Journal ArticleDOI
Online Estimation of Steady-State Load Models Considering Data Anomalies
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TLDR
The challenges in online estimation of the load parameters using phasor measurement unit data are addressed and a novel adaptive search-based algorithm to estimate load model parameters is presented here.Abstract:
Several techniques have been developed to estimate the load parameters in power systems. Most of the existing algorithms mainly focus on estimating the parameters for offline studies. With on-going smart grid development, high-resolution data at faster rates are available to allow estimation of load parameters in real time. This paper addresses the challenges in online estimation of the load parameters using phasor measurement unit data. A novel adaptive search-based algorithm to estimate load model parameters is presented here. In this paper, a static load model is used with the Z (constant impedance), I (constant current), and P (constant power) components of the load. Developed estimation algorithms for the ZIP parameter estimation are validated using the IEEE 14-bus system and data provided by the industry collaborators. Simulation results demonstrate the accurate estimation of the ZIP load model using the developed method. Also, various techniques to eliminate anomalies in the input data for accurate estimation of the load parameters have been presented in this paper.read more
Citations
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Journal ArticleDOI
Detecting the Location of Short-Circuit Faults in Active Distribution Network Using PMU-Based State Estimation
TL;DR: The results proved that the proposed method for short-circuit fault detection and identification based on state estimation (SE) is more accurate and reliable than traditional SE based methods in fault conditions and can precisely determine the real location of fault at lower SE execution times.
Journal ArticleDOI
A Real Time Event Detection, Classification and Localization Using Synchrophasor Data
TL;DR: Algorithms include statistic, clustering, and Maximum Likelihood Criterion (MLE) based anomaly detection, Density-based spatial clustering of applications with noise (DBSCAN) for event detection and physics-based rule/ decision tree for event classification.
Journal ArticleDOI
Implementation of Real-Time Impedance-Based Stability Assessment of Grid-Connected Systems Using MIMO-Identification Techniques
TL;DR: A real-time implementation for the online stability analysis using MIMO-identification methods, where the stability of grid-connected system is rapidly assessed in the dq domain using orthogonal injections and Fourier techniques.
Journal ArticleDOI
Modeling Load Dynamics to Support Resiliency-Based Operations in Low-Inertia Microgrids
TL;DR: This paper examines the importance of including multi-state electromechanical dynamic models of the end- use load when evaluating the operations of low inertia microgrids, and shows that by properly representing their behavior, it is possible to cost effectively size equipment while supporting resilient operations of critical end-use loads.
Journal ArticleDOI
Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data
TL;DR: Data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data are provided.
References
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Book
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
TL;DR: With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory.
Journal ArticleDOI
Initial results in Prony analysis of power system response signals
TL;DR: Prony analysis as mentioned in this paper extends Fourier analysis by directly estimating the frequency, damping, strength, and relative phase of modal components present in a given signal, which can be used to extract such information from transient stability program simulations and from large-scale system tests of disturbances.
Journal ArticleDOI
Load Representation in Power System Stability Studies
C. Concordia,S. Ihara +1 more
TL;DR: In this paper, the authors present a load model for power system stability studies, which is qualitatively different from generator load modeling in many aspects, such as reliability of load estimates, models of different components must be combined to obtain a reasonably manageable overall system model and field measurements are not at all easy.