Ciaran Roberts

Micro Synchrophasor-Based Intrusion Detection in Automated Distribution Systems: Toward Critical Infrastructure Security

Because electric power distribution systems are undergoing many technological changes, concerns are emerging about additional vulnerabilities that might arise. Resilient cyber-physical systems (CPSs) must leverage state measures and operational models that interlink their physical and cyber assets, to assess their global state. Here, the authors describe a viable process of abstraction to obtain this holistic state exploration tool by analyzing data from micro-phasor measurement units (μPMUs) and monitoring distribution supervisory control and data acquisition (DSCADA) traffic. To interpret the data, they use semantics that express the specific physical and operational constraints of the system in both cyber and physical realms.

Synchrophasor data analytics in distribution grids

The deployment of high-fidelity, high-resolution sensors in distribution systems will play a key role in enabling increased resiliency and reliability in the face of a changing generation landscape. In order to leverage the full potential of such a rich dataset, it is necessary to develop an analytics framework capable of both detecting and analyzing patterns within events of interest. This work details the foundation of such an infrastructure. Here, we present an algorithm for detecting events, in the form of edges in voltage magnitude time series data, and an approach for clustering sets of events to reveal unique features that distinguish different events from one another (e.g. capacitor bank switching from transformer tap changes). We test the proposed infrastructure on distribution synchrophasor data obtained from a utility in California over a one week period. Our results indicate that event detection and clustering of archived data reveals features unique to the operation of voltage regulation equipment. The chosen data set particularly highlights the value of the derivative of the localized voltage angle as a distinguishing feature.

Estimating Behind-the-meter Solar Generation with Existing Measurement Infrastructure: Poster Abstract

Real-time PV generation information is crucial for distribution system operations, in particular for switching operations, state-estimation, and management of the voltage at the point of provision. However, most behind-the-meter solar installations are not monitored. Typically, the only information available to the distribution system operator is the installed capacity. Our main purpose in this study is to estimate behind-the-meter PV generation using data from smart meters.

Using Micro-Synchrophasor Data for Advanced Distribution Grid Planning and Operations Analysis

Author(s): Stewart, Emma; Kiliccote, Sila; McParland, Charles; Roberts, Ciaran | Abstract: This report reviews the potential for distribution-grid phase-angle data that will be available from new micro-synchrophasors (µPMUs) to be utilized in existing distribution-grid planning and operations analysis. This data could augment the current diagnostic capabilities of grid analysis software, used in both planning and operations for applications such as fault location, and provide data for more accurate modeling of the distribution system. µPMUs are new distribution-grid sensors that will advance measurement and diagnostic capabilities and provide improved visibility of the distribution grid, enabling analysis of the grid’s increasingly complex loads that include features such as large volumes of distributed generation. Large volumes of DG leads to concerns on continued reliable operation of the grid, due to changing power flow characteristics and active generation, with its own protection and control capabilities. Using µPMU data on change in voltage phase angle between two points in conjunction with new and existing distribution-grid planning and operational tools is expected to enable model validation, state estimation, fault location, and renewable resource/load characterization. Our findings include: data measurement is outstripping the processing capabilities of planning and operational tools; not every tool can visualize a voltage phase-angle measurement to the degree of accuracy measured by advanced sensors, and the degree of accuracy in measurement required for the distribution grid is not defined; solving methods cannot handle the high volumes of data generated by modern sensors, so new models and solving methods (such as graph trace analysis) are needed; standardization of sensor-data communications platforms in planning and applications tools would allow integration of different vendors’ sensors and advanced measurement devices. In addition, data from advanced sources such as µPMUs could be used to validate models to improve/ensure accuracy, providing information on normally estimated values such as underground conductor impedance, and characterization of complex loads. Although the input of high-fidelity data to existing tools will be challenging, µPMU data on phase angle (as well as other data from advanced sensors) will be useful for basic operational decisions that are based on a trend of changing data.

