关键词：
Computer crime;Location awareness;Hybrid power systems;Prevention and mitigation;Automation;Power distribution;Hardware;Cyber-physical system;cyber security;hybrid attack;bi-level resilient control;feeder automation
摘要：
Distributed feeder automation system (DFAs), as a promising protection technology for power distribution system (PDS) with distributed generation, its vulnerability to cyberattacks and hybrid attacks (contains both physical and cyberattacks) is gradually recognized and haunts utilities, creating potential risks for its large-scale applications. This paper proposes a novel bi-level resilient control solution (BRCS) deployed to DFAs without hardware burden. Two key modules are developed: 1) Lightweight distributed cyberattack detection module (DCDM), deployed into agents of DFAs, based on unsupervised learning to realize the quick detection and reporting of cyberattacks; 2) Robust centralized fault section localization module (CFSLM), installed in DFAs’ host workstation located in control center, achieving the correctly fault section localization and the high dimensionally awareness of attack events in cyberattack and hybrid attack scenarios. By adopting BRCS, outages and load losses caused by cyberattacks can be 100% avoided, and faults caused by physical attacks can be correctly isolated at once. Finally, the effectiveness and performance of the proposal are verified and conducted by the real two-feeder test platform with DFAs. In this process, a digital high-dimensional awareness and control unit is created against cyber and hybrid attacks, contributing to the system-level application of risk management and resilient control.
摘要：
Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed method can improve the accuracy and generalization of SOC estimation without altering the original GRNN output. The proposed method is tested on datasets collected from two lithium-ion batteries under dynamic drive cycles at different temperatures. The results show that the mean absolute errors (MAEs) of the proposed method is 80% and 56% lower than those of GRNNs and Deep-GRNNs, respectively. Particularly at low temperatures, the ResNet-GRNNs reduce MAEs by 86% and 79%. Moreover, the proposed method achieves low MAEs of 0.51% and 1.14%, respectively, under untrained varying temperatures. Finally, upon testing in a practical BMS, the proposed method achieved the highest level of accuracy while reducing memory consumption by 70%, demonstrating its superiority in practical applications.
Lithium-ion batteries are now widely used as energy storage units in electric vehicles. Achieving high accuracy in state of charge (SOC) estimation in the battery management system (BMS) is critical for safe operation of electric vehicles. However, accurate SOC estimation remains a challenging task due to the complex dynamics of batteries and the wide range of ambient temperature. Here we propose a new method called ResNet-GRNN for accurate SOC estimation. Our approach combines a Residual network (ResNet) and a gated recurrent neural network (GRNN). Compared to traditional GRNNs, the proposed method can improve the accuracy and generalization of SOC estimation without altering the original GRNN output. The proposed method is tested on datasets collected from two lithium-ion batteries under dynamic drive cycles at different temperatures. The results show that the mean absolute errors (MAEs) of the proposed method is 80% and 56% lower than those of GRNNs and Deep-GRNNs, respectively. Particularly at low temperatures, the ResNet-GRNNs reduce MAEs by 86% and 79%. Moreover, the proposed method achieves low MAEs of 0.51% and 1.14%, respectively, under untrained varying temperatures. Finally, upon testing in a practical BMS, the proposed method achieved the highest level of accuracy while reducing memory consumption by 70%, demonstrating its superiority in practical applications.
摘要：
Increasing multi-energy coordination in the ship necessitates advanced operation strategies to achieve greenhouse gas reduction and energy efficiency improvement in the maritime industry. However, previous research always overlooks onboard heterogeneous energy carriers and ship power distribution networks (SPDN), as well as underwater radiated noise (URN) generated by ship propellers. This will pose a huge threat to the operational safety of the multi-energy ship microgrids (MESMs) and further harm normal marine life. Hence, this paper formulates a coordinated model for a MESM with combined power, thermal, hydrogen, and freshwater flows. First, the joint energy management and voyage scheduling are modeled for the MESM, considering SPDN constraints and URN limits. Then, a copula-based two-stage operation structure with stochastic programming (SP) and rolling horizon (RH) methods is designed to tackle diverse uncertainties from onboard multi-energy loads and renewable energy. Finally, a progressive hedging (PH) algorithm is developed to support distributed calculation and accelerate the solution. Numerical case studies based on a real voyage in the Nordic countries are used to validate the effectiveness and superiority of the proposed model and method.
