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Stability and performance analysis of the compressed kalman filter algorithm for sparse stochastic systems

Science China(Technological Sciences) 2024年第2期67卷 380-394页

作者： LI RongJiang GAN Die XIE SiYu LüJinHu Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China Zhongguancun Laboratory Beijing 100049China School of Aeronautics and Astronautics University of Electronic Science and Technology of ChinaChengdu 611731China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

This paper considers the problem of estimating unknown sparse time-varying signals for stochastic dynamic *** deal with the challenges of extensive sparsity,we resort to the compressed sensing method and propose a compressed kalman filter(KF)*** algorithm first compresses the original high-dimensional sparse regression vector via the sensing matrix and then obtains a KF estimate in the compressed low-dimensional ***,the original high-dimensional sparse signals can be well recovered by a reconstruction *** ensure stability and establish upper bounds on the estimation errors,we introduce a compressed excitation condition without imposing independence or stationarity on the system signal,and therefore suitable for feedback *** further present the performance of the compressed KF ***,we show that the mean square compressed tracking error matrix can be approximately calculated by a linear deterministic difference matrix equation,which can be readily evaluated,analyzed,and ***,a numerical example demonstrates that our algorithm outperforms the standard uncompressed KF algorithm and other compressed algorithms for estimating high-dimensional sparse signals.

关键词： sparse signal compressed sensing kalman filter algorithm compressed excitation condition stochastic stability tracking performance

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A Distributed Compensation Method for Active Power filters Based on Model Predictive Control and kalman filter algorithm

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2023年第7期18卷 1076-1084页

作者： Wang, Rui Tao, Jun Zhang, Huaying Zhu, Qianlong Anhui Univ Sch Elect Engn & Automat Hefei 230000 Anhui Peoples R China Shenzhen Power Supply Co Ltd New Smart City High Qual Power Supply Joint Lab Ch Shenzhen 518020 Guangdong Peoples R China

Research on global harmonic mitigation against distributed and high-proportioned nonlinear loads in modern distribution networks has received extensive attention. In this paper, a distributed compensation method for active power filters (APFs) is proposed, which utilizes a small number of APFs with adjustable reference to alleviate the global voltage distortion caused by a large number of distributed nonlinear loads. The model predictive control (MPC) is employed to calculate the optimal current reference for compensators at each optimization moment by predicting the system ' s performance in a certain future time period. Furthermore, the kalman filter (KF) algorithm is used to estimate the initial harmonic voltage for the MPC, reducing the unfavorable effects of the model uncertainty and measurement noise. The test results based on a seven-bus distribution network in MATLAB are provided to verify the effectiveness of the proposed method. (c) 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

关键词： harmonic mitigation active power filter distributed compensation method model predictive control kalman filter algorithm

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Linear kalman filter algorithm for analysis of transient stability swings in large interconnected power systems

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ELECTRIC POWER SYSTEMS RESEARCH 1995年第3期34卷 173-178页

作者： Soliman, SA AlKandari, AM ElHawary, ME Electrical Engineering Department College of Technological Studies PO Box 42325 Shuwaikh 70654 Kuwait Electrical Engineering Department Technical University of Nova Scotia Halifax NS Canada B3J 2X4

The amount of damping in power system oscillations plays an important role in the stability of the system. This paper presents a new application of linear kalman filtering for analysis of transient stability swings in large interconnected power systems in order to determine the damping constants and oscillation amplitude. It is shown that kalman filtering models are well suited for analyzing beat oscillations as well as noisy measurements. The effects of sampling rate, data window size and overall accuracy are also investigated. Results are reported for an actual recorded data set. II is concluded that the quality of estimates is degraded as the noise level is increased.

关键词： kalman filter algorithm transient stability electromechanical oscillations swing curves damping

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Selection of Optimal Location and Size of Multiple Distributed Generations by Using kalman filter algorithm

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IEEE TRANSACTIONS ON POWER SYSTEMS 2009年第3期24卷 1393-1400页

作者： Lee, Soo-Hyoung Park, Jung-Wook Yonsei Univ Sch Elect & Elect Engn Seoul 120749 South Korea

Increase in power consumption can cause serious stability problems in electric power systems if there are no ongoing or impending construction projects of new power plants or transmission lines. Additionally, such increase can result in large power losses of the system. In costly and environmentally effective manner to avoid constructing the new infrastructures such as power plants, transmission lines, etc., the distributed generation (DG) has been paid great attention so far as a potential solution for these problems. The beneficial effects of DG mainly depend on its location and size. Therefore, selection of optimal location and size of the DG is a necessary process to maintain the stability and reliability of existing system effectively before it is connected to a power grid. However, the systematic and cardinal rule for this issue is still an open question. In this paper, a method to determine the optimal locations of multiple DGs is proposed by considering power loss. Also, their optimal sizes are determined by using the kalman filter algorithm.

