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Fuzzy-Inverse-Model-Based Networked Tracking control Frameworks of Time-Varying Signals

IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1708-1710页

作者： Shiwen Tong Dianwei Qian Keya Yuan Dexin Liu Yuan Li Jiancheng Zhang the College of Robotics Beijing Union UniversityBeijing 100101China the School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

Dear Editor,Tracking control in networked environment is a very challenging problem due to the contradiction of rapid response to the time-varying signal and the inevitable delay introduced by networks. This letter has proposed several fuzzy-inverse-model-based network tracking control frameworks which are helpful in handling the system with nonlinear dynamics and uncertainties.

关键词： contradiction letter inverse

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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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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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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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Two-Stage Distributionally Robust Optimization Model of Integrated Energy System Considering Flexible Resources 8

Two-Stage Distributionally Robust Optimization Model of Inte...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Li, Jie Deng, Dan Li, Yunzhi North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331523558

Due to the intermittent and fluctuating properties of renewable energy, Integrated Energy Systems (IES) face great challenges in terms of secure, economic, and low-carbon operation. To improve the penetration rate of renewable energy and the flexibility of IES, a two-stage distributionally robust optimization model is proposed considering flexible resources. Firstly, a fuzzy set of the total error function is established based on the Wasserstein distance. Then a two-stage distributionally robust optimization model is established within the day-ahead and real-time scales. Finally, the flexible resource model is finely established and the flexibility constraints are considered based on the flexibility demand of IES. The simulation results show that the upward and downward flexibility of IES can be maximally increased by 8 342.3 kW and 8 428.6 kW by means of flexible resources, meanwhile the system economy has not significantly decreased. © 2024 IEEE.

关键词： Clean energy

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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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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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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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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning

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Neural Computing and Applications 2024年第25期36卷 15545-15565页

作者： Gong, Yiyun Yu, Haibo Kang, Li Sun, Chaoli Zeng, Jianchao School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Computer Science and Technology North University of China Taiyuan030051 China School of Environment and Safety Engineering North University of China Taiyuan030051 China School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China

This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance surrogate-assisted evolutionary optimization for medium-scale expensive problems. The method attempts to address the challenges posed by multi-polar and multi-variable coupling properties in such problems. During the iteration, TSLL splits the optimization process into two distinct stages. First, two unsupervised feature learning techniques: principal component analysis (PCA) and Autoencoder, are utilized to improve the representation of potential optimal samples in the solution space. PCA is used for feature reduction, extracting the most important features. On the other hand, Autoencoder focuses on reconstructing features within the medium-scale solution space. To ensure comprehensive exploration of the entire solution space, TSLL employs two distinct surrogate modeling approaches along with Q-learning in the second stage to dynamically select the mutation strategy for the differential evolution operator. Numerous experiments demonstrate the superiority of TSLL over five state-of-the-art surrogate-assisted approaches and two sophisticated evolutionary algorithms including the winner of CEC 2017 on medium-scale benchmark problems and a wind farm layout problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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