Large amounts of distributed generation (DG), especially solar, and electric vehicles may cause technical problems related to power quality, safety and reliability on electric distribution circuits. These impacts are not yet well understood. Early evidence suggests that significant infrastructure upgrades and operational changes in utility distribution systems may be required as DG penetration levels reach a critical threshold. New empirical information gathered from the field is needed to better define these requirements and the potential need for new technology developments. Data from distribution circuits must be collected and analyzed at higher resolution and with greater specificity so that the effects of distributed resources can be better understood and mitigated if necessary.

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

There is significant interest in smart grid analytics based on phasor measurement data. One application is estima- tion of the Thevenin equivalent model of the grid from local measurements. In this paper, we propose methods using phasor measurement data to track Thevenin parameters at substations delivering power to both an unbalanced and balanced feeder. We show that for an unbalanced grid, it is possible to estimate the Thevenin parameters at each instant of time using only instantaneous phasor measurements. For balanced grids, we propose a method that is well-suited for online applications when the data is highly temporally-correlated over a short window of time. The effectiveness of the two methods is tested via simulation for two use-cases, one for monitoring voltage stability and the other for identifying cyber attackers performing "reconnaissance" in a distribution substation.

As the distribution grid moves toward a tightly monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. In this paper, focusing on microphasor measurement unit (μPMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. Due to the key role of the μPMU devices in our architecture, an optimal μPMU placement with limited number of sensors is also described that finds the best location of the devices with respect to our rules. The effectiveness of the proposed methods are tested through the synthetic and real μPMU data.

The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system operators has been widely documented. Micro-PMUs (µPMUs) are an emerging sensing technology that can provide similar benefits to Distribution System Operators (DSOs), enabling a level of visibility into the distribution grid that was previously unattainable. In order to support the deployment of these high resolution sensors, the automation of data analysis and prioritizing communication to the DSO becomes crucial. In this paper, we explore the use of µPMUs to detect anomalies on the distribution grid. Our methodology is motivated by growing concern about failures and attacks to distribution automation equipment. The effectiveness of our approach is demonstrated through both real and simulated data.

Electric power distribution systems are undergoing many technological changes and concerns are surfacing on possible additional vulnerabilities. Resilient cyber-physical systems (CPSs) in general must leverage state measures and operational models that interlink the physical and the cyber assets that compose them, to assess the global state. In this paper we describe a viable process of abstraction to obtain this holistic system state exploration tool, through the analysis of data from Micro Phasor Measurement Units (μPMUs) combined with the monitoring of Distribution Supervisory Control and Data Acquisition (DSCADA) traffic, and using semantics to interpret these data that expresses the specific system physical and operational constraints in both cyber and physical realms.

Conventional cyber-security intrusion detection systems monitor network traffic for malicious activity and indications that an adversary has gained access to the system. The approach discussed here expands the idea of a traditional intrusion detection system within electrical power systems, specifically power distribution networks, by monitoring the physical behavior of the grid. This is achieved through the use of high-rate distribution Phasor Measurement Units (PMUs), alongside SCADA packets analysis, for the purpose of monitoring the behavior of discrete control devices. In this work we present a set of algorithms for passively learning the control logic of voltage regulators and switched capacitor banks. Upon detection of an abnormal operation, the operator is alerted and further action can be taken. The proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.