摘要：
In the modular multilevel converter (MMC) based high voltage direct current (HVDC) practical projects, the existing voltage feedforward control (VFC) method presents challenges in addressing the frequency shift of high-frequency resonance (HFR) caused by the changes in the interconnected AC system's operating conditions. In this paper, the mathematical characterization function of the impedance real part is used to reveal the mechanism why the existing VFC cannot simultaneously reshape the multiple HFR risk regions into the positive damping characteristics. Therefore, a frequency divided voltage feedforward control (FDVFC) based on the second-order band-pass filters (BPFs) is proposed to simultaneously reshape the multiple HFR risk regions into the positive damping characteristics. The proposal includes the selection of the number of BPFs and how to design their parameters. The parameter design principles and parameter selection ranges of the BPFs are presented one by one using the simplified model of the MMC in high-frequency band. Finally, the effectiveness and correctness of the FDVFC and its analytical calculation expressions for parameter selection are verified by time-domain simulations.
期刊：
IET Conference Proceedings,2025年2024(6):723-727 ISSN：2732-4494
作者机构：
[Yunfeng Li; Tao Wen; Yuhang Zhang; Hangyu Wei; Siyi Xia; Huirong Ye; Yuming Wang] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, People's Republic of China
会议名称：
20th International Conference on AC and DC Power Transmission 2024 (ACDC 2024)
会议时间：
2024
会议地点：
Shanghai, China
会议论文集名称：
20th International Conference on AC and DC Power Transmission 2024 (ACDC 2024)
摘要：
In the modular multilevel converter (MMC) based high voltage direct current (HVDC) transmission system, the high-frequency negative damping effect of the MMC output impedance induces high frequency resonances. The existing active damping controls, such as the virtual series impedance, change the range of high-frequency negative damping effects, thereby suppressing the high-frequency resonances (HFRs). However, it can only be applied to the single operating condition of AC power grid because the single damping controller cannot effectively reshape the impedance characteristics of MMC in multiple HFR risk regions simultaneously. Therefore, a frequency divided active damping control (FDADC) is proposed to address the suppression requirements of multiple HFR risk regions. Based on the simplified model of the MMC in high-frequency band, the analytical calculation method and expressions for the parameters’ selection ranges of the frequency divided damping controller are presented. The parameters of damping controller suitable for multiple operating conditions of AC systems are given, and their effectiveness is verified through electromagnetic transient simulation models.
摘要：
Accurate oscillation mode recognition and stability analysis based on big data are critical for the safe operation of wind turbine systems. This paper utilizes modern statistical and machine learning methodology to analyze the correlation between monitored wind turbine operation data and oscillation phenomena, and a system oscillation analysis and diagnosis method is proposed based on an improved association rule mining (ARM) model. Firstly, the oscillation modes in the power data are measured by the synchronous extraction transform. By improving the ARM model, a thorough study is conducted on the correlation between oscillation modes and variables such as wind speed, compensation degree, voltage fluctuation, etc. Finally, the component importance measure is used to optimize each element's risk weight calculation method relative to the system oscillation. The experiments demonstrate that the proposed association rule analysis method can effectively analyze the relationship between system oscillation phenomena and influencing factors and exhibits high diagnostic accuracy.
