The recent advent of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, has introduced a wide range of new applications in power distribution systems. A sub-class of such emerging applications are called event-based methods. These methods focus on the analysis of events in the stream of micro-PMU measurements to achieve situational awareness, enhance load modeling, integrate distributed energy resources, etc. In this paper, we explore a scenario, where a cyberattack compromises the micro-PMU measurements during an event. Such a targeted attack could be limited in scope but result in a major impact on the operation of the power grid by highly deviating the outcome of the event-based methods. First, we investigate and model two types of such attacks, eventunsynchronized (basic) attacks and event-synchronized (advanced) attacks. We then conduct a geometric analysis to understand each attack type, in a setting where the events are represented in the phasor domain in a differential mode. Next, we introduce a novel method to detect the presence of the attack and then identify which micro-PMUs are compromised so as to discard the compromised measurements as a defense mechanism. The proposed approach makes critical use of magnitude as well as phase angle measurements from micro-PMUs. The method is tested on the IEEE 33-bus power distribution test system.
This paper investigates cyber-attacks against distribution-level phasor measurement units, a.k.a., micro-PMUs. The focus is on a specific use case of micro-PMUs for locating the source of events in distribution systems. A method is proposed to detect the attack, based on two different detection criteria. Furthermore, a novel optimization-based algorithm is developed to identify which micro-PMU(s) are compromised. Importantly, the proposed attack detection and identification methods do not require prior knowledge on the number and location of affected micro-PMU(s). The proposed methods and algorithms are test through computer simulations on the IEEE 33-bus test system.
A novel method is proposed to address the issue of low-observability in Distribution System State Estimation (DSSE). We first use the historical data at the unobservable locations to construct and train proper Generative Adversarial Network (GAN) models to compensate for lack of direct real-time measurements. We then integrate the trained GAN models, together with the direct synchronized measurements at the observable locations, into the formulation of the DSSE problem. In this regard, we simultaneously take advantage of the forecasting capabilities of the GAN models, the available real-time synchronized measurements, and the DSSE formulations based on physical laws in the power system. As a result, on one hand we conduct a physics-conditioned estimation of the unknown power injections at the unobservable locations; and on the other hand, we also achieve a complete DSSE solution for the understudy low-observable active power distribution system.
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