研究目的
To develop a smart LED driver that adjusts electricity usage in response to utility commands without compromising human visual comfort, addressing the instability in power systems due to renewable energy integration.
研究成果
The proposed control method effectively reduces light flickering by controlling the rate of change of LED power based on human visual perception, making smart lighting systems viable for demand response in smart grids with high renewable energy penetration. Future work could explore broader applications and optimizations.
研究不足
The study is limited to specific LED types and power levels; results may vary with different lighting conditions or user demographics. The experimental setup uses a controlled light chamber, which may not fully represent real-world environments. The control method's effectiveness depends on the accuracy of the human perception model derived from the survey.
1：Experimental Design and Method Selection:
The study involves designing a smart LED driver with a two-stage power converter (PFC and DC/DC converters) and a new control method using input-feed forward control and slew rate control to manage power changes.
2：Sample Selection and Data Sources:
A survey is conducted with 17 interviewees (ages 23-40) in a light chamber to assess human eye response to light intensity changes. The LED lamp and driver specifications are used.
3：List of Experimental Equipment and Materials:
Includes an LED lamp (Philips Fortimo LED DLM Module 2000 32W/840), LED driver (TI UCC28810EVM-002 modified with TMS320F28069 DSP), inductors (L1: 1mH, L2: 400μH), capacitors (C1: 82μF, C2:
4：5μF), and a light chamber (8m x 7m x 2m). Experimental Procedures and Operational Workflow:
The LED driver is tested under ramp and step input functions. Visual assessments involve interviewees pressing a button when they notice light changes, with data recorded on input power, DC voltage, control signal, brightness, and trigger pulses. The slew rate control is activated and deactivated in different tests.
5：Data Analysis Methods:
Data from assessments are analyzed to determine the average number of trigger pulses, and mathematical models (e.g., equations for power and voltage changes) are used to validate the control method.
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