Research on Temperature Drift Compensation Method to Improve Measurement Accuracy of Outdoor D-PMU

2020 ◽  
Author(s):  
Li Xin ◽  
Shi Bonian ◽  
Wu Renbo
Keyword(s):  
Measurement Accuracy ◽  
Compensation Method ◽  
Temperature Drift ◽  
Improve Measurement ◽  
Drift Compensation

scholarly journals Temperature Drift Compensation of Eddy Current Sensor under High Temperature Environment

2018 ◽  
Vol 38 ◽  
pp. 04005
Author(s):  
Ping He ◽  
YunKai Ma ◽  
Hui Chen
Keyword(s):  
High Temperature ◽  
Eddy Current ◽  
Measurement Accuracy ◽  
Compensation Method ◽  
Current Sensor ◽  
High Precision Measurement ◽  
Temperature Drift ◽  
Eddy Current Sensor ◽  
Practical Engineering ◽  
Drift Compensation

Eddy current sensor is an sensor based on eddy current effect. In practical engineering applications, the ambient temperature of eddy current sensor may be up to 135 ℃. The temperature drift of eddy current sensor magnifies the error of displacement detection. In this paper, the main factors that cause temperature drift are analyzed in detail, and the results show that the compensation based on single parameter can not meet the demand of high-precision measurement. For this reason, this paper proposes an external compensation method which applies mathematical fitting to realize compensation for temperature drift. The experimental results show that the measurement accuracy of the external compensation method reaches 0.25% in the working temperature range, which greatly improves the measurement accuracy of eddy current sensor under high temperature.

scholarly journals A Temperature Drift Compensation Method for Pulsed Eddy Current Technology

Sensors ◽  
2018 ◽  
Vol 18 (6) ◽  
pp. 1952 ◽  
Author(s):  
Biting Lei ◽  
Pengxing Yi ◽  
Yahui Li ◽  
Jiayun Xiang
Keyword(s):  
Eddy Current ◽  
Compensation Method ◽  
Temperature Drift ◽  
Current Technology ◽  
Pulsed Eddy Current ◽  
Drift Compensation

Modeling and Temperature Drift Compensation Method for Surface Plasmon Resonance-Based Sensors

2017 ◽  
Vol 17 (19) ◽  
pp. 6246-6257 ◽  
Author(s):  
Carlos Alberto de Souza Filho ◽  
Antonio Marcus Nogueira Lima ◽  
Franz Helmut Neff
Keyword(s):  
Surface Plasmon Resonance ◽  
Surface Plasmon ◽  
Plasmon Resonance ◽  
Compensation Method ◽  
Temperature Drift ◽  
Drift Compensation

scholarly journals Effectiveness of Variable-Gain Kalman Filter Based on Angle Error Calculated from Acceleration Signals in Lower Limb Angle Measurement with Inertial Sensors

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yuta Teruyama ◽  
Takashi Watanabe
Keyword(s):  
Kalman Filter ◽  
Lower Limb ◽  
Measurement Accuracy ◽  
Inertial Sensors ◽  
Angle Measurement ◽  
Angle Error ◽  
Variable Gain ◽  
Improve Measurement ◽  
Kalman Gain ◽  
Acceleration Signals

The wearable sensor system developed by our group, which measured lower limb angles using Kalman-filtering-based method, was suggested to be useful in evaluation of gait function for rehabilitation support. However, it was expected to reduce variations of measurement errors. In this paper, a variable-Kalman-gain method based on angle error that was calculated from acceleration signals was proposed to improve measurement accuracy. The proposed method was tested comparing to fixed-gain Kalman filter and a variable-Kalman-gain method that was based on acceleration magnitude used in previous studies. First, in angle measurement in treadmill walking, the proposed method measured lower limb angles with the highest measurement accuracy and improved significantly foot inclination angle measurement, while it improved slightly shank and thigh inclination angles. The variable-gain method based on acceleration magnitude was not effective for our Kalman filter system. Then, in angle measurement of a rigid body model, it was shown that the proposed method had measurement accuracy similar to or higher than results seen in other studies that used markers of camera-based motion measurement system fixing on a rigid plate together with a sensor or on the sensor directly. The proposed method was found to be effective in angle measurement with inertial sensors.

