A Machine-Learning Pipeline for Large-Scale Power-Quality Forecasting in the Mexican Distribution Grid  
[PERSON]  
Computer Science Department, University of Oregon, Eugene, OR 97403, USA Facultad de Ingenieria Electrica, Universidad Michocana de San Nicolas de Hidalgo, Morelia 58030, Mexico Comision Federal de Electricidad, Av. Paseo de la Reforma 164, Juarez, Cuauhtemoc, Ciudad de Mexico 06600, Mexico Correspondence: [EMAIL_ADDRESS]; Tel. : +1-541-346-3487  
[PERSON]  
Computer Science Department, University of Oregon, Eugene, OR 97403, USA Facultad de Ingenieria Electrica, Universidad Michocana de San Nicolas de Hidalgo, Morelia 58030, Mexico Comision Federal de Electricidad, Av. Paseo de la Reforma 164, Juarez, Cuauhtemoc, Ciudad de Mexico 06600, Mexico Comision Federal de Electricidad, Av. Paseo de la Reforma 164  
that use data from large distribution grids usually employ information from only a few power quality meters (PQM). As a result, the need for investigating a methodology for PQ indices forecasting in big-data environments remains. This article presents the methodology, architecture, and results of a short-term PQ indices-forecasting project developed in Mexico for the electric-power distribution grid of CFE (the Spanish acronym for Comision Federal de Electricidad--Federal Board of Electric Power), the state-owned enterprise responsible for the Mexican state grid and also the primary electric-power provider in the country. This article presents MIRD, a system whose main objective is the application of artificial-intelligence techniques (machine-learning-based time-series forecasting) to monitor the evolution of significant PQ indices. This monitoring process occurs at crucial coupling points of the distribution grid. Based on the Mexican Energy Law reform in the mid-2010s, CRE (the Spanish acronym for Comision Reguladora de Energia--Energy Regulation Board), issued the general conditions and criteria for distributing electric power in Mexico. This law expresses strict regulations on the quality of delivered energy in terms of detailed PQ indices intervals and values. In particular, that law requires that CFE's power distribution grid fulfill the following requirements (see Table 1):  
Operation voltage.The voltage a quality-control node or quality node operates at must lie within a defined interval for at least 90% of the 10-min voltage measurements in the node. This acceptance interval is 93-105% of the node's nominal voltage. The operation voltage is calculated as the average of the three per-phase RMS voltages, as \(V_{oper}=\sqrt{3}(V_{an}+V_{bn}+V_{cn})/3\). Electric power factor.The power factor measured at a distribution circuit must be greater than or equal to 0.95 in at least 80% of a given set of circuits. The power factor is calculated as the ratio of the real power absorbed by the load to the apparent power that flows through the circuit. Current unbalance.The three-phase current flowing into a distribution circuit must be within the required balance interval, which is 15% in at least 80% of a given set of circuits. The current unbalance is calculated as follows: (a) the per-phase current average is determined, (b) the largest per-phase current deviation from the average is determined, and (c) the current unbalance is the ratio of the largest deviation and the current average, expressed as a percentage. Under these regulations, CFE requires an efficient early-warning system to meet standards and avoid penalties determined by CRE. In the past, CFE's monitoring engineers received only a weekly report listing the performance of the distribution circuits under their command. If CFE did not meet the standards for any reason, there was nothing they could do to avoid fines issued to CFE. By providing a prediction or estimation of the PQ indices for the following week, engineers can act ahