A Microgrid Monitoring System Over Mobile Platforms

Abstract:

 Real-time awareness of the phasor state, including the volatile frequency and phase angle, is critical to maintain reliable and stable operations of the power grid. However, the high cost and low accessibility of current synchrophasors restrict their large-scale deployment over highly distributed microgrids. In this paper, we present a practical system design for monitoring the microgrid frequency and phase angle over mobile platforms and significantly reduce the cost of such monitoring. Being different from current synchrophasors, our system does not rely on continuous GPS reception and hence it is highly accessible and applicable to heterogeneous microgrid scenarios. We develop various techniques to provide the timing signal that is necessary for precise microgrid monitoring. For frequency monitoring, the network time protocol is exploited for time synchronization. For phase angle monitoring which requires a higher timing accuracy, 200 Hz primary synchronization signal being embedded in the 4G LTE cellular signal is harvested for time synchronization. We implemented our system over off-the-shelf smartphones with a few peripheral hardware components and realized an accuracy of 1.7 MHz and 0.01 rad for frequency and phase angle monitoring, respectively. Although the accuracy of the prototype is lower than that of the GPS-based systems, the system could still satisfy the requirements of microgrid monitoring. The total cost of the system can be controlled within $100 and no installation cost is required. Experiment results compared with the traditional frequency disturbance recorders verify the effectiveness of our proposed system.

 

Existing System:

 

Current power grid monitoring systems allow direct measurement of frequency and phase angle by installing synchrophasors at either high-voltage transmission level [3] or low-voltage distribution level [4]. These power grid monitoring systems, although having been proved to be effective in wide-area power grid infrastructure, are generally considered unsatisfactory for monitoring the operating status of the newly emerging distributed power systems, so-called microgrids [5]. The decentralization of microgrids poses higher requirements on the installation cost and accessibility of power monitoring devices, and makes the current synchrophasors too expensive and inconvenient to be deployed into individual households in high volume. PMUs are deployed in substations and equipped with current transformer and power transformer for accessing the high voltage, which increases both manufacturing cost and the installation cost. For example, the installation cost of one transmission-level Phasor Measurement Unit (PMU) is more than $80,000 at the Tennessee Valley Authority (TVA). These PMUs are not intended to be used at the distributed consumer level, and require professional installation which reduces end-users’ incentives of having synchrophasors in their home energy systems.

 

Proposed System:

 

Our monitoring system consists of a voltage regulator module, a voltage transform circuit, a microprocessor-based analog-to-digital (AD) sampling module and an Android-based smartphone. The system design and implementation are shown in Fig. 2(a) and 2(b), respectively. The voltage regulator outputs the necessary DC power to power up the whole system, including the smartphone. An 8-bit microprocessor (MCU) ATmega328 (Arduino Uno board) is used to control the voltage sampling process through external AD Converter (ADC) at the sampling frequency of 1,440 Hz, and sends these raw voltage data to smartphone every 100 ms for phasor state estimation processing. The communication between the microprocessor and the smartphone is conducted by the USB host controller IC MAX3421E (USB host shield) [24]. Similar as being connected to the desktop PC, the smartphone behaves as USB slave in relation to the USB host chip, and can communicate with the MCU and be charged at the meantime. The PSS harvesting circuit, shown within the dotted line in Fig. 2(a), will be attached to the frequency monitoring system for phase angle monitoring. In the LTE-based phase angle monitoring, the PSS harvesting circuit will extract the PSS signals and transmit them to the MCU in the form of pulses. The rising edges of the pulses will be detected through External Interrupt (EI) in the MCU, and trigger new sampling Cycles.

 

Conclusion:

 

In this paper, we presented the design and implementation of a GPS-free smartphone-based microgrid monitoring system. Specifically, we designed a simple NTP-based frequency monitoring prototype with a few peripherals. In addition, we harvested the PSS signal in the 4G LTE radio as the synchronization signal to achieve phase angle monitoring. The proposed adaptive method will reduce the computational load at the smartphone with nearly no accuracy loss. The experiments compared with FDR devices verify the effectiveness of the system on both frequency and phase angle monitoring. The systematic cost of the prototype excluding the smartphone is controlled within $100, and no specialized installation is required, which would facilitate the massive deployment of the monitoring system. Our future work will focus on further improving the performance of PSS harvesting module, simplifying the power grid monitoring system, and integrating more functionalities of power grid monitoring onto mobile platforms.

 

Reference:

 

[1] F. Giraud and Z. M. Salameh, “Steady-state performance of a gridconnected rooftop hybrid wind-photovoltaic power system with battery storage,” IEEE Trans. Energy Convers., vol. 16, no. 1, pp. 1–7, Mar. 2001.

 

[2] IEEE Guide for Monitoring, Information Exchange, and Control of Distributed Resources Interconnected With Electric Power Systems, IEEE Standard 1547.3-2007, Nov. 2007.

 

[3] V. Venkatasubramanian, H. Schattler, and J. Zaborszky, “Fast timevarying phasor analysis in the balanced three-phase large electric power system,” IEEE Trans. Autom. Control, vol. 40, no. 11, pp. 1975–1982, Nov. 1995.

 

[4] Y. Liu et al., “Wide-area measurement system development at the distribution level: An FNET/GridEye example,” IEEE Trans. Power Del., Doi: 10.1109/TPWRD.2015.2478380.

 

[5] K. Tomsovic, D. E. Bakken, V. Venkatasubramanian, and A. Bose, “Designing the next generation of real-time control, communication, and computations for large power systems,” Proc. IEEE, vol. 93, no. 5, pp. 965–979, May 2005.

 

[6] A. Armenia and J. H. Chow, “A flexible phasor data concentrator design leveraging existing software technologies,” IEEE Trans. Smart Grid, vol. 1, no. 1, pp. 73–81, Jun. 2010.

 

[7] L. Zhan and Y. Liu, “Improved WLS-TF algorithm for dynamic synchronized angle and frequency estimation,” in Proc. IEEE PES Gen. Meeting Conf. Expo., National Harbor, MD, USA, Jul. 2014, pp. 1–5.

 

[8] W. Wang et al., “Highly accurate frequency estimation for FNET,” in Proc. IEEE Power Energy Soc. Gen. Meeting (PES), Vancouver, BC, Canada, Jul. 2013, pp. 1–5.

 

[9] I. Kamwa, S. R. Samantaray, and G. Joos, “Compliance analysis of PMU algorithms and devices for wide-area stabilizing control of large power systems,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 1766–1778, May 2013.

 

[10] L. Zhan, Y. Liu, J. Culliss, J. Zhao, and Y. Liu, “Dynamic single-phase synchronized phase and frequency estimation at the distribution level,” IEEE Trans. Smart Grid, vol. 6, no. 4, pp. 2013–2022, Jul. 2015.