Dual mass MEMS gyroscope temperature drift compensation based on TFPF-MEA-BP algorithm

Sensor Review ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huiliang Cao ◽  
Rang Cui ◽  
Wei Liu ◽  
Tiancheng Ma ◽  
Zekai Zhang ◽  
...  
Keyword(s):  
Training Data ◽  
Experimental Result ◽  
Intrinsic Mode Functions ◽  
Temperature Drift ◽  
Connection Weight ◽  
Bp Network ◽  
Content Type ◽  
Mems Gyroscope ◽  
Time Frequency ◽  
Drift Compensation

Purpose To reduce the influence of temperature on MEMS gyroscope, this paper aims to propose a temperature drift compensation method based on variational modal decomposition (VMD), time-frequency peak filter (TFPF), mind evolutionary algorithm (MEA) and BP neural network. Design/methodology/approach First, VMD decomposes gyro’s temperature drift sequence to obtain multiple intrinsic mode functions (IMF) with different center frequencies and then Sample entropy calculates, according to the complexity of the signals, they are divided into three categories, namely, noise signals, mixed signals and temperature drift signals. Then, TFPF denoises the mixed-signal, the noise signal is directly removed and the denoised sub-sequence is reconstructed, which is used as training data to train the MEA optimized BP to obtain a temperature drift compensation model. Finally, the gyro’s temperature characteristic sequence is processed by the trained model. Findings The experimental result proved the superiority of this method, the bias stability value of the compensation signal is 1.279 × 10–3°/h and the angular velocity random walk value is 2.132 × 10–5°/h/vHz, which is improved compared to the 3.361°/h and 1.673 × 10–2°/h/vHz of the original output signal of the gyro. Originality/value This study proposes a multi-dimensional processing method, which treats different noises separately, effectively protects the low-frequency characteristics and provides a high-precision training set for drift modeling. TFPF can be optimized by SEVMD parallel processing in reducing noise and retaining static characteristics, MEA algorithm can search for better threshold and connection weight of BP network and improve the model’s compensation effect.

Research on Data Aggregation Algorithms Based on OPT in Wireless Sensor Networks

2013 ◽  
Vol 427-429 ◽  
pp. 1991-1994
Author(s):  
Xue Wen He ◽  
Le Ping Zheng ◽  
Kuan Gang Fan ◽  
Sun Han ◽  
Qing Mei Cao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Measurement Accuracy ◽  
Wireless Sensor ◽  
Data Traffic ◽  
Fusion Model ◽  
Design Goal ◽  
Improve Measurement ◽  
Battery Energy

Since wireless sensor networks consist of sensors with limited battery energy, a major design goal is to maximize the lifetime of sensor network. To improve measurement accuracy and prolong network lifetime, reducing data traffic is needed. In the clustering-based wireless sensor networks, a novel data aggregation algorithm based on OPT and Layida Method is proposed. In the proposed method, Layida Method preprocesses data and data fusion model for data integration are used. Its availability is proved by comparing with the results of two existing algorithms.

scholarly journals Application of Measurement Signal Reduction to Improve Measurement Accuracy

2018 ◽  
Vol 7 (4.36) ◽  
pp. 1035
Author(s):  
Rustem R. Ziyatdinov ◽  
Leisan R. Zakirova
Keyword(s):  
Measuring Channel ◽  
Operational Amplifiers ◽  
Degree Of Polarization ◽  
Measured Signal ◽  
Random Component ◽  
Temperature Drift ◽  
Measuring Device ◽  
Improve Measurement ◽  
Accuracy Of Measurements ◽  
Causes Of Errors

Most modern technical tasks require high precision measurements. To do this, it is necessary to analyze the causes of errors and take measures to reduce their influence on the accuracy of measurements. The causes of errors are very diverse and cannot always be identified. However, some systematic components of the measurement error can be described and calculated mathematically. In this case, the task of reducing the signal at the output of a measuring device to the form it would have when using an “ideal” device is reduced to calculating a certain linear operator which product to the measured signal allows obtaining the minimum systematic error. In this paper, the application of the reduction method is given by the example of a measuring instrument for the degree of polarization of light radiation which comprises three measuring channels for measuring the intensity of linearly polarized radiation. Each channel is built with the use of three operational amplifiers. The main errors of a measuring channel that can be described and determined are the errors of the operational amplifiers associated with the bias voltages and temperature drift. In real measuring systems there are much larger of such components. However, the use of computer equipment for modeling systems and processes, as well as measurements, removes all restrictions on the possibilities of processing the obtained data in a software way. With the help of computer technology it is possible to reduce the influence of perturbing effects and systematic errors, and also to eliminate gross errors. The random component of an error can be reduced by increasing the number of measurements and carrying out statistical data processing.   

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