![]() ![]() Synchrophasor data is used for applications varying from state estimation, islanding control, identifying outages, voltage stability detection and correction, disturbance recording, and others. They are able to measure highly accurate bus voltage phasors as well as branch current phasors incident to the buses at which PMUs are equipped. PMUs are critical components of today s energy management systems, designed to enable near real-time wide area monitoring and control of the electric power system. Power systems are rapidly becoming populated by phasor measurement units (PMUs) in ever increasing numbers. PMU Data Integrity Evaluation through Analytics on a Virtual Test-Bed Jamei, Mahdi Scaglione, Anna Roberts, Ciaran. The effectiveness of the proposed methods are tested through the synthetic and real μ PMU data.« less And due to the key role of the μ PMU devices in our architecture, a source-constrained optimal μ PMU placement is also described that finds the best location ofmore » the devices with respect to our rules. Here, focusing on Micro-Phasor Measurement Unit (μ PMU) data, we propose a hierarchical architecture for monitoring the grid and establish a set of analytics and sensor fusion primitives for the detection of abnormal behavior in the control perimeter. IEEE As the distribution grid moves toward a tightly-monitored network, it is important to automate the analysis of the enormous amount of data produced by the sensors to increase the operators situational awareness about the system. Jamei, Mahdi Scaglione, Anna Roberts, Ciaran Extensive results using field PMU data from WECC system reveal that the Gaussian assumption is questionable.Īnomaly Detection Using Optimally-Placed μ PMU Sensors in Distribution Grids This letter proposes a simple yet effective approach to assess this assumption by using the stability property of a probability distribution and the concept of redundant measurement. Gaussian PMU measurement error has been assumed for many power system applications, such as state estimation, oscillatory modes monitoring, voltage stability analysis, to cite a few. ![]() The performance of the proposed method is verified through simulation studies.« lessĪssessing Gaussian Assumption of PMU Measurement Error Using Field Data This approach will help to maintain an accurate dynamic model suitable for online dynamic studies. ![]() Then, a calibration algorithm is developed to estimatemore » parameters of the reduced model. First, a model reduction method is used to reduce the number of dynamic components. To facilitate online dynamic studies for large power system interconnections, this paper proposes a model reduction and calibration approach using phasor measurement unit ( PMU) data. To improve model accuracy, identification algorithms have been developed to calibrate parameters of individual components using measurement data from staged tests. Lower confidence on model accuracy usually leads to conservative operation and lowers asset usage. The paper is intended to aid the reader in recognizing and properly addressing data quality issues in PMU data.« lessĬalibration of Reduced Dynamic Models of Power Systems using Phasor Measurement Unit ( PMU) DataĪccuracy of a power system dynamic model is essential to the secure and efficient operation of the system. Along with the filter's description, examples of data quality issues from application of the filter to nine months of archived PMU data are provided. Measurements are compared to preselected thresholds tomore » determine if they are reliable. The filter operates based only on the information included in the data files, without supervisory control and data acquisition (SCADA) data, state estimator values, or system topology information. The data quality filter described in this paper was developed for use with the Data Integrity and Situation Awareness Tool (DISAT), which analyzes PMU data to identify anomalous system behavior. With so much data, it is impractical to identify and remove poor quality data manually. Networks of phasor measurement units (PMUs) continue to grow, and along with them, the amount of data available for analysis. A Data Quality Filter for PMU Measurements: Description, Experience, and Examples ![]()
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