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Monitoring of subsynchronous oscillation in a series-compensated wind power system using an adaptive extended kalman filter

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IET RENEWABLE POWER GENERATION 2020年第19期14卷 4193-4203页

作者： Shair, Jan Xie, Xiaorong Yuan, Liang Wang, Yanhui Luo, Yongzhi Tsinghua Univ State Key Lab Power Syst Beijing 100084 Peoples R China Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia State Grid East Inner Mongolia Elect Power Supply Hohhot 010000 Peoples R China

The characteristics of subsynchronous oscillation (SSO) in series-compensated wind power systems are significantly affected by the system's operating condition. Besides the variation in the magnitude and frequency of the SSO during an SSO event, the fundamental frequency may also deviate from its nominal value. This study aims at capturing the dynamics of both subsynchronous and fundamental frequency components simultaneously. This work first explores the strengths and weaknesses of various kalman filtering based frequency tracking algorithms for joint tracking of the fundamental and SSO components. Then, it proposes a novel adaptive extended kalman filtering (AEKF) algorithm, in which the process noise covariance is updated online by maximising the probability density function of the predicted error. The process noise covariance factor changes to positive non-zero value whenever the frequency of the fundamental component deviates. Thus, the proposed AEKF extracts the time-varying subsynchronous component while also tracking the small variations in the fundamental frequency. The tracking performance of the AEKF is validated on computer-generated test signals as well as on the electromagnetic transient simulation model of an actual wind power system facing SSO. The captured fundamental and subsynchronous dynamics can be used for designing monitoring, protection, and control schemes for the SSO.

关键词： nonlinear filters oscillations wind power plants kalman filters adaptive kalman filters adaptive filters fundamental frequency AEKF actual wind power system captured fundamental dynamics subsynchronous dynamics subsynchronous oscillation series-compensated wind power system adaptive extended kalman filter SSO event kalman filtering based frequency tracking algorithms joint tracking fundamental SSO kalman filtering algorithm process noise covariance factor changes fundamental component deviates time-varying subsynchronous component

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State of charge prediction of supercapacitors via combination of kalman filtering and backpropagation neural network

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IET ELECTRIC POWER APPLICATIONS 2018年第4期12卷 588-594页

作者： Wang Houlian Zhou Gongbo China Univ Min & Technol Sch Mech & Elect Engn Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Jiangsu Key Lab Mine Mech & Elect Equipment Xuzhou 221116 Jiangsu Peoples R China

Supercapacitors are increasingly applied to the field of electric vehicles. Although the state of charge (SOC) directly shows the remaining capacity of supercapacitors in an energy management system, vehicle drivers also require the changes of supercapacitors in the future for driving reference. How to predict SOC of supercapacitors has become a pressing problem. In order to solve the above problem, a method combining backpropagation (BP) neural network with the kalman filtering algorithm is proposed to predict SOC in the future. The BP neural network inputs SOC estimated by the kalman filtering algorithm as the training data to train network, and thereby being able to forecast the SOC in the future period. The algorithm is verified in simulations and experiments under the two conditions: NewYorkBus and NYCC, with the consideration of the influence of the length of training data and temperature. The results show the max absolute error during prediction at different lengths is under 6% in simulations and experiments. In addition, temperature has almost no effect on the prediction accuracy. The current research implies that this method can be applied to predict supercapacitors' SOC in the future.

关键词： supercapacitors electric vehicles kalman filters backpropagation neural nets power engineering computing state-of-charge prediction supercapacitors kalman filtering algorithm backpropagation neural network SOC prediction energy management system vehicle drivers driving reference BP neural network training data NewYorkBus NYCC

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Design and verification of Zigbee sensor height setting algorithm based on kalman Filter 14

Design and verification of Zigbee sensor height setting algo...

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14th IEEE International Conference on Electronic Measurement and Instruments (ICEMI)

作者： Liu Ning Wei Xiaohui Xu Chen Natl Ocean Technol Ctr Tianjin 300112 Peoples R China

ISBN: (数字)9781728105109

ISBN: (纸本)9781728105109

In order to obtain the meteorological and hydrological parameters of the sea-air boundary layer in real time, calculate and modify the refractive index profile to evaluate the influence of the evaporating waveguide on the propagation of electromagnetic waves at sea, the environmental monitoring and diagnosis system of the evaporating waveguide is designed. The system carries out multi-sensor networking' based on Zigbee technology, which can measure temperature, humidity and air pressure parameters at different levels. Measurement terminal fixed high is the key problem in the process of system design, measurement terminal 9 shaft position sensor, using kalman Filter algorithm to solve attitude, through the terminal and calculating chain cord length measurement terminal height, the experimental results show that the kalman Filter is given under the condition of static and dynamic Angle data is better than that of fast calculating or gyroscope integration results. The algorithm is applied to the measurement system. Under the breeze condition, the height setting error of the measurement terminal is less than 0.3m, which is better than the existing' sonde height setting results.

