Mr. Yong Zhu Profile

Yong Zhu

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Proceedings Article | 15 January 2024 Paper

Design and implementation of high-precision power harmonics detection system based FFT-FT

Yongwei Tao, Yong Zhu, Ge Chen

Proceedings Volume 12983, 129831V (2024) https://doi.org/10.1117/12.3017884

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This paper proposed a high-precision and high-order power voltage harmonics detection method based on STM32H7. The system which uses STM32H7 as the core utilizes FFT-FT algorithm to find the peak of panoramic spectrum to further refine the spectrum and compensate amplitude error, so as to achieve high precision real-time power voltage harmonics measurement. The input voltage is attenuated, amplified whose gain is programmed, and input into STM32 MCU after digital-to-analog. Then, the harmonic amplitude is measured through searching the fundamental frequency after the FFT of voltage signal. This system adopts one-dimensional linear search algorithm to improve its real-time performance. The actual test results show the absolute error is less than or equal to 0.01Hz and the relative error is less than 0.03% for the frequency measurement in the range of 40~70Hz AC voltage. And the harmonic amplitudes’ absolute error of rectangular wave is less than or equal to 4mV. In particular, the relative error of the fundamental wave and the third harmonic is less than 0.5%, and the absolute error of the 63rd harmonic is only 1mV. Therefore, the system has the characteristics of high-orders of harmonic measurement, high precision of frequency and amplitude measurement, which can meet the application requirements of high precision and high order harmonics measurement in power system.

Proceedings Article | 8 March 2023 Paper

Meta-learning-based few-shot identification for novel loads

Bin Liu, Zhukui Tan, Zhongxiao Cong, Yong Zhu, Jin Li

Proceedings Volume 12586, 1258618 (2023) https://doi.org/10.1117/12.2670309

KEYWORDS: Machine learning, Data modeling, Feature extraction, Statistical modeling, Power grids, Deep learning, Matrices, Power consumption

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In recent years, load identification technology has received great attention as the value of real-time load-side electricity information has gradually emerged. There are several ways to precisely identify the different types of loads. However, practical situations with novel load types and little labeled data are seldom considered. For this reason, this paper proposes a few-shot identification method for novel loads based on the Model-Agnostic Meta-Learning (MAML). It uses the Adaptive Weighted Recurrence Graphs (AWRG) model as the base learner, which has the best performance in load identification, and pre-trains the model with existing data. The proposed method uses meta-training to get initial parameters that are generalized across multiple load types to improve the learning ability of the model on few-shot tasks with novel loads. Compared with transfer learning methods commonly used for generalized load identification, the results on the WHITED dataset show that the proposed method can improve the scalability of the load identification for practical applications.

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