作者：
Yushuang He;Feipeng Wang;Hongming Yang;Archie James Johnston Xiao Zhang;Jian Li
期刊：
IEEE TRANSACTIONS ON DEVICE AND MATERIALS RELIABILITY,2025年:1-1 ISSN：1530-4388
作者机构：
[Feipeng Wang; Jian Li] State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, China;[Hongming Yang] School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China;[Archie James Johnston Xiao Zhang] National Key Laboratory of Science and Technology on Vessel Integrated Power System, Naval University of Engineering, Wuhan, China;[Yushuang He] State Key Laboratory of Power Transmission Equipment Technology, Chongqing University, Chongqing, China<&wdkj&>School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha, China
摘要：
Metallized film capacitors (MFCs) are valued for their ability to withstand high-electric-fields, yet they face short-term failure risks when subjected to overvoltage-induced self-healing (SH). This paper presents a monitoring method designed to address the challenges posed by multiple instances of SH in pulsed power applications. Traditional capacitance estimation using sampled current during SH is hindered by the significant arc current. To address this, the study explores the dynamic interplay between sampling current, arc current, and MFC current throughout the SH process. The introduction of Kalman filtering effectively mitigates the impact of noise signals on capacitance monitoring during the short-term cumulative discharge process of SH. Experimental and simulation results attest to the efficiency of the proposed approach, demonstrating an estimation error of less than 1%. Furthermore, a thorough structural analysis of MFCs demonstrates that the proposed method can effectively identify the transition from isolated, safe SH behavior to clustered, disruptive SH events, thereby enabling timely intervention to prevent severe damage.
作者机构：
[Xia, Xiangyang; Xia, Tian; Xia, XY; Yue, Jiahui] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.;[Gong, Yu] State Grid Jibei Elect Power Co Ltd, Beijing 100045, Peoples R China.;[Tan, Jianguo] Zhejiang Narada Power Source Co Ltd, Hangzhou 311300, Peoples R China.;[Wen, Lixing] Was Energy Technol Co Ltd, Xiangtan 411100, Peoples R China.
关键词：
lithium-ion battery;state of charge;Kalman filter algorithm;dung beetle optimizer
摘要：
Accurate prediction of the State of Charge (SOC) of lithium-ion batteries is the foundation for the stable and efficient operation of battery management systems. This paper proposes a lithium-ion battery SOC estimation method based on the Dung Beetle Optimizer (DBO), optimizing the second-order Kalman filter algorithm (DBO-DKF). Leveraging the DBO's fast convergence speed and strong global search capability, this method optimizes the Kalman filter algorithm in the parameter identification stage and the extended Kalman filter algorithm in the SOC estimation stage to address the issue of insufficient estimation accuracy caused by noise covariance matrices of input current and voltage measurements. Through the discharge of current tests under complex conditions, as well as comparing and analyzing credibility indicators such as MAE, RMSE, and MSE as measures of estimation accuracy, it can be verified that the proposed method effectively enhances SOC estimation accuracy.
摘要：
Since the voltage amplitude of the arc suppression device is different during the normal operation and single line-to-ground fault, the problems of high cost and low module utilization rate are serious. An integrated grid-connected converter (IGCC) with reactive power compensation and fault regulation ability is proposed. First, the topology and operation mechanism of IGCC are introduced in this article. A common unit combining neutral point clamped (NPC) and cascaded H-bridge is formed by improving the traditional arc suppression device. By adding a fourth leg in the NPC module and connecting with the arc suppression inductance, the integration of the two structures is realized. The access of NPC unit not only reduces the number of modules of traditional arc suppression device, but also provides an integrated port for arc suppression device and reactive power compensation device. Second, the parameters of active and passive part of IGCC are optimally designed to ensure the stable operation of IGCC. In addition, compared with existing schemes, the superiority of IGCC in cost and volume is proved. Finally, the correctness, feasibility and effectiveness of the proposed topology and functions are verified by the simulation and experiment results.
摘要：
The frequent occurrence of motor faults has been a great disturbance to the development of production in various fields. Traditional fault diagnosis methods primarily use 1-D data or 2-D data. However, 3-D data hold significant promise for motor fault diagnosis due to its voluminous data and unique spatial information. This article aims to explore a motor fault diagnosis method leveraging 3-D data. Nonetheless, motor fault 3-D data exhibit the limitation of lacking geometric structure. To address this limitation, this article proposes a fault diagnosis method named SP-PointCNN. This method uses the proposed spherical projection (SP) method to mitigate the limitations present in the 3-D data of the motor. Meanwhile, a neural network based on PointCNN is constructed to enable the utilization of 3-D data in fault diagnosis. A series of experiments demonstrated the validity of the proposed method. After tested, the diagnosis accuracy of SP-PointCNN can reach 99.36%.
