| Summary: | 碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Power management strategy is very important in terms of the efficiency of data centers and the availability of information technology (IT) equipment. In order to determine the actual power consumption, accurate measurements of power supply unit’s attributes such as input/output voltage and current levels, temperatures and fan speeds are essential. Conventionally, output current measurement is realized using current shunt. Although it is simple to implement, current shunt dissipates quite an amount of power, and a tradeoff exists between precision and dissipated power. In addition, it suffers from temperature drift problem.
In this thesis, an artificial neural network (ANN)-based algorithm for estimating the output current information of a server power supply is proposed. The input data of the proposed ANN includes input voltage, input current, power factor, output voltage and ambient temperature. To generate a full coverage of the training data, an automatic testing platform (ATP) consists of a programmable power supply, a high accuracy power meter, two electronic loads, one data acquisition system and one thermal chamber is developed. Using the constructed ATP, training data of the proposed ANN can be generated efficiently.
According to the experimental results, the estimation error of the output current is less than 1.3 % in three tested cases. Therefore, the proposed technique can be utilized to estimate the output current of power supply. The advantages of the proposed method include high power efficiency, reduced board size and decreased bill of material cost from sensing circuitry.
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