关键词： Zigbee kalman filtering algorithm CC2530 Wireless Sensor network

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A Study on Applying an Autoregressive Model with the kalman filtering in Accuracy Improvement of Dissolved Oxygen Measurement

A Study on Applying an Autoregressive Model with the Kalman ...

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International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP)

作者： Tanatipuknon, Asadang Karnjana, Jessada Thammasat Univ Sirindhorn Int Inst Technol Pathum Thani Thailand Natl Sci & Technol Dev Agcy NECTEC Pathum Thani Thailand

ISBN: (纸本)9781728101644

The dissolved oxygen (DO) measurement plays a crucial role in every automatic aerator-control system for shrimp farming because the DO affects both the animal survival rate and the growth rate. As a consequence, the accuracy degree of the DO sensor matters. The sensor with high accuracy is not economical. Therefore, we propose a framework for accuracy improvement in the DO measurement of the low-cost sensor. The proposed framework is based on the kalman filtering algorithm in which an autoregressive technique is used to model the state transition. Therefore, it does not require a complex DO dynamic model. Experimental results show that the framework can be used to improve measurement accuracy in most cases. In the best case, our proposed method increases the accuracy by 13.5%. However, the degree of improvement is on a small scale.

关键词： kalman filtering algorithm autoregressive model dissolved oxygen measurement shrimp farming

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Investigation of a DC OCT signal processing method based on an improved kalman algorithm

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OPTIK 2018年 171卷 809-820页

作者： Shen, Yan Ge, Jin-ming Liu, Rui-tong Fan, Wei Yang, Ying-xuan Harbin Inst Technol Dept Elect Engn Harbin 150001 Heilongjiang Peoples R China State Grid Liaoning Elect Power Res Inst Shenyang 110006 Liaoning Peoples R China

In this paper, we propose an improved kalman filtering algorithm, to solve problems associated with processing of direct current (DC) signals in optical current transformers (OCTs). The algorithm was realized as follows. Based on the characteristics of the OCT, mixed noise was included with the parameters and state information of the measurement model, to ensure that it could be processed. State predictions and update equations for the improved kalman algorithm were obtained, according to the minimum mean squared error criterion, using the established model. Finally, real-time noise estimation and correction was achieved by designing a recursive noise filter. Results from simulations and experiments demonstrate that the improved signal processing algorithm is effective in filtering mixed noise from DC signals measured by an OCT. Furthermore, the proposed algorithm is able to achieve real-time noise correction during the filtering process.

关键词： Optical current transformer Signal processing kalman filtering algorithm

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Complex neural fuzzy system and its application on multi-class prediction - A novel approach using complex fuzzy sets, IIM and multi-swarm learning

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APPLIED SOFT COMPUTING 2019年 84卷 105735-000页

作者： Li, Chunshien Tu, Chia-Hao Natl Cent Univ Dept Informat Management Lab Intelligent Syst & Applicat Taoyuan Taiwan

In this paper, we present a novel complex neural fuzzy approach to multi-class prediction. The complex neural fuzzy system (CNFS) is proposed using complex fuzzy sets (CFSs), fuzzy causalities and multi-swarm machine learning. In general, CFSs are regarded as advanced fuzzy sets with membership degrees defined in the unit disk of the complex plane, in contrast to regular fuzzy sets with membership degrees in the real-valued unit interval [0,1]. The proposed model is composed of the premises designed by CFSs, the consequences designed by Takagi-Sugeno linear functions, and a fuzzy causality layer connecting the premises toward the consequences, and it is able to perform prediction of multiple targets. The usage of fuzzy causality in the proposed model makes difference to traditional fuzzy models using If-Then rules, and gives the freedom and flexibility for model construction. To optimize the proposed CNFS, we present a hybrid learning scheme using the particle swarm optimization with multiple swarms (denoted as PSOmsw) and the kalman filtering algorithm (denoted as KFA). In the hybrid learning method, the KFA updates the consequence parameters while the PSOmsw evolves the rest parameters of the model. The proposed approach has been tested with experiments using several real-world stock market datasets. Compared with other methods, the proposed approach has shown excellent performance. (C) 2019 Published by Elsevier B.V.