期刊：
International Journal of Circuit Theory and Applications,2025年53(5):2834-2854 ISSN：0098-9886
通讯作者：
Liu, HW
作者机构：
[Liu, Hongwen; Yang, Qing] Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Sec, Chongqing 400044, Peoples R China.;[Liu, Hongwen] Yunnan Power Grid Co Ltd, Elect Power Res Insitute, Kunming, Peoples R China.;[Zeng, Xiangjun] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.;[Chai, Chenchao] Yunnan Megasun Technol Co Ltd, Kunming, Peoples R China.
通讯机构：
[Liu, HW ] C;Chongqing Univ, State Key Lab Power Transmiss Equipment & Syst Sec, Chongqing 400044, Peoples R China.
关键词：
distribution network;extinction coil;ground fault controllable power supply;ground fault extinction
摘要：
A PI automatic adjustment method based on phase deviation fault residual voltage control is proposed to automatically and stably control the amplitude and phase of controllable voltage sources, achieving fast and effective arc suppression of grounding faults in distribution networks. Abstract Aiming at the problem that the current arc suppression method of coil cannot achieve full compensation. First, the calculation model of the full compensation parameters of the controllable voltage source is established by analyzing the grounding fault arc suppression principle of the independent controllable voltage source and the parallel arc suppression coil. Second, by analyzing the arc suppression characteristics of controllable voltage source, a PI automatic regulation method based on phase deviation‐fault residual voltage control is proposed. The method uses fault residual voltage and the change of output voltage amplitude of controllable voltage source to control the adjustment direction and fault residual voltage to control the output voltage amplitude of controllable voltage source. The simulation results show that when the capacitive current of the distribution network is 24.47 A, the fault residual voltage is 2.5 V and the residual current is 446 mA after compensation by the independent controllable voltage source. When the capacitive current is 40.8 A and the deharmonic degree of arc suppression coil is −9%, the fault residual voltage and residual current are 2.2 V and 60 mA after compensated by the parallel arc suppression coil of the controlled voltage source. The proposed PI control method exhibits good stability, resulting in low fault residual voltage, and outperforms traditional control methods, enhancing the arc extinction effect of ground faults.
作者机构：
[Wang, Feipeng; Du, Guoqiang; Pan, Lei; He, Yushuang; Li, Jian] Chongqing Univ, Sch Elect Engn, State Key Lab Power Transmiss Equipment Technol, Chongqing, Peoples R China.;[Yang, Hongming; He, Yushuang] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha, Peoples R China.;[Zhang, Xiao] Naval Univ Engn, Natl Key Lab Sci & Technol Vessel Integrated Power, Natl Key Lab Vessel Integrated Power Syst Technol, Wuhan, Peoples R China.;[Zhang, Zhicheng] Xian Jiaotong Univ XJTU, Sch Chem, Xian Key Lab Sustainable Energy Mat Chem, Xian, Peoples R China.;[Wang, Kaizheng] Kunming Univ Sci & Technol, Fac Elect Engn, Kunming, Peoples R China.
通讯机构：
[Wang, FP ] C;Chongqing Univ, Sch Elect Engn, State Key Lab Power Transmiss Equipment Technol, Chongqing, Peoples R China.
