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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Lv, Ruomeng North China Electric Power University School of Control and Computer Engineering Baoding China

ISBN: (纸本)9798350355253

Aiming at the problems of abnormal points and noise points existing in the original high-dimensional data of wind turbine system, and considering the strict requirements of fault warning on the accuracy of prediction model, a wind turbine fault warning method based on Bayes optimized hybrid neural network prediction model is proposed. In order to ensure the quality of modeling data, Ordering Points To Identify the Clustering Structure (OPTICS) and Isolation forest (iForest) were used to clean the pre-processed data. A "Convolutional Neural Network (CNN)"-"Long Short-Term Memory (LSTM)"-Attention hybrid model based on Bayesian optimization was established, and combined with the spatial feature extraction capability of CNN, the time series processing capability of LSTM and the weight allocation strategy of Attention mechanism, the accurate prediction of wind turbine under normal working conditions was realized through Bayesian optimization. The sliding window method is used to construct an early warning index, and its threshold is calculated by the kernel density estimation method to achieve reliable early warning and reduce the false alarm rate. Experiments with real fault data show that this method has excellent performance in improving prediction accuracy and reducing false positive rate. © 2024 IEEE.

关键词： Wind forecasting

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

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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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Electricity theft detection model based on the CEEMDAN-CNN-ViT 2

Electricity theft detection model based on the CEEMDAN-CNN-V...

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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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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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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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