Autopsy on active distribution networks: A data-driven fault analysis using micro-PMU data

In this paper, we conduct a data-driven experimental analysis on a single-phase-to-neutral fault at a distribution grid in Riverside, CA using data from five distribution level phasor measurement units, a.k.a, Micro-PMUs. Of particular interest is to extract the time-line during the fault. With the high resolution, precision, and time synchronization of the data from Micro-PMUs, the hypothesis about optimal operation of protection devices during each period of the fault time-line is examined, followed by exploring the success of coordination between lateral fuse and main feeder recloser. Also, this paper studies the transient effect of fault on the load-level as well as feeder-level. In addition, the response of inverter based resources to fault, specifically to islanding, is observed and the miscoordination between anti-islanding protection of PV inverters and the feeder recloser is deduced. Finally, the effect of the fault on outlying area feeder-level and customer-level is investigated. This paper takes a first step in using micro-PMU data to conducting a detailed analysis, an autopsy, of how different voltage levels are affected by fault switching events in distribution systems.

Improving distribution network model accuracy using impedance estimation from micro-synchrophasor data

An accurate network model is essential for performing detailed analysis of a power system. The quality of many distribution network models is very diverse, especially for low voltage (LV) networks. To help identify areas where the model is incomplete or incorrect, Micro Phasor Measurement Units (μPMUs) can be integrated into a network. These μPMUs would work together, with a trusted cloud back-end system. The basis for this paper is to determine how the data collected by μPMUs can be used, and what can be calculated from this data to help recognize areas where the network model is inaccurate and may need resurveyed. As a preliminary investigation to determine the feasibility of the approach, this paper discusses the calculation of the impedance of both a transformer and line, and compares the values obtained from μPMU data to the level of value expected on the network.

A data-driven analysis of lightning-initiated contingencies at a distribution grid with a PV farm using Micro-PMU data

In this paper, we conduct a data-driven experimental analysis of lightening-induced contingencies at a distribution grid in Riverside, CA, using data from three distribution level phasor measurement units, a.k.a, Micro-PMUs. The data was collected during four hours of a rainy day with several lightening strikes on October 24, 2016. Of particular interest was to analyze the impact and the response of a 7.5 MW PV farm. Due to the use of three Micro-PMUs, including one in an outlying area, we are able to distinguish system-wide events across the sub-transmission network against local events at the PV farm and its associated substation. Multiple interesting observations are made and the related causes are discussed. For example, based on the analysis of phase angle difference data, we observe that during at least one of the lightening events, there was a reverse power flow from the PV site to the substation due to a transient short-circuit caused by the surge arresters. This paper takes a first step in using Micro-PMU data to conduct a detailed analysis of how distributed energy resources (DERs) could be affected and and/or respond to the lightening-induced contingencies in distribution systems.