摘要：
AbstractMetallised film capacitors (MFCs) are renowned for their unique self‐healing (SH) properties, which bestow them with exceptional reliability and stability in the face of intense electric fields, high voltages, and pulse power applications. Nonetheless, the exploration of SH characteristics concerning single‐layer dielectric film remains insufficient for advancing MFC reliability evaluation. To establish the theoretical correlation of SH characteristics from the device to the film in the MFCs, this work developed a simulation model to analyse the SH dynamic behaviour in the MFCs. The effects of coupling capacitors, arc resistance and insulation resistance on the macroscopic characteristics (voltage drop and pulse current) are focused during the SH process in MFCs. The results indicate that SH is primarily associated with the voltage drop duration rather than the sampling current. Consequently, the SH process in MFC is characterised as an abrupt decrease in voltage to its minimum value. This refinement enhances the SH energy dissipation model of MFC. The quantified relationship between the macroscopic characteristics and microstructure evolution (polypropylene decomposition and aluminium electrode vaporisation) is established in MFCs under diverse SH energy levels. As SH energy and duration increase, the proportion of energy attributed to polypropylene decomposition increases, resulting in multi‐layer ablation and adhesion within the metallised film and a pronounced deterioration in MFC electrical performance. The examination of macro–micro perspectives sheds new light on the intricate mechanisms governing the SH behaviour in MFCs, offering valuable insights for the advancement of their design, reliability evaluation, and performance optimisation in diverse electrical applications.
摘要：
This study aims to investigate SF 6 decomposition gases, specifically H 2 S, SO 2 , SOF 2 , and SO 2 F 2 , as a means of diagnosing faults in GIS. Using first-principles density functional theory (DFT), simulations were conducted on Zr- and In-modified WTe 2 from several perspectives, including adsorption energy, charge transfer, adsorption distance, density of states, differential charge density, and desorption times at different temperatures. The results demonstrate that the modification of WTe 2 with Zr and In atoms is spontaneous. The pristine WTe 2 exhibits weak physisorption towards all four SF 6 decomposition gases. In contrast, the Zr-WTe 2 system shows strong chemisorption for all four decomposition gases, while the In-WTe 2 system exhibits robust chemisorption specifically for SO 2 , SOF 2 , and SO 2 F 2 , with adsorption energies of −1.169 eV, −1.371 eV, and −1.255 eV, respectively. The Zr-WTe 2 system also demonstrates prolonged desorption times for H 2 S, SO 2 , SOF 2 , and SO 2 F 2 , suggesting its potential as a scavenger for SF 6 decomposition gases. Notably, the In-WTe 2 system exhibits a rapid desorption time of only 0.892 s for SOF 2 at room temperature, indicating its potential for detecting H 2 S gas under ambient conditions.
通讯机构：
[Huang, JC; Peng, ZY ] C;Changsha Univ Sci & Technol, Sch Energy & Power Engn, Key Lab Efficient & Clean Energy Utilizat, Changsha 410111, Peoples R China.
摘要：
Despite the advancements in film fabrication techniques for emerging perovskite solar cells, achieving a high-quality film by solution processing, while maintaining considerable performance remains a significant challenge. To tackle the issue of inferior CsPbI 2 Br perovskite films deposited via solution-based methods, a novel thermal conduction heating approach was devised and implemented, significantly enhancing film uniformity. Crucially, aliphatic amine acetates (3A) were introduced into the precursor solution to regulate the crystallization process and therefore to mitigate defects. Systematic investigation into the impact of 3A molecules featuring varying alkyl chain lengths on defect passivation revealed that the molecular dipole moment of these additives contributed to both defect mitigation and grain size refinement. Notably, the integration of alkyl chains significantly bolstered the hydrophobic properties of the perovskite film. Consequently, an impressive efficiency of 13.50% for HTM-free carbon-based CsPbI 2 Br perovskite solar cells was achieved, and the device exhibited robust stability retaining 92.4% of its initial efficiency at room temperature after being stored in dry air for 5400 h. This research offers profound insights into defect passivation mechanisms and perovskite crystallization dynamics, paving the way for further advancements in the field of perovskite solar cell technology.
作者机构：
[Liu, Rui; Zhang, Chuanliang; Chen, Jiaxiang; Wang, Ziyi] Changsha Univ Sci & Technol, Coll Energy & Power Engn, Changsha 410114, Peoples R China.;[Zhao, Bin] Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
通讯机构：
[Zhao, B ] C;Changsha Univ Sci & Technol, Sch Elect & Informat Engn, Changsha 410114, Peoples R China.
