关键词： Wind forecasting

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control Interval Reconstruction of Floating Wind Turbine Based on Response Characteristics 22

Control Interval Reconstruction of Floating Wind Turbine Bas...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhou, Jingfeng Song, Ziqiu Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

The oscillatory movement, aerodynamic damping effect, gyroscopic effect, and other phenomena in floating wind turbines result in distinct response characteristics of output and load under varying wind-wave coupling scenarios. Designing controllers within traditional control intervals using wind speed or wind-wave metrics is insufficient for fundamentally improving the wind turbine’s performance. Firstly, according to the response distribution of the floating wind turbine, the response characteristics in different scenarios are extracted based on the autoencoder. And the response characteristics are clustered to form a control interval with response as the measurement index, which provides a basis for the partitioning in the construction of digital twin models for floating wind turbines. Secondly, within these reconstructed control intervals, control parameters are optimized based on the specific efficiency improvement and load reduction requirements of each scenario. This process leads to the development of a variable parameter controller. Finally, simulation experiments using FAST in various wind-wave coupling scenarios demonstrate that the proposed variable parameter controller effectively stabilizes power output and reduces loads. © 2024 IEEE.

关键词： Wind turbines

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Resilient control Strategies for Multi-Area Interconnected Power Systems: Combating External FDI Attacks with UIO and Adaptive control 2

Resilient Control Strategies for Multi-Area Interconnected P...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Zheng, Zhou School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373318

This work proposes a robust control technique to mitigate false data injection (FDI) attacks on multi-region interconnected power systems. The strategy is based on unknown input observer (UIO) and adaptive control. In order to preserve system performance and stability, adaptive control technology dynamically modifies controller parameters based on estimates from UIO. The efficacy and resilience of the suggested approach are confirmed by numerical simulation and real-world examples, which provide crucial technical support for the power system stability and safety. © 2024 IEEE.

关键词： control system stability

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MedSyn: Medical Image Synthesis of Brain Magnetic Resonance Images Based on Hybrid Information Aggregation 8

MedSyn: Medical Image Synthesis of Brain Magnetic Resonance ...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Jiang, Minbo Song, Zhiwei Zhu, Chuanzhen Wang, Yi School of Computer and Control Engineering Yantai University Yantai China

ISBN: (纸本)9798350305838

Magnetic resonance imaging (MRI) is widely used in clinical scenarios. However, acquisition of certain image modalities may be in limitation due to multiple considerations such as time duration and labor costs. Hence, medical image synthesis can resolve those limitations by estimating the desired target image modality without inducing an actual scan. In this paper, we proposed a novel method which aggregated the hybrid information of MRI to improve the performance of medical image synthesis and addressed it as MedSyn. Specifically, the MedSyn adopted the GAN framework, and made some great improvement. In generator, we proposed a simple yet efficient Tri-Focus Visual Center (TVC) to activate contextual features by establishing inter-dimensional dependencies with the assistance of rotation operations and gaussian functions. Furthermore, we proposed a Hybrid Global-Local Feature Extractor (HGL) module to capture long-range dependencies and exploit potential relationships between spatial and channel dimensions to extract global-local information. Benefitting from the modules of TVC and HGL jointly, the generator could capture obvious image features and underlying semantic information. In discriminator, we proposed a Dynamic Multi-Scale (DMS) Discriminator which could enhance discriminative ability by combining features of different scales dynamically. To evaluate the performance of the proposed method, we conducted experiments on three brain datasets (the Meningioma dataset, the IXI dataset and BRATS dataset), and compared the results with some state-of-the-art methods. The experimental results showed that our MedSyn outperformed other state-of-the-art methods in terms of both quantitative evaluation and qualitative visual assessments. © 2023 IEEE.

关键词： Magnetic resonance imaging

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