A Backend Framework for the Efficient Management of Power System Measurements

Increased adoption and deployment of phasor measurement units (PMU) has provided valuable fine-grained data over the grid. Analysis over these data can provide insight into the health of the grid, thereby improving control over operations. Realizing this data-driven control, however, requires validating, processing and storing massive amounts of PMU data. This paper describes a PMU data management system that supports input from multiple PMU data streams, features an event-detection algorithm, and provides an efficient method for retrieving archival data. The event-detection algorithm rapidly correlates multiple PMU data streams, providing details on events occurring within the power system. The event-detection algorithm feeds into a visualization component, allowing operators to recognize events as they occur. The indexing and data retrieval mechanism facilitates fast access to archived PMU data. Using this method, we achieved over 30x speedup for queries with high selectivity. With the development of these two components, we have developed a system that allows efficient analysis of multiple time-aligned PMU data streams.Comment: Published in Electric Power Systems Research (2016), not available ye

Data-Driven Diagnostics of Mechanism and Source of Sustained Oscillations

Sustained oscillations observed in power systems can damage equipment, degrade the power quality and increase the risks of cascading blackouts. There are several mechanisms that can give rise to oscillations, each requiring different countermeasure to suppress or eliminate the oscillation. This work develops mathematical framework for analysis of sustained oscillations and identifies statistical signatures of each mechanism, based on which a novel oscillation diagnosis method is developed via real-time processing of phasor measurement units (PMUs) data. Case studies show that the proposed method can accurately identify the exact mechanism for sustained oscillation, and meanwhile provide insightful information to locate the oscillation sources.Comment: The paper has been accepted by IEEE Transactions on Power System

Source Location of Forced Oscillations Using Synchrophasor and SCADA Data

Recent advances in synchrophasor based oscillation monitoring algorithms have allowed engineers to detect oscillation issues that may have previously gone undetected. Although such an oscillation can be flagged and its oscillation shape can indicate the general vicinity of its source, low number of synchrophasors means that a specific generator or load that is the root cause of an oscillation cannot easily be pinpointed. Fortunately, SCADA serves as a much more readily available telemetered source of data if only at a relatively low sampling rate of 1 sample every 1 to 10 seconds. This paper shows that it is possible to combine synchrophasor and SCADA data for effective source location of forced oscillations. For multiple recent oscillation events, the proposed automatic methods were successful in correct identification of the oscillation source which was confirmed in each case by discussion with respective generation plant owners

Using Effective Generator Impedance for Forced Oscillation Source Location

Locating the sources of forced low-frequency oscillations in power systems is an important problem. A number of proposed methods demonstrate their practical usefulness, but many of them rely on strong modeling assumptions and provide poor performance in certain cases for reasons still not well understood. This paper proposes a systematic method for locating the source of a forced oscillation by considering a generator's response to fluctuations of its terminal voltages and currents. It is shown that a generator can be represented as an effective admittance matrix with respect to low-frequency oscillations, and an explicit form for this matrix, for various generator models, is derived. Furthermore, it is shown that a source generator, in addition to its effective admittance, is characterized by the presence of an effective current source thus giving a natural qualitative distinction between source and nonsource generators. Detailed descriptions are given of a source detection procedure based on this developed representation, and the method's effectiveness is confirmed by simulations on the recommended testbeds (eg. WECC 179-bus system). This method is free of strong modeling assumptions and is also shown to be robust in the presence of measurement noise and generator parameter uncertainty.Comment: 13 page

PI parameter tuning of converters for sub-synchronous interactions existing in grid-connected DFIG wind turbines