关键词： Distributed generation grid connection kalman filter algorithm load-concentration-bus optimal location optimal size power loss

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Design and Implementation of auto-filling liquid nitrogen for HTS maglev vehicles based on kalman filter algorithm

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CRYOGENICS 2020年 111卷 103167-103167页

作者： Wen, Peng Wang, Wen Ren, Yu Lei, Wuyang Cheng, Hao Xu, Yihuan Deng, Zigang Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China CRRC Tangshan Co Ltd Tangshan 063035 Peoples R China

The cryostat equipped with superconducting materials is one of the most important component of high-temperature superconducting maglev vehicles. However, due to the multiple energy disturbance forms in the cryostat, such as AC losses of superconductors, vibration of the cryostat and so on, the evaporation of liquid nitrogen (LN2) will be stimulated to accelerate, which will seriously threaten the vehicle safety. Therefore, this paper designs an intelligent controller which can realize the function of auto-filling LN2 based on the kalman filter (KF) algorithm. Moreover, in this publication the corresponding methodology will be described as well in detail. Initially, the platinum resistance sensors were used to study the relationship between the temperature and LN2 level, meanwhile, the system state equation and observation equation were established. Then, the KF algorithm was introduced to estimate the true LN2 level. Based on the feedback signal of LN2 level, the system utilizes the STM32 microprocessor as control chip to control the solenoid valves and realizes the function of auto-filling. The controller has been implemented and the total time for filling one cryostat was reduced from 86 min to 16 min. This result is very encouraging and inspiring for the practical application of HTS maglev systems.

关键词： High-temperature superconductors Liquid nitrogen Auto-filling LN2 kalman filter algorithm

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A new state monitoring method for IoT sensor based on kalman filter algorithm

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2020年第4期13卷 448-463页

作者： Dong, Zhanqi Xuchang Univ Sch Elect Engn Xuchang 461000 Peoples R China

In order to overcome the high error of traditional sensor working state monitoring, a new sensor state monitoring method based on kalman filter algorithm is proposed in this paper. Install the corresponding monitoring equipment around the internet of things sensor to be tested, collect the working signal of the sensor in real time, and ensure the accuracy of the monitoring signal. kalman filter algorithm is used to analyse the initial signal state of internet of things sensor, and filter the sensor state signal. The status signals of Internet of Things (IoT) sensors after filtering are clustered, and the current working status of IoT sensors is determined by comparing with the setting range, so as to obtain the monitoring results. The experimental results show that compared with the traditional monitoring method, the proposed method has the lowest monitoring error rate of 15%, which has high practical value.

关键词： kalman filter algorithm signal clustering IoT sensor state monitoring initial signal state setting range

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A hybrid model-data-driven framework for inverse load identification of interval structures based on physics-informed neural network and improved kalman filter algorithm

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APPLIED ENERGY 2024年 359卷

作者： Liu, Yaru Wang, Lei Ng, Bing Feng Beihang Univ Inst Solid Mech Sch Aeronaut Sci & Engn Natl Key Lab Strength & Struct Integr Beijing 100191 Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore Beihang Univ Ningbo Inst Technol Aircraft & Prop Lab Ningbo 315100 Peoples R China

Accurately capturing data on the external loads that large structural systems endure is crucial for improving the performance of energy equipment. This paper introduces a novel hybrid model -data -driven framework for the dynamic load identification of interval structures, which seamlessly combines finite -element modeling with machine learning techniques. To address potential ill -posed issues in model -driven methods and the interpretability limitations of data -driven methods, we propose a physics -informed neural network. This neural network effectively inverts uncertain modal responses with low data requirements and high predictive performance high by integrating the underlying modal transformation equation into the loss function of a fully connected neural network. To identify the modal loads using predicted modal displacement/acceleration responses, we introduce a pioneering dynamics inversion method. This method modifies the traditional kalman filter with an assumption of unknown inputs to reduce the sensitivity of load identification process to different noises. In addition, our approach incorporates a subinterval Chebyshev expansion method to adaptively determine the interval boundaries of external loads. The efficiency of the proposed method is assessed through two numerical examples and validated through comparative research against baseline methods. Our findings suggest that this approach enhances precision, robustness, and generalization in dynamic load identification, even when facing challenges such as limited training data, significant noise interference, and non -zero initial conditions.

关键词： Dynamic load identification Model-data-drive method Physics-informed neural network kalman filter algorithm Interval structures

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Submodule IGBT Parameters Estimation Scheme in Modular Multilevel Converters with Reduced Voltage Sensors Based on kalman filter algorithm 4th

Submodule IGBT Parameters Estimation Scheme in Modular Multi...