关键词：
Airfoil fin PCHE;Bezier curves;Pareto front;Multi-objective genetic algorithm;Comprehensive performance
摘要：
The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bézier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bézier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (η1) and the third enhanced ratio (η3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to η1 and η3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the η1 of the PCHE channel gradually increases, while η3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, η3 significantly increases while η1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, η3 improves without markedly affecting η1. Compared to the traditional airfoil channel, the η1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the η3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.
The airfoil fin (AFF) Printed circuit heat exchanger (PCHE) has attracted significant attention for its excellent comprehensive performance. This study proposes an optimized design for AFF PCHE to enhance the comprehensive performance by integrating Bézier curves, computational fluid dynamics (CFD), and multi-objective genetic algorithm (MOGA). A set of 12 Bézier curve-based variables is utilized to define and control the airfoil geometry, with optimization targets set on two comprehensive evaluation criteria: the first enhanced ratio (η1) and the third enhanced ratio (η3). The MOGA-generated Pareto front reveals the evolution of AFF structures in relation to η1 and η3. Results show that as the leading and trailing edges of the AFFs become sharper and the thickness decreases, the η1 of the PCHE channel gradually increases, while η3 decreases. Conversely, as the thickness of the AFFs increases and the trailing edge shape transitions from blunt to elliptical and finally to round, η3 significantly increases while η1 decreases. Furthermore, when changes focus mainly on the leading edge of the AFFs, η3 improves without markedly affecting η1. Compared to the traditional airfoil channel, the η1 of the Fin-b channel increases by 3.1%-10.8%, demonstrating its greater suitability under identical flow rate conditions. Similarly, the η3 of the Fin-g channel is 1.4%-11.6% higher than that of the traditional airfoil channel, highlighting its superior performance under identical pumping power conditions. The present work provides a valuable reference for optimizing the design of AFF PCHEs under identical flow rate and pumping power conditions.
关键词：
Electric vehicle;Lithium-ion battery;Internal temperature;State-dependent model;Extended Kalman filter
摘要：
Accurate internal temperature estimation of lithium-ion batteries (LIBs) plays an important role in their safe and economical application. However, the traditional observer estimation method based on linear thermal models cannot accurately describe the nonlinear dynamics of the LIB, and the data-driven estimation approach based on an open loop structure makes it difficult to obtain satisfactory robustness. To bridge this research gap, we construct a state-dependent model to represent the LIB's nonlinear dynamics. The coefficients of this model are implemented by a set of radial basis function neural networks, thus the model considers the actual state of the LIB under different working conditions. Based on the identified state-dependent model offline, an online estimation method of the internal temperature is implemented using the extended Kalman filter (EKF). Furthermore, the robustness is further verified by the extended state observer-EKF. The validation results for different batteries and working conditions show that the root mean square error (RMSE) does not exceed 0.30 °C in the presence of wrong initial values, and it does not exceed 0.28 °C in the presence of bias noise.
Accurate internal temperature estimation of lithium-ion batteries (LIBs) plays an important role in their safe and economical application. However, the traditional observer estimation method based on linear thermal models cannot accurately describe the nonlinear dynamics of the LIB, and the data-driven estimation approach based on an open loop structure makes it difficult to obtain satisfactory robustness. To bridge this research gap, we construct a state-dependent model to represent the LIB's nonlinear dynamics. The coefficients of this model are implemented by a set of radial basis function neural networks, thus the model considers the actual state of the LIB under different working conditions. Based on the identified state-dependent model offline, an online estimation method of the internal temperature is implemented using the extended Kalman filter (EKF). Furthermore, the robustness is further verified by the extended state observer-EKF. The validation results for different batteries and working conditions show that the root mean square error (RMSE) does not exceed 0.30 °C in the presence of wrong initial values, and it does not exceed 0.28 °C in the presence of bias noise.