As a clean energy, wind power has been extensively exploited in the past few years. However, oscillations in wind turbines, particularly those from controllers, could severely affect the stability of power systems. Therefore, oscillation suppression is a recent research focus. Based on the small-signal model eigenvalues and participation factors, this paper detects the sub-synchronous interactions (SSI) mainly determined by converters' PI parameters in a grid-connected doubly fed induction generator (DFIG). With the aim of oscillation restraint, a novel optimization model with the reference-point based non-dominated sorting genetic algorithm (NSGA-III) and the t-distributed stochastic neighbour embedding (t-SNE) is developed to explore and visualize optimal ranges of PI parameters, facilitating the selection of the appropriate PI parameters to augment the damping. Additionally, to study the adaptability of the optimal PI parameters, interactions performance of the system that uses optimal parameters is studied with different output levels of the wind turbine. Finally, a time domain simulation and a practical experiment are conducted to demonstrate the effectiveness of the proposed approach. Results illustrate that the SSI of a grid-connected DFIG is suppressed by the optimization model. This study is highly beneficial to power system operators in integrating wind power and maintaining system stability.</p

PMU’s behavior with flicker-generating voltage fluctuations: an experimental analysis

Phasor measurement units (PMUs), which are the key components of a synchrophasor-based wide area monitoring system (WAMS), were historically conceived for transmission networks. The current trend to extend the benefits of the synchrophasor technology to distribution networks requires the PMU to also provide trustworthy information in the presence of signals that can occur in a typical distribution grid, including the presence of severe power quality (PQ) issues. In this framework, this paper experimentally investigates the performance of PMUs in the presence of one of the most important PQ phenomena, namely the presence of voltage fluctuations that generate the disturbance commonly known as flicker. The experimental tests are based on an ad-hoc high-accuracy measurement setup, where the devices under test are considered as “black boxes” to be characterized in the presence of the relevant signals. Two simple indices are introduced for the comparison among the different tested PMUs. The results of the investigation highlight possible critical situations in the interpretation of the measured values and provide a support for both the design of a new generation of PMUs and the possible development of an updated synchrophasor standard targeted to distribution systems

Early Anomaly Detection and Classification with Streaming Synchrophasor Data in Electric Energy Systems

The large-scale streaming data collected from the increasing deployed phasor measurement unit (PMU) devices poses significant difficulties for real-time data-driven analytics in power systems. This dissertation presents a dimensionality-reduction-based monitoring framework to make better use of the streaming PMU data for early anomaly detection and classification in power systems. The first part of this dissertation studies the fundamental dimensionality of large-scale PMU data, and proposes an online application for early anomaly detection using the reduced dimensionality. First, PMU data under both normal and abnormal conditions are analyzed by principal component analysis (PCA), and the results suggest an extremely low underlying dimensionality despite the large number of raw measurements. In comparison with prior work of utilizing multi-channel high-dimensional PMU data for power system anomaly detection, the proposed early anomaly detection algorithm employs the reduced-dimensional data from PCA, and detects the occurrence of an anomaly based on the change of core subspaces of the low-dimensional PMU data. Theoretical justification for the algorithm is provided using linear dynamical system theory. It is demonstrated that the proposed algorithm is capable to detect general power system anomalies at an earlier stage than would be possible by monitoring the raw PMU data. The second part of this dissertation investigates the classification of a special anomaly in power systems, low-frequency oscillation, which may cause severe impacts on power systems while at the same time is difficult to be accurately classified. We present a robust classification framework with online detection and mode estimation of low-frequency oscillations by using synchrophasor data. Based on persistent homology, a cyclicity response function is proposed to detect an oscillation, through the use of the low-dimensional features (pre-PCA features) extracted from PCA. Whenever the cyclicity response exceeds a numerically robust threshold, an oscillation can be detected. After the detection, PCA is applied again to extract the low-dimensional features (post-PCA features) from the multi-channel transient PMU data. It is shown that the post-PCA features preserve the underlying modal information in a more robust way in comparison to raw synchrophasor measurements. Based on the post-PCA features, fast Fourier transform (FFT) and Prony analysis can be subsequently applied to extract modal information of the oscillation. The proposed classification framework offers system operators a data-driven analytical tool for fast detection of low-frequency oscillation and robust mode estimation against high measurement noise