Aplicación de análisis de componente principal en sistemas eléctricos de potencia

El análisis de componente principal (ACP) es una técnica estadística de análisis multivariable ampliamente utilizada para encontrar patrones de datos de alta dimensión. La ventaja fundamental de ACP es la reducción del número de dimensiones de los datos, sin que exista mucha pérdida de información. En este artículo se hace una descripción de esta transformación matemática, y se presentan dos aplicaciones en el área de los sistemas eléctricos de potencia. ABSTRACT A common method from statistics for analyzing data is principal component analysis (PCA). The purpose of PCA is to identify the dependence structure behind a multivariable stochastic observation in order to obtain a compact description of it. The paper describes the mathematical fundamentals of PCA and two applications in power system area

Data analytic tool for clustering identification based on dimensionality reduction of frequency measurements

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This work presents a data analytic tool for clustering analysis based on Dimensionality Reduction (DR) of power system measurements. The proposed method is applied to frequency measurements of the ENTSO-E dynamic model of continental Europe and the results are compared with other conventional DR approaches. After considerable reduction of the raw measurements, a phasor metric for identification of coherency groups of generators is proposed. The recommended measure stands for its simple implementation, interpretation and fast computation. To illustrate the effectiveness of the clustering approach and the coherency of the metrics, a particular study case following the outage of a representative generation unit in France is presented

A tensor decomposition approach for contingency screening and coherency identification in power systems

Contingency Screening and Coherent Identification are two fundamental parts of power system planning and operation. A common characteristic among these two methods is the need to analyze multiples contingencies. However, most of the current work existing in the literature is based on the paradigm of analyzing one contingency at a time, using 2D arrays (matrices) for the event representation. The drawback with this type of representations is the impossibility to consider multiple contingencies simultaneously. In this paper a reformulation of the problem using 3D arrays (tensors) is presented. Then, the extraction of the information is carried out using PARAFAC2. With this information, a severity index for contingency screening is proposed and identification of the coherent areas is accomplished. The approach is validated in the IEEE NETSNYPS test system. The results confirm that the proposed approach allows to extract more information than in the traditional form

IoT-Based Electricity Bill for Domestic Applications

This work proposes a real-time electricity bill for quantifying the energy used in domestic facilities in Mexico. This bill is a low-cost tool that takes advantage of the IoT technology for generating an easy reading real-time bill allowing the customers to constantly review and administrate their energy consumption. Using low-cost sensors and the electronic board Particle® Photon, an energy meter is proposed. The presented prototype is extremely compact and satisfies safety measures to be used by anyone in a domestic installation. The measurement data is displayed and processed in real-time, and an appropriate algorithm determines the accumulated kWh. The energy consumed is displayed using an Html interface of easy interpretation for the customers, given recommendations about their consumption habits and some alarms in case of abnormal or high consumption. As a reinforcement measure for avoiding large consumption bills, the system is programmed to send messages to the user, remembering if the estimated consumption is large