MEMS gyroscopes are widely used in the underwater vehicles owing to their excellent performance and affordable costs.However,the temperature sensitivity of the sensor seriously affects measurement accuracy.Therefore,i...MEMS gyroscopes are widely used in the underwater vehicles owing to their excellent performance and affordable costs.However,the temperature sensitivity of the sensor seriously affects measurement accuracy.Therefore,it is significantly to accurately identify the temperature compensation model in this paper,the calibration parameters were first extracted by using the fast calibration algorithm based on the Persistent Excitation Signal Criterion,and then,MEMS gyro temperature compensation model was established by utilizing the thin plate spline interpolation method,and the corresponding identification results were compared with the results from the polynomial fitting method.The effectiveness of the proposed algorithm has been validated through the comparative experiment.展开更多
Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monit...Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monitoring and close-loop control applications. However, the PMUs data quality issue affects applications based on PMUs a lot. This paper proposes a simple yet effective method for recovering PMU data. To simply the issue, two different scenarios of PMUs data loss are first defined. Then a key combination of preferred selection strategies is introduced. And the missing data is recovered by the function of spline interpolation. This method has been tested by artificial data and field data obtained from on-site PMUs. The results demonstrate that the proposed method recovers the missing PMU data quickly and accurately. And it is much better than other methods when missing data are massive and continuous. This paper also presents the interesting direction for future work.展开更多
文摘MEMS gyroscopes are widely used in the underwater vehicles owing to their excellent performance and affordable costs.However,the temperature sensitivity of the sensor seriously affects measurement accuracy.Therefore,it is significantly to accurately identify the temperature compensation model in this paper,the calibration parameters were first extracted by using the fast calibration algorithm based on the Persistent Excitation Signal Criterion,and then,MEMS gyro temperature compensation model was established by utilizing the thin plate spline interpolation method,and the corresponding identification results were compared with the results from the polynomial fitting method.The effectiveness of the proposed algorithm has been validated through the comparative experiment.
基金supported in part by National Natural Science Foundation of China(NSFC)(51627811,51707064)Project Supported by the National Key Research and Development Program of China(2017YFB090204)Project of State Grid Corporation of China(SGTYHT/16-JS-198)
文摘Nowadays, the technology of renewable sources grid-connection and DC transmission has a rapid development. And phasor measurement units(PMUs) become more notable in power grids, due to the necessary of real time monitoring and close-loop control applications. However, the PMUs data quality issue affects applications based on PMUs a lot. This paper proposes a simple yet effective method for recovering PMU data. To simply the issue, two different scenarios of PMUs data loss are first defined. Then a key combination of preferred selection strategies is introduced. And the missing data is recovered by the function of spline interpolation. This method has been tested by artificial data and field data obtained from on-site PMUs. The results demonstrate that the proposed method recovers the missing PMU data quickly and accurately. And it is much better than other methods when missing data are massive and continuous. This paper also presents the interesting direction for future work.