Automated Anomaly Detection in Distribution Grids Using uPMU Measurements

Automated Anomaly Detection in Distribution Grids Using µ PMU Measurements Mahdi Jamei ∗ , Anna Scaglione ∗ , Ciaran Roberts † , Emma Stewart † , Sean Peisert † , Chuck McParland † , Alex McEachern ‡ , ∗ School of Electrical, Computer, and Energy Engineering, Arizona State University, Tempe, AZ, USA † Lawrence Berkeley National Laboratory, Berkeley, CA, USA ‡ Power Standards Laboratory, Alameda, CA, USA Abstract—The impact of Phasor Measurement Units (PMUs) for providing situational awareness to transmission system op- erators 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. Index Terms—Intrusion Detection, Anomaly Detection, Micro- Phasor Measurement Unit, Distribution Grid I. I NTRODUCTION The state vectors of the transmission grid are closely monitored and their physical behavior is well-understood [1]. In contrast, Distribution System Operators (DSOs) have historically lacked detailed real-time actionable information about their system. This, however, is set to change. As the distribution grid shifts from a demand serving network towards an interactive grid, there is a growing interest in gaining situational awareness via advanced sensors such as Micro- Phasor Measurement Units (µPMUs) [2]. The deployment of the µPMUs in isolation without addi- tional data driven applications and analytics is insufficient. It is critical to equip DSOs with complimentary software tools that are capable of automatically mining these large data sets in search of useful, actionable information. There has been a lot of work focused on using PMU data at the transmission level to improve Wide-Area Monitoring, Protection and Control (WAMPC) [3], [4]. The distribution grid, however, is lagging in this respect. Due to inherent differences between operational behavior, such as imbalances and increased variability on the distribution and transmission grid, the algorithms derived for WAMPC at the transmission level are generally not directly applicable at the distribution level. Our work is aimed at addressing this issue. We focus on an important application of µPMU data in the distribution system: anomaly detection, i.e., behavior that differs significantly from normal operation of the grid during (quasi) steady-state. An anomaly can take a number of forms, including faults, misoperations of devices or switching transients, among others, and its root cause can be either a natural occurrence, error or attack. The risk of cyber-physical attacks via an IP network has recently gained significant interest due to the increase in automation of our power gird via two-way communication. This communication is typically carried out on breachable networks that can be manipulated by attackers [5]. Even if an anomaly naturally occurs, it is important to notify the DSO to ensure proper remedial action is taken. A. Related Work The majority of published work in anomaly detection using sensor data, primarily SCADA and PMU data, has focused on the transmission grid. The proposed methods are typically data-driven approaches, whereby the measurements are in- spected for abnormality irrespective of the underlying physical model. One such example, the common path data mining approach implemented on PMU data and audit logs at a central server, is proposed in [6] to classify between a disturbance, an attack via IP computer networks and normal operation. Chen et al., [7] derive a linear basis expansion for the PMU data to reduce the dimensionality of the measurements. Through this linear basis expansion, it is shown how an anomaly, which changes the grid operating point, can be spotted by comparing the error of the projected data onto the subspace spanned by the basis and the actual values. Valenzuela et al., [8] used Principal Component Analysis (PCA) to classify the power flow results into regular and irregular subspaces. Through analyzing the data residing in the irregular subspace, their method determines whether the irregularity is caused by a network attack or not. Jamei et al., [9] propose an intrusion detection architecture that leverages µPMU data and SCADA communication over IP networks to detect potentially damag- ing activities in the grid. These aforementioned algorithms are all part of the suite of machine learning techniques that the security monitoring architecture will rely on. B. Our Contribution µPMUs, due to their high sampling frequency, are a much richer data source in comparison to traditional Distribution Supervisory Control and Data Acquisition (DSCADA). In this

Transformer monitoring using Kalman filtering

In this paper, we present a systems-theoretic approach to real-time in-situ monitoring of operating transformers. The most significant and novel result is the estimation of partial discharge buildup in transformers. In addition, it is capable of calculating secondary side power factor, and detecting voltage fluctuations, reactive buildup and core saturation. The paper discusses critical design considerations such as sampling time, model excitation, and system order. Concerns regarding power quality, reliability and resilience are increasing in the distribution grid with the injection of power from renewables. The algorithm presented here could help mitigate these by continuously monitoring transformer health and performance during operation.

Validation of the Ornstein-Uhlenbeck process for load modeling based on µPMU measurements

This paper investigates the suitability of the Ornstein-Uhlenbeck process, driven by various Lévy processes, for load modeling at the distribution network level. An indepth description outlining the procedure for estimating the required parameters is given. Both the statistical properties of the simulated processes and its auto-correlation is compared to that of the field measured data to demonstrate the suitability of the proposed methodology. The development of such stochastic models is facilitated by measures obtained from micro-synchrophasors (μPMUs). The data from these devices serves to demonstrate the need to model the volatility along with validating a model attempting to model said volatility.