Engineering topics

Gentz, Reinhard

Publications and source records attributed to Gentz, Reinhard.

7 records

DOE OSTI2022

Differentially Private K -Means Clustering Applied to Meter Data Analysis and Synthesis

The proliferation of smart meters has resulted in a large amount of data being generated. It is increasingly apparent that methods are required for allowing a variety of stakeholders to leverage the data in a manner that preserves the privacy of the consumers. The sector is scrambling to define policies, such as the so called ‘15/15 rule’, to respond to the need. However, the current policies fail to adequately guarantee privacy. Here, in this paper, we address the problem of allowing third parties to apply K-means clustering, obtaining customer labels and centroids for a set of load time series by applying the framework of differential privacy. We leverage the method to design an algorithm that generates differentially private synthetic load data consistent with the labeled data. We test our algorithm’s utility by answering summary statistics such as average daily load profiles for a 2-dimensional synthetic dataset and a real-world power load dataset.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Supporting Cyber Security of Power Distribution Systems by Detecting Differences Between Real-time Micro-Synchrophasor Measurements and Cyber-Reported SCADA (Final Report)

As modern power grids tend towards greater levels of automation and communication, the challenges of identifying and mitigating vulnerabilities to cyber-attacks are ones that are increasingly demanding attention. Today’s power system has evolved to form the foundational bedrock of modern society, and an attack on this infrastructure could prove disastrous. In this project we were tasked to investigate the use of distribution synchrophasors as an independent isolated sensor network with which we can corroborate, or flag potentially spoofed,Supervisory Control And Data Acquisition (SCADA) data. We adapted an approach to marry the underlying physical properties of power systems with the network communications used by power systems in order to offer insights unattainable by either data stream isolation. While the concept of intrusion detection systems (IDS) is well understood for monitoring network traffic and traditional IT computing systems, the approach discussed in this report is motivated by several key notions: first, current SCADA communications alone presents an incomplete view of the grid. Second, the power grid, and the equipment controlling it, is grounded by laws of physics. Given this, we leverage high-frequency physical grid measurements to understand the physical condition of the grid, and combine this with SCADA. While high-frequency physical grid measurements and SCADA communication over Internet Protocol (IP) networks are fundamentally disparate information sources, when collectively examined through appropriate lenses, they offer a much more nuanced depiction of the grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

An Automated Disruption-Tolerant Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. This paper presents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Here, experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2019

An Automated, Disruption-Tolerant Device Authentication and Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. In this paper a novel Kerberos based framework for automated, disruption-tolerant key management for control system environments is presented. Experimental tests and their results are presented to quantify expected performance overhead of this approach. In addition, Zeek sensor analytics are presented to monitor the health and security of the key management framework operation.

Edgar, Thomas W.↗

DOE OSTI2019

Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization

Here, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations.

42 ENGINEERING↗

DOE OSTI2019

Learning Behavior of Distribution System Discrete Control Devices for Cyber-Physical Security

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. Finally, the proposed learning algorithms are validated on both simulated data and on measured PMU data from a utility pilot deployment site.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Detecting control system misbehavior by fingerprinting programmable logic controller functionality

In recent years, attacks such as the Stuxnet malware have demonstrated that cyberattacks against control systems cause extensive damage. These attacks can result in physical damage to the networked systems under their control. In this paper, we discuss our approach for detecting such attacks by distinguishing between programs running on a programmable logic controller (PLC) without having to monitor communications. Using power signatures generated by an attached, high-frequency power measurement device, we can identify what a PLC is doing and when an attack may have altered what the PLC should be doing. To accomplish this, we generated labeled data for testing our methods and applied feature engineering techniques and machine learning models. The results of this research demonstrate that Random Forests and Convolutional Neural Networks classify programs with up to 98% accuracy for major program differences and 84% accuracy for minor differences. Our results can be used for both online and offline applications.

97 MATHEMATICS AND COMPUTING↗