Dynamic vulnerability assessment due to transient instability based on data mining analysis for Smart Grid applications

Abstract

In recent years, some Smart Grid applications have been designed in order to perform timely Self-Healing and adaptive reconfiguration actions based on system-wide analysis, with the objective of reducing the risk of power system blackouts. Real time dynamic vulnerability assessment (DVA) has to be done in order to decide and coordinate the appropriate corrective control actions, depending on the event evolution. This paper presents a novel approach for carrying out real time DVA, focused on Transient Stability Assessment (TSA), based on some time series data mining techniques (Multichannel Singular Spectrum Analysis MSSA, and Principal Component Analysis PCA), and a machine learning tool (Support Vector Machine Classifier SVM-C). In addition, a general overview of the state of the art of the methods to perform vulnerability assessment, with emphasis in the potential use of PMUs for post-contingency DVA, is described. The developed methodology is tested in the IEEE 39 bus New England test system, where the simulated cause of vulnerability is transient instability. The results show that time series data mining tools are useful to find hidden patterns in electric signals, and SVM-C can use those patterns for effectively classifying the system vulnerability status.

Smart Grid for the Smart City

“…In their paper, they show that machine learning (decision trees with various feature types) can be effectively used to perform line-event detection with performance very close to that attained by a domain expert's hand-built classification rule (when applied to a signal located near a fault). Along similar lines, [14] focuses on Dynamic Vulnerability Assessment (DVA) for self-healing and adaptive reconfiguration of the Smart Grid based on time series analysis. Specifically, the authors apply some data mining techniques to time series (Multichannel Singular Spectrum Analysis, MSSA, and Principal Component Analysis, PCA) as well as SVMs to find hidden patterns in electric signals, which then allow for an effective classification of the system vulnerability status.…”

“…By sharing their actual voltage with their neighbors and estimating the impedance of distribution lines, DGs are in the position of evaluating the optimal voltages they should achieve. These voltages are then reached through iterative current injections, as dictated by (12.13) and (12.14), 14) where Z eq i is the equivalent Thevenin impedance seen from node n i , and V G i is assumed to be real. The results proposed in this work show that by coordinating the end-users equipped with DGs, the power distribution losses can be further reduced with respect to the previous two cases (i.e., where the DGs are operated independently, with no coordination).…”

Identification of dynamic equivalents based on heuristic optimization for smart grid applications

“…), has pushed the operation of electric power systems dangerously close to their technical limits, related to reduced generation reserve margins, increased demands for large power transactions (due to increasing interconnections) and physical limitations in the transmission network. Under these conditions, critical perturbations may cause cascading events that could eventually drive the system to blackouts [1]. Nowadays, the emerging intelligent technology has allowed designing the structure of the so-called "Smart Grid", which is supposed to efficiently respond to the actual system conditions and provide autonomous control actions to enhance the system security in real time [2].…”

“…This new scheme requires special control strategies which should be adjusted depending on the real time event progress. In this context, it is necessary to introduce a "Self-Healing Grid" functionality, which can provide critical information in real time, fast vulnerability assessment (VA), and perform timely and adaptive wide control actions [1]. VA is one of the main tasks to be necessarily considered in such a structure, since it has the function of detecting the need for performing global control actions.…”

“…For instance, modern highperformance computing (HPC) techniques (e.g. parallel or distributed processing) allow the improvement of calculation time [1], however these techniques are not fast enough to analyze large power systems in real time.…”