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POWER QUALITY DATA ANALYTICS: INFORMATION EXTRACTION FROM POWER QUALITY DISTURBANCES

Grant number: 12/07300-7
Support type:Scholarships in Brazil - Post-Doctorate
Effective date (Start): October 01, 2012
Effective date (End): October 14, 2016
Field of knowledge:Engineering - Electrical Engineering - Power Systems
Principal researcher:Walmir de Freitas Filho
Grantee:Diogo Salles Correa
Home Institution: Faculdade de Engenharia Elétrica e de Computação (FEEC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:10/01690-2 - Technological development for protection, analysis, supervision and automation of electrical power systems of the future, AP.TEM
Associated scholarship(s):13/02008-9 - Intelligent analytics techniques for improving power quality of modern distribution systems, BE.EP.PD

Abstract

Power quality is a field that deals with all types of power disturbances. Past power quality research and development activities have been focused on the harmful aspects of the disturbances. With the wide spread use of power quality (PQ) monitoring tools, more and more users and developers started realizing that power disturbances can carry valuable information about the conditions of a system and its equipment. As a result, initiatives that explore the "useful" aspects of power disturbances have emerged. For example, voltage sag disturbances caused by short-circuits have been exploited for fault location purposes, characteristics of capacitor-switching transients are used to determine and locate which feeder capacitors are operating normally. Such information-oriented use of power quality (PQ) disturbance data and monitoring techniques could emerge as an important field of the future smart grid. Indeed, in recent technical meetings, this field has been denominated "Power Quality Data Analytics" following the general industry trend and the terminology for computer-based knowledge extraction from data.In this context, the objective of this post-doctoral is to develop analytic tools to interpret power quality (PQ) disturbance raw data so that it can be applied to solve many non-PQ problems such as fault location, fault anticipation, home appliance identification, load modeling, equipment condition monitoring (e.g., switched capacitor tracking). Raw measurement PQ data are usually captured at substations, distribution transformers, and customers' service entrances. They are available in the form of voltage and current time-series (waveforms), which can be processed to provide steady-state and transient characteristics such as voltage and current rms profiles, harmonic distortions, switching/energizing conditions, etc. With these analytics tools, it will be possible to examine, derives and extracts unique signatures from this diverse set of raw data, and forms useful information and conclusions. For example, capacitor switching transients exhibits unique waveforms that can help to identify its location and eventually to determine if the capacitor is switching as planned. Another application is home appliances identification using only the voltage/current raw data measured at the customer service entrance panel, which is somewhat similar to the "speech recognition" problem.