# Research

## Phasor Measurement Unit

A Phasor Measurement Unit (PMU) for an AC electricity grid typically measures voltage and current signals to compute the respective phasors, characterized by an amplitude and an angle, synchronized based on GPS clock. A typical installation of a PMU on a transmission line is depicted in the figure on the left obtained from the article on "Low-Cost Microcontroller-Based Phasor Measurement Units Improve Smart Grid Reliability" hosted by DigiKey.

The use of a common GPS clock for each PMU allows measurements from different locations and utilities to be time-aligned (synchronized) to providing a correlated view of the entire grid connection at any given time. A PMU also measures AC frequency and rate of change of frequency. In terms of accuracy specification, a PMU is required to estimate phase angle with an accuracy of one microsecond and requirements to meet IEEE C37.118.1 standards. PMUs can generate a lot of data - with three phase phasors, real/reactive power and frequency (rate of change) a PMU easily produces more than a dozen signals at 60Hz sampling rates. More details on the use of PMUs in electricity grid can also be found by following the North American SynchroPhasor Initiative (NASPI) website.

## Main Contributions

Extracting information out of PMU data for the purpose of monitoring and automating power flow and stability of the electricity grid is an important application for the use of PMU data. The SyGMA lab is a key player in the emerging technology on electric grid instrumentation and the research in this lab develops new data processing, modeling and model validation applications based on synchrophasor measurements for advanced grid monitoring and automatic control of electric networks. Our main contributions include:

### Automatic event detection driven by PMU data

Based on optimal filtering of phasor data, residual signal are computed in real-time that monitor changes in the phasor due to changes in AC frequency and/or Voltage or Current amplitude. The optimal filtering is computed via an off-line optimization of filter coefficient that are estimated based on data obtained from a PMU when no apparent disturbance was present on the PMU data.

### Dynamic modeling and model validation of power flow based on PMU measurements

With a PMU in place and a particular event of a power drop or power increase detected by the event detection algorithm, the transients in the power flow can be observed and modeled. Power flow fluctuations typically have an oscillatory behavior and for modeling purposes, both the frequency of oscillation, the damping of the oscillation and its participation factor are important parameters. The SyGMA lab has developed automatic signal analysis tools based on a realization-based method that can characterize the oscillation frequency and damping parameters without user intervention. The realization based method uses simple linear algebra techniques (Singular Value Decomposition) to facilitate computations.

### Dynamic control and damping of power oscillations via active inverter control

When power oscillations can be measured with a PMU and a dynamic model can be formulated using the above described realization methods, then it will be possible to actively control those power oscillations. This is only possible when an inverter is available that can handle the control signals needed to damped power oscillations, while the actual control algorithms would have to use feedback measurements from a PMU device to react to power oscillations. The SyGMA lab was funded by the California Energy Comissions (CEC) to demonstrate such power damping control on a small scale inverter coupled to a PhotoVoltaic system. Demonstration with a high power DC output driving a controllable Grid Tied Inverter provided by One Cycle Control and three phase voltage/current measurement implemented on a NI myRIO shows a significant damping of power oscillations.

### Synchrophasor data quality and data compression validation

With three phase phasors, real/reactive power and frequency (rate of change), a PMU easily produces more than a dozen signals at 60Hz sampling rates. Storing such signals for long period of times will put strain on the data archiving system that has to store high throughput and time synchronozed measurements. To alleviate data storage requirements, the OSIsoft PI system uses data compression based on the "swinging door" mechanism in which data points are only stored if subsequent measurements deviate from a straight line with a predefined accuracy requirement. The SyGMA lab investigated the "data loss " due to this "lossy" data compression technique and has shown little to no effect on the data quality, provided the correct accuracy is used that resembles the noise level of your measurement.

Future projects of the SyGMA lab will include state estimation problems, equipment monitoring using PMU data and the monitoring and controlled islanding conditions of microgrid by active feedback control of inverters based on PMU measurements. More details on current research contributions can also be found in the publications tab of this website.