An official website of the United States government
Traditionally, distribution system operators had limited visibility beyond distribution system substations. It was not unusual for electric utilities to have insufficient information about the network and phase connectivity model for the distribution system. This resulted in limited situational awareness at the distribution system level. In this paper, a visual analytics approach to gleaning intelligence from the vast amounts of data accumulated in the distribution system is proposed. The web-based visual analytics interface integrates data from heterogeneous datasets such as AMI, GIS and SCADA. The interface is designed to enable distribution system operators visualize and analyze the state of the distribution system over time. This paper presents the use of the visual analytics system to identify mismatched meter-to-transformer associations and to visualize voltage violations in a real-world distribution network.
more »
« less
You Can’t Protect What You Don’t Understand: Characterizing an Operational Gas SCADA Network
Natural gas distribution networks are part of a nation’s critical infrastructure, ensuring gas delivery to households and industries (e.g., power plants) with the correct chemical composition and the right conditions of pressure and temperature. Gas distribution is monitored and controlled by a Supervisory Control and Data Acquisition (SCADA) network, which provides centralized monitoring and control over the physical process.In this paper, we conduct the first openly available network measurement study of the SCADA network of an operational large-scale natural gas distribution network. With a total of 154 remote substations communicating through the SCADA system with a Control Room and over 98 days of observation, this is, to the best of our knowledge, the most extensive dataset of this kind analyzed to date.By combining the information obtained from engineering and IEC 104 network traffic, we reconstruct the gas distribution system’s layout, including the type and purpose of the substations and the physical properties of the gas that enters the SCADA system. Our analysis shows that it is possible to extract this information, essential for security monitoring, purely from the raw network traffic and without background knowledge provided by the control system engineers. We also note that configuration changes in SCADA environments, although probably less frequent than in IT environments, are not as rare and exceptional as the research community assumed.
more »
« less
- Award ID(s):
- 1929406
- PAR ID:
- 10397927
- Date Published:
- Journal Name:
- IEEE SafeThings 2022
- Page Range / eLocation ID:
- 243 to 250
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
The complexity of the power system has increased due to recent grid modernization and active distribution systems. As a result, monitoring and controlling modern power systems have become challenging. Dynamic security assessment (DSA) in power systems is a critical operational situational awareness (OpSA) tool for the energy control center (ECC). State-of-the-art (SOTA) DSA has been based on traditional state estimation utilizing the supervisory control and data acquisition (SCADA) / phasor measurement units (PMU) and transmission network topology processing (TNTP) based on SCADA monitoring of relay signals (TNTP-SMRS). Due to the slow data rates of SCADA, these applications cannot efficiently support an online DSA tool. Furthermore, an inaccurate network model based on TNTP-SMRS can lead to erroneous DSA. In this paper, a distributed dynamic security assessment (D-DSA) based on multilevel distributed linear state estimation (D-LSE) and efficient and reliable hierarchical transmission network topology processing utilizing synchrophasor network (H-TNTP-PMU) has been proposed. The tool can be used in real-time operation at the ECC of modern power systems. D-DSA architecture comprises three levels, namely Level 1 - component level security assessment (substations and transmission lines), Level 2 - area level security assessment, and Level 3 - network level security assessment. D-DSA concurrently evaluates all available substations’ security in the substation security assessment (SSA) and all available transmission lines’ security in the transmission line security assessment (TSA). Under the area security assessment (ASA), all SSA and TSA in each area are separately integrated to assess the area SSI (ASI-SSI) and TSI (ASI-TSI). Subsequently, each area’s area-level security index (ASI) is calculated by fusing ASI-SSI and ASI-TSI. At the network level security assessment, network SSI (NSI-SSI) and TSI (NSI-TSI) are estimated by fusing all ASI-SSIs and ASI-TSI, respectively. Network level security index (NSI) is estimated by fusing the NSISSI and NSI-TSI in network security assessment (NSA). Typical results of D-DSA are presented for two test systems, the modified two-area four-machine power system model and the IEEE 68 bus power system model. Results indicate that the proposed D-DSA can complete the assessment accurately at the PMU data frame rate, enabling online security assessment regardless of the network size.more » « less
-
Attack detection problems in industrial control systems (ICSs) are commonly known as a network traffic monitoring scheme for detecting abnormal activities. However, a network-based intrusion detection system can be deceived by attackers that imitate the system’s normal activity. In this work, we proposed a novel solution to this problem based on measurement data in the supervisory control and data acquisition (SCADA) system. The proposed approach is called measurement intrusion detection system (MIDS), which enables the system to detect any abnormal activity in the system even if the attacker tries to conceal it in the system’s control layer. A supervised machine learning model is generated to classify normal and abnormal activities in an ICS to evaluate the MIDS performance. A hardware-in-the-loop (HIL) testbed is developed to simulate the power generation units and exploit the attack dataset. In the proposed approach, we applied several machine learning models on the dataset, which show remarkable performances in detecting the dataset’s anomalies, especially stealthy attacks. The results show that the random forest is performing better than other classifier algorithms in detecting anomalies based on measured data in the testbed.more » « less
-
null (Ed.)Abstract—It is well known that physical interdependencies exist between networked civil infrastructures such as transportation and power system networks. In order to analyze complex nonlinear correlations between such networks, datasets pertaining to such real infrastructures are required. However, such data are not readily available due to their proprietary nature. This work proposes a methodology to generate realistic synthetic power distribution networks for a given geographical region. A network generated in this manner is not the actual distribution system, but its functionality is very similar to the real distribution network. The synthetic network connects high voltage substations to individual residential consumers through primary and secondary distribution networks. Here, the distribution network is generated by solving an optimization problem which minimizes the overall length of the network subject to structural and power flow constraints. This work also incorporates identification of long high voltage feeders originating from substations and connecting remotely situated customers in rural geographic locations while maintaining voltage regulation within acceptable limits. The proposed methodology is applied to the state of Virginia and creates synthetic distribution networks which are validated by comparing them to actual power distribution networks at the same location. Index Terms—synthetic distribution networks, radial networks, Mixed Integer Linear Programmingmore » « less
-
null (Ed.)Renewable energy sources such as solar and wind provide an effective solution for reducing dependency on conventional power generation and increasing the reliability and quality of power systems. Presented in this paper are design and implementation of a laboratory scale solar microgrid cyber-physical system (CPS) with wireless data monitoring as a teaching tool in the engineering technology curriculum. In the system, the solar panel, battery, charge controller, and loads form the physical layer, while the sensors, communication networks, supervisory control and data acquisition systems (SCADA) and control systems form the cyber layer. The physical layer was seamlessly integrated with the cyber layer consisting of control and communication. The objective was to create a robust CPS platform and to use the system to promote interest in and knowledge of renewable energy among university students. Experimental results showed that the maximum power point tracking (MPPT) charge controller provided the loads with power from the solar panel and used additional power to charge the rechargeable battery. Through the system, students learned and mastered key concepts and knowledge of multi-disciplinary areas including data sampling and acquisition, analog to digital conversion, solar power, battery charging, control, embedded systems and software programing. It is a valuable teaching resource for students to study renewable energy in CPS.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SPW54247.2022.9833864