摘要：
This paper presents a method of rotor position estimation for switched reluctance motors suitable for saturation. The effects of saturation as well as voltage changes are taken into account at the same time. It is based on the inductance in the unsaturated region. When the phase inductance is equal to the threshold, it is defined as a characteristic point. Meanwhile the characteristic pulse signal is triggered. Different inductance intersection thresholds are determined when the phase current and bus voltage change. The rotor position is estimated by interval speed. Compared with the traditional inductance method, the position estimation error is smaller. Finally, the correctness and effectiveness of the proposed method are verified by simulation and experiments.
摘要：
Time series classification is a significant and complex issue in data mining, it is prevalent across various fields and holds substantial research value. However, enhancing the classification rate of time series data remains a formidable challenge. Traditional time series classification methods often face difficulties related to insufficient feature extraction or excessive model complexity. In this study, we propose a self-optimizing polynomial neural network with a temporal feature enhancement, which is referred to as OPNN-T. Existing classifiers based on polynomial neural networks (PNNs) struggle to achieve high-quality performances when dealing with time series data, primarily due to their inability to extract temporal information effectively. The goal of the proposed classifier is to enhance the nonlinear modeling capability for time series data, thereby improving the classification rate in practical applications. The key features of the proposed OPNN-T include the following: (1) A temporal feature module is employed to capture the dependencies in time series data, providing adaptability and flexibility in handling complex temporal patterns. (2) A polynomial neural network (PNN) is constructed using sub-datasets combined with three types of polynomial neurons, which enhances its nonlinear modeling capabilities across diverse scenarios. (3) A self-optimization mechanism is integrated into iteratively optimized sub-datasets, features, and polynomial types, resulting in significant improvements in the classification rate. The experimental results demonstrate that the proposed method achieves superior performances across multiple standard time series datasets, exhibiting higher classification accuracy and greater robustness than the existing classification models. Our research offers an effective solution for time series classification, and highlights the potential of polynomial neural networks in this field.
摘要：
During the battery cathode materials preparation, the temperature correlation, the external environment disturbance, and the system instability caused by control updating widely exist. All these make it difficult to accurately control the temperature of roller kiln. For this reason, an event-triggered decentralized H infinity control method based on adaptive dynamic programming is proposed. First, the temperature interconnection model is established by describing the relationship between temperatures of the atmosphere outlet and each temperature zone. Then, as for temperature interconnection and disturbance, an auxiliary subsystem is introduced and a cost function including upper bound of interconnection term, temperature state, control input, auxiliary control law and disturbance is designed. Next, the event-trigger mechanism is introduced. The event-triggering condition is designed by considering the temperature interconnection, temperature state, control input and disturbance. It is proved that the temperature event-triggered decentralized H infinity control problem can be converted to solve the Hamilton-Jacobi-Isaacs (HJI) equation problem of a set of auxiliary subsystems and the critic learning method is used to solve the HJI equation. The state of the auxiliary subsystem and the pulse dynamic system are proved to be uniformly ultimately bounded. Finally, the proposed control approach is implemented to roller kiln to prove its validity.
关键词：
Monte Carlo method;snake-like robot;volume calculation;workspace;α-shape
摘要：
The method is applicable for solving the obstacle avoidance workspace of a snake-like robot working on high-voltage transmission cables, based on an improved Monte Carlo method, to address the issues of uneven distribution of scattered points, difficulty in extracting point cloud boundaries, and insufficient accuracy in traditional Monte Carlo methods. The proposed method first generates a seed workspace for the snake-like robot using traditional Monte Carlo method and then envelops the seed workspace with a cube and divides it into several smaller cubes that contain points in the workspace equally. Next, Gaussian distribution probability density function is used to extend and sample the seed workspace of the robot, generating the workspace of the snake-like robot. Finally, the α - shape algorithm is used to extract the point cloud boundaries of the snake-like robot workspace and calculate its volume, accurately determining the workspace. Simulation experiments comparing the reconstructed surface obtained from the α - shape algorithm with the point cloud of the snake-like robot workspace show high accuracy.
