Cause Identification of Electromagnetic Transient Events using Spatiotemporal Feature Learning

This paper presents a spatiotemporal unsupervised feature learning method for cause identification of electromagnetic transient events (EMTE) in power grids. The proposed method is formulated based on the availability of time-synchronized high-frequency measurement, and using the convolutional neural network (CNN) as the spatiotemporal feature representation along with softmax function. Despite the existing threshold-based, or energy-based events analysis methods, such as support vector machine (SVM), autoencoder, and tapered multi-layer perception (t-MLP) neural network, the proposed feature learning is carried out with respect to both time and space. The effectiveness of the proposed feature learning and the subsequent cause identification is validated through the EMTP simulation of different events such as line energization, capacitor bank energization, lightning, fault, and high-impedance fault in the IEEE 30-bus, and the real-time digital simulation (RTDS) of the WSCC 9-bus system.Comment: 9 pages, 7 figure

Optimal Recloser Setting, Considering Reliability and Power Quality in Distribution Networks

International audienceReclosers and fuses are the commonplace protective devices in distribution networks. A recloser can prevent long-time outages by clearing temporary faults before operation of the fuses in the system. Thus, it decreases the rate of long-term outages and improves system reliability and power quality. Despite positive features of reclosers, each operation of a recloser causes a momentary voltage interruption that exacerbates power quality. Nowadays, power quality issues have become more important because of the increasing use of sensitive equipment to voltage interruptions. According to the mentioned concerns, it seems necessary to set reclosers to strike a balance between power quality and the effectiveness of fuse saving scheme. Thus, we proposed a method to set reclosers. Due to the random nature of faults, the proposed method is stochastic based on the Monte Carlo method. The proposed method determines the optimal number of operations, reclosing intervals, and protection zones. The proposed method efficiency is evaluated according to the simulation results, and the proposed method is capable of establishing an optimal trade-off between power quality and protection efficiency

Robust Event Cause Analysis in Power Grids using Machine Learning Algorithms

Power grids are composed of generation, transmission, distribution and customer level assets along with protection, monitoring, and control equipment that are well-coordinated and operated to deliver sinusoidal voltage and current waveforms with desirable magnitude and frequency. However, faults, abnormal events, such as load or generation outages, grid equipment failures, assets malfunctioning, or lightning strikes occur that prevent the power grids from delivering the desired quality of service to the customers. These events frequently occur in the grid and protection devices usually isolate the faulty and malfunctioning sections of the grid to prevent further damage to the grid asset and equipment, and to prevent the propagation of disturbances to other sections of the grid. Fast and accurate detection and classification of abnormal events will, therefore, lead to a more accurate root cause analysis of failures, a quicker system restoration process after system disturbances, and less adverse impacts from socioeconomic, national security, and public health perspectives. Therefore, establishing an accurate event diagnostics (i.e., detection and classification) framework to extract useful information such as the cause or location of events is of utmost importance. On the other hand, disruptive and abnormal events may not cause immediate and direct failures on the grid. However, they are potential sources for permanent failures over time. Therefore, providing a framework for detecting and distinguishing these events from each other provides electric utilities with a comprehensive post-event analysis mechanism that can be used for preventive maintenance scheduling of the equipment exposed to the adverse events, or preventive actions prioritization to mitigate the potential future adverse impacts. This dissertation will present novel and robust data-driven frameworks for event cause analysis in power grids (distribution and transmission) based on the state-of-the-art measurement devices equipped with global positioning systems (GPS)