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4th International Forum on Smart Grid Protection and Control (Purple Mountain Forum)

作者： Zhou, Tenglong Zheng, Wendi Xu, Qidong Zeng, Xiangyong Nie, Jianxiong Fuzhou Univ Fuzhou 350108 Peoples R China Fujian Smart Elect Engn Technol Res Ctr Fuzhou 350108 Peoples R China

ISBN: (纸本)9789811397790;9789811397783

Considering the reliability of modular multilevel converter (MMC) closely related to key component parameters, a novel parameter estimation method is proposed for insulated gate bipolar transistor (IGBT) of submodule (SM) in MMC. The relationship between the switch status and the voltage of submodule is analyzed and the model of the bridge arm voltage is established. kalman filtering theory is introduced against the measurement noise of the bridge arm data from sensors. Under the linear minimum variance criterion, the model of the IGBT parameter monitoring information in MMC submodule is established to obtain the IGBT parameters from each submodule in the bridge arm. The substantial reduction in the number of voltage sensors improves the system reliability and decreases its cost and complexity. This method can also effectively eliminate the influence of sensor noise. Finally, the effectiveness of the proposed technique is validated via a PSCAD/EMTDC simulation analysis of MMC-HVDC.

关键词： Modular multilevel converter IGBT parameters estimation kalman filter algorithm

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Design of an Eye Movement-Controlled Wheelchair Using kalman filter algorithm

Design of an Eye Movement-Controlled Wheelchair Using Kalman...

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IEEE International Conference on Information and Automation (ICIA)

作者： Bai, Dianchun Liu, Zhicheng Hu, Qing Yang, Junyou Yang, Guang Ni, Changlin Yang, Deguo Zhou, Long Shenyang Univ Technol Sch Elect Engn Shenyang Liaoning Provin Peoples R China

ISBN: (纸本)9781509041022

Nowadays, electric wheelchairs on the market mostly were controlled by joystick which required users to operate by hands flexibly. But this wheelchair cound not be used by patients with hand disability or paraplegics. This paper designs an electric wheelchair controlled by eye movements. An eye tracker of Tobii is connected with a electric wheelchair. The eye tracker of Tobii is used to convert eye movement signals into coordinate of gazing points. But these data of gazing points are too diverging to be used directly. kalman filter algorithm is used to filter data of coordinate and obtain optimal data. By discriminating eye movement-controlled panel area where gazing points is located in, a computer sends appropriate signal to the wheelchair, so as to achieve the goal of moving wheelchair. Thus patients are no longer using hands to control electric wheelchairs.

关键词： eye tracker eye-controlled electric wheelchair kalman filter algorithm

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CIM-KF: Efficient Computing-in-memory Circuits for Full-Process Execution of kalman filter algorithm 24

CIM-KF: Efficient Computing-in-memory Circuits for Full-Proc...

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53rd International Conference on Parallel Processing (ICPP)

作者： Xiao, Pingdan Hong, Qinghui Du, Sichun Zhang, Jiliang Hunan Univ Changsha Peoples R China

ISBN: (纸本)9798400717932

kalman filter (KF) algorithm, which can solve the state estimation problem of multi-variable and complex dynamical system, plays a pivotal role in a multitude of engineering scenarios. However, the traditional digital computing architecture represented by the vonNeumann architecture are currently confronted with high overhead challenges in terms of latency, energy, and area when executing KF algorithm. Aiming at the problem, we propose CIM-KF, the first Computing-in-memory (CIM) circuits for KF algorithm. CIMKF can efficiently carry out the entire process of KF algorithm by capitalizing on the large-scale parallel computation inherent in the CIM architecture. The evaluation shows that CIM-KF has average 96.15% accuracy for 32-th order parameter matrix in KF algorithm, and can be 13.89x similar to 55.21x faster than existing ASIC and FPGA implementations of KF algorithm. The results also demonstrate that CIM-KF can deliver up to 3.52x energy efficiency improvement and 54.79x area efficiency improvement, and reduce over 90% latency overhead, compared with the SOTA counterparts. Furthermore, we propose a novel design of ReRAM-CIM architecture, underpinned by the CIM-KF, aimed at precise and rapid calculation of the state of capacity and terminal voltage in Battery Management Systems. By evaluation, our proposed architecture boasts the estimation accuracy within 2% error for the individual battery cell of state of capacity and terminal voltage. With the strengths of the parallel computing intrinsic to CIM architecture and the speed of analog circuit, our architecture facilitates speed up to 70.6x in accurate estimation when bench-marked against the SOTA work.

关键词： Computing-in-memory ReRAM Circuit design kalman filter algorithm

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