The integration of Information and Communications Technology (ICT) enables real-time communication for smart meters to participate in power system operations. However, Advanced Metering Infrastructures (AMI) are vulnerable to cyber attacks. Both utilities and power consumers may become victims of cyber intrusions. In this paper, a two-stage cyber intrusion protection system is proposed. At the first stage of intrusion detection, a Support Vector Machine (SVM) is used as a detection algorithm to discover suspicious behaviors inside a smart meter. At the second stage, the Temporal Failure Propagation Graph (TFPG) technique is used to generate attack routes for identifying attack events. Finally, the proposed pattern recognition algorithm is used to calculate the similarity between a detected abnormal event and pre-defined cyber attacks. A higher similarity value implies a higher chance that a smart meter is under attack. An AMI security test platform has been developed to: (1) Collect training/testing data for SVM, (2) Simulate and analyze cyber attack events, and (3) Validate the proposed cyber attack protection system. The test platform consists of Network-Simulator 3 (NS-3) software to simulate an AMI network environment and single board computers (SBCs) to emulate the IEEE 802.15.4 communication between a grid router and a smart meter.

Today’s power distribution system is changing to a power-electronics-enabled distribution system, especially with the increasing penetration of distributed energy resources (DERs). To monitor and manage those electronic devices and DERs at the grid edge, the advanced metering infrastructure (AMI) with two-way communications presents great potential. At present, extensive research explores the upstream communication from smart meters to electric utilities (e.g., meter reading) but few examine the downstream communication from the utilities to smart meters (e.g., meter pinging). This paper discusses the AMI two-way communication and its recent industrial practice in the U.S., especially for applying the smart meter pinging functionality to monitor grid-edge devices and DERs. This paper then develops the two-way communication model and the network calculus method to quantify the impact of the two-way communication on the AMI network. In the end, the proposed method is validated with ns-3 simulation using the modified 13-node test feeder and real-world feeder systems.

A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.

Power distribution systems are traditionally characterized using passive means such as parameter estimation and topology identification. This work investigates an active way to test and characterize dynamically changing distribution systems. The method actively perturbs local distribution networks with programmable pulses using a Grid Resonance Probe (GRP) and observes the response using a micro-phasor measurement unit (μPMU). After applying advanced data analytics, the perturbation and response measurements can reveal characteristics of distribution systems, such as feeder parameters and topologies. To validate the proposed approach, three case studies are executed on a real distribution system, including visualization, feeder connection identification, and feeder impedance estimation. Real data processing considerations and sensitivity analysis are also discussed. The proposed testing and analysis approach is expected to provide engineers a new solution to measure, parameterize, and characterize distribution systems.

The U.S. Department of Energy's Office of Scientific and Technical Information