Event Detection in Micro-PMU Data: A Generative Adversarial Network Scoring Method

A new data-driven method is proposed to detect events in the data streams from distribution-level phasor measurement units, a.k.a., micro-PMUs. The proposed method is developed by constructing unsupervised deep learning anomaly detection models; thus, providing event detection algorithms that require no or minimal human knowledge. First, we develop the core components of our approach based on a Generative Adversarial Network (GAN) model. We refer to this method as the basic method. It uses the same features that are often used in the literature to detect events in micro-PMU data. Next, we propose a second method, which we refer to as the enhanced method, which is enforced with additional feature analysis. Both methods can detect point signatures on single features and also group signatures on multiple features. This capability can address the unbalanced nature of power distribution circuits. The proposed methods are evaluated using real-world micro-PMU data. We show that both methods highly outperform a state-of-the-art statistical method in terms of the event detection accuracy. The enhanced method also outperforms the basic method

Unsupervised Data-Driven Event Analysis of Smart Grid Time-Series

There have been major advancements in recent years to enhance situational awareness in power distribution systems by using advanced sensor technologies. Smart meters and distribution-level phasor measurement units (D-PMUs) are among the most common sensors that have been deployed recently in power distribution networks. Included in the captured time-series of the measurements from these sensors, there are ``events'', that are generally unscheduled, infrequent, and often unknown in their type and nature. Therefore, in practice, we often do not have any prior knowledge about the events until they occur. In this regard, such sensor measurements can be seen as time series that are in form of unlabeled data. Accordingly, in this thesis, we address the analysis of events in the selected types of smart grid time series by using unsupervised machine learning. We start by the analysis of time series in smart meter data to extract events and abnormalities. Our analysis also includes extracting proper choices of features and methods. Next, we move to the analysis of the time series data from D-PMUs that have a much higher resolution and carry more information than the measurements from smart meters as they also measure phase angles. Accordingly, three versions of unsupervised event detection methods are developed, which work based on generative adversarial networks and deep recurrent neural networks. These methods, specifically focused on high frequency and small time series windows of one D-PMU data. Results based on real-world sensor data show that by learning normal behaviour of the system via the proposed methods, we can extract the events more accurately compared to the prevalent methods. Subsequently, a two-step unsupervised clustering method is also proposed, which works based on a linear mixed integer programming formulation to cluster events in time series from D-PMUs. Finally, to address the task of unsupervised event clustering for a situation within a low observable distribution system with only a handful of available D-PMUs, a novel unsupervised graph representation learning model is developed. The developed unsupervised clustering model, extracts the time domain features from the time series in fundamental and harmonics phasor measurements, and then it takes advantage of the system topology by using graph learning models to separate, characterize, and classify the events

Event-Based Analysis of Solar Power DistributionFeeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event's source region by an impedance-based analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locally-induced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.Comment: 5 pages, 6 figures, IEEE ISGT202