Real-time integrated computer interface and GMR current sensor of extended linear range for power quality measurement in building microgrid

Abstract

Global warming has become a worldwide concern. Ways to tackle it include the reduction of wastage in energy utilization and the more use of renewable energy sources. Smart Grid and microgrid are introduced recently to enable all these initiatives. Compared to the traditional electrical grid, smart grid/microgrid features two-way information flow between the electricity supplier and the users. It enables various means of energy saving, such as load-shedding and dynamic pricing. On the other hand, more and more distributed renewable energy sources and electric vehicle charging stations are connected to the microgrid. These systems are liable to cause power quality problems at the low-voltage electrical network. Microgrid can function effectively only when power quality monitoring is properly carried out. Power quality measurement is hence becoming more and more important in today’s world. In order to provide timely information for microgrid operations and control, a reliable communication link that is able to handle large amount of data flow is indispensable. To this researchers have developed many real-time power quality data compression algorithm to relax the demand on communication link. An integrated computer real-time interface is hence developed for a power quality data compression algorithm that works on hybrid sinusoidal and wavelet transform. The proposed interface forms a part of an advanced metering infrastructure with power quality monitoring functions. The interface is able to (1) display real-time power waveform and frequency information; (2) calculate power quantities for energy studies; and (3) extract power quality information from the compressed data without full decoding. Meanwhile, one of the key factors in power quality monitoring and building energy management is the flexibility and accuracy in current sensing. For building microgrid applications, features such as dynamic response and Home Area Network all require current measurements down to final circuits and individual appliances. In particular, DC measurement is necessary as power electronics are integral parts of the microgrid. A new current sensing element is necessary to cater for such applications. The current sensing element proposed in this thesis forms a part of a newly developed smart meter with power quality monitoring functions. A sensor based on giant magnetoresistance (GMR) effect is adopted which has the advantages of high sensitivity, low cost, wide frequency range, low power consumption, small size, and high compatibility. It is particularly suitable for building microgrid applications. A bipolar GMR current sensor is proposed to replace the traditional current sensing means, such as the Hall effect sensor and the current transformer. As GMR would be saturated at low magnetic field and has a unipolar output, the present work is to reduce the net magnetic flux at the point of GMR through counteracting the magnetic field by Helmholtz coils. The results show that the prototype is capable of measuring up to 45A, as compared to the original saturation limit of 4.5A. The feedback signal for reducing the net flux is proportional to the input current as long as the gain of the circuit is sufficiently large. By measuring the feedback current, the input current can be easily estimated by a resistor. However with a fixed gain of the amplifier, the linearity, frequency response, and power consumption of the current sensor is not optimized. A digital microcontroller is hence implemented to control the amplifier gain with respect to the input current. Extensive measurements have been conducted to the prototype design. It is shown that the frequency response range is from DC to 12 kHz, and the power consumption is from 1.6 W to 3.2 W, when the input current is from 0 A to 45 A, respectively.

Date of Award	15 Jul 2013
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Chung Fai Norman TSE (Supervisor) & Wing Hong Ricky LAU (Co-supervisor)