Study on the Tangent Calculation Method of Frequency-domain Dielectric Loss Angle Based on Improved Kalman Filtering Algorithm

2021 ◽  
Author(s):  
Zhiming Huang ◽  
Ran Zhuo ◽  
Mingli Fu ◽  
Yue Yu ◽  
Yan Luo ◽  
...  
Keyword(s):  
Dielectric Loss ◽  
Frequency Domain ◽  
Kalman Filtering ◽  
Calculation Method ◽  
Loss Angle ◽  
Filtering Algorithm ◽  
Dielectric Loss Angle ◽  
Kalman Filtering Algorithm ◽  
Tangent Calculation

Sparse Representation Based Dielectric Loss Angle Measurement

2015 ◽  
Vol 9 (1) ◽  
pp. 566-570
Author(s):  
Zhang Ji ◽  
Jianfeng Zheng
Keyword(s):  
Dielectric Loss ◽  
Power Systems ◽  
Sparse Representation ◽  
Precise Measurement ◽  
Angle Measurement ◽  
Measuring Error ◽  
Loss Angle ◽  
Analysis Method ◽  
Electric Capacity ◽  
Dielectric Loss Angle

Precise measurement of dielectric loss angle is very important for electric capacity equipment in recent power systems. When signal-to-noise is low and fundamental frequency is fluctuating, aiming at the measuring error of dielectric loss angle based on some recent Fourier transform and wavelet transform harmonics analysis method, we propose a novel algorithm based on sparse representation, and improved it to be more flexible for signal sampling. Comparison experiments describe the advantages of our method.

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

2021 ◽  
pp. 014233122110056
Author(s):  
Bingya Zhao ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Topological Entropy ◽  
Kalman Filtering ◽  
Transmission Rate ◽  
Necessary Condition ◽  
Estimation Errors ◽  
Positive Role ◽  
Filtering Algorithm ◽  
Wiretap Channels ◽  
Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

scholarly journals Distributed Diffusion Unscented Kalman Filtering Algorithm with Application to Object Tracking

2020 ◽  
Vol 53 (2) ◽  
pp. 3577-3582
Author(s):  
Hao Chen ◽  
Jianan Wang ◽  
Chunyan Wang ◽  
Dandan Wang ◽  
Jiayuan Shan ◽  
...  
Keyword(s):  
Object Tracking ◽  
Kalman Filtering ◽  
Filtering Algorithm ◽  
Kalman Filtering Algorithm
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