The moving-average-filter-based quasi-type-1 phase-locked loop (MAF-QT1 PLL) can provide zero steady-state phase error in the presence of frequency drifts, achieving a high filtering capability. However, in the presence of an MAF and frequency drift, MAF-QT1 PLL cannot achieve a fast transient response and frequency-dependent characteristic under adverse grid conditions. To overcome this drawback of the MAF-QT1 PLL, this paper proposes a frequency-adaptive improved moving-average-filter-based quasi-type-1 PLL (FAIMAF-QT1 PLL). Aiming at the shortcoming of the slow dynamic response of the MAF-QT-1 PLL, a correction link is introduced, and an improved moving-average-filter-based quasi-type-1 PLL (IMAF-QT1 PLL) is obtained. To improve the anti-interference ability of the IMAF-QT1 PLL in response to grid frequency changes, a FAIMAF-QT1 PLL and a corresponding digital implementation scheme are proposed. The regulator parameter setting method is proposed based on the small-signal model of the FAIMAF-QT1 PLL. Simulation and experimental results show that the FAIMAF-QT1 PLL can accurately track the grid voltage phase in the presence of power grid frequency fluctuations, phase angle jumps, distorted harmonic injections and unbalanced voltage drops and has good steady-state and dynamic performance.

The traditional weighing and selling process of non-barcode items requires manual service, which not only consumes manpower and material resources but is also more prone to errors or omissions of data. This paper proposes an intelligent self-service vending system embedded with a single camera to detect multiple products in real-time performance without any labels, and the system realizes the integration of weighing, identification, and online settlement in the process of non-barcode items. The system includes a self-service vending device and a multi-device data management platform. The flexible configuration of the structure gives the system the possibility of identifying fruits from multiple angles. The height of the system can be adjusted to provide self-service for people of different heights; then, deep learning skill is applied implementing product detection, and real-time multi-object detection technology is utilized in the image-based checkout system. In addition, on the multi-device data management platform, the information docking between embedded devices, WeChat applets, Alipay, and the database platform can be implemented. We conducted experiments to verify the accuracy of the measurement. The experimental results demonstrate that the correlation coefficient R2 between the measured value of the weight and the actual value is 0.99, and the accuracy of non-barcode item prediction is 93.73%. In Yangpu District, Shanghai, a comprehensive application scenario experiment was also conducted, proving that our system can effectively deal with the challenges of various sales situations.

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