关键词： Complex fuzzy set Fuzzy causality Multi-swarm PSO kalman filtering algorithm Complex neural fuzzy system

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Design of Quad-rotor Target Tracking System 30

Design of Quad-rotor Target Tracking System

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30th Chinese Control and Decision Conference (CCDC)

作者： Xiu, Chunbo Zhao, Yalong Wang, Ruosi Tianjin Polytech Univ Sch Elect Engn & Automat Tianjin 300387 Peoples R China

ISBN: (纸本)9781538612446

The accuracy of tracking based on Camshift would decrease due to the similarity between target color and background color or the target is obscured. For the above problems, improved target tracking algorithm based on Camshift is proposed in this paper. The Camshift algorithm is improved by using the contour features of the target, and Camshift search window is updated according to the contour feature of the target. Thus, interference of background and strong light is weakened. kalman filtering algorithm is used to predict the motion state of the tracking target, enhancing the of of tracking when the tracking target is obscured. Experiments show that Camshift is combined with the contour feature of target and make the tracking more effectively under the conditions of background. And the kalman filtering algorithm is used to predict position of the target to make the tracking effectively when the target is obscured.

关键词： Camshift Tracking algorithm kalman filtering algorithm Target Contour

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Improved Target Tracking algorithm Based on Camshift 30

Improved Target Tracking Algorithm Based on Camshift

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30th Chinese Control and Decision Conference (CCDC)

作者： Xiu, Chunbo Su, Xuemiao Pan, Xiaonan Tianjin Polytechn Univ Sch Elect Engn & Automat Tianjin 300387 Peoples R China

ISBN: (纸本)9781538612446

The accuracy of tracking based on Camshift would decrease due to the similarity between target color and background color or the target is obscured. For the above problems, improved target tracking algorithm based on Camshiff is proposed in this paper. The Camshift algorithm is improved by using the contour features of the target, and Camshift search window is updated according to the contour feature of the target. Thus, interference of background and strong light is weakened. kalman filtering algorithm is used to predict the motion state of the tracking target, enhancing the efficiency of tracking when the tracking target is obscured. Experiments show that Camshift is combined with the contour feature of target and make the tracking more effectively under the conditions of background. And the kalman filtering algorithm is used to predict position of the target to make the tracking effectively when the target is obscured.

关键词： Camshift Tracking algorithm kalman filtering algorithm Target Contour

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RSSI Localization Method for Mine Underground Based on RSSI Hybrid filtering algorithm 9

RSSI Localization Method for Mine Underground Based on RSSI ...

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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Liu Yi Liang Tao Sun Jun School of Mechanical Electronic and Information Engineering China University of Mining and Technology (Beijing)

ISBN: (纸本)9781509038237;9781509038220

Aiming at the coal mine personnel positioning method based on wireless sensor network technology, contrasting with advantage of now-existing feasible methods, consulting with traditional trilateral positioning, this paper proposed weighted trilateral localization method based on RSSI ranging. In order to ensure the precision of distance measurement, analysis the cause of error of RSSI, improved the performance of the linear discrete kalman filter, to propose hybrid filtering algorithm. The simulation shows that the improved method has effectively reduce the positioning error and improve the personnel positioning accuracy.

关键词： wireless sensor networks RSSI kalman filtering algorithm weighted trilateral localization algorithm

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Investigation of optical current transformer signal processing method based on an improved kalman algorithm

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OPTICAL ENGINEERING 2018年第1期57卷 013103-013103页

作者： Shen, Yan Ge, Jin-ming Zhang, Guo-qing Yu, Wen-bin Liu, Rui-tong Fan, Wei Yang, Ying-xuan Harbin Inst Technol Dept Elect Engn Harbin Heilongjiang Peoples R China State Grid Liaoning Elect Power Res Inst Shenyang Liaoning Peoples R China

This paper explores the problem of signal processing in optical current transformers (OCTs). Based on the noise characteristics of OCTs, such as overlapping signals, noise frequency bands, low signal-to-noise ratios, and difficulties in acquiring statistical features of noise power, an improved standard kalman filtering algorithm was proposed for direct current (DC) signal processing. The state-space model of the OCT DC measurement system is first established, and then mixed noise can be processed by adding mixed noise into measurement and state parameters. According to the minimum mean squared error criterion, state predictions and update equations of the improved kalman algorithm could be deduced based on the established model. An improved central difference kalman filter was proposed for alternating current (AC) signal processing, which improved the sampling strategy and noise processing of colored noise. Real-time estimation and correction of noise were achieved by designing AC and DC noise recursive filters. Experimental results show that the improved signal processing algorithms had a good filtering effect on the AC and DC signals with mixed noise of OCT. Furthermore, the proposed algorithm was able to achieve real-time correction of noise during the OCT filtering process. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： optical current transformer signal processing kalman filtering algorithm central difference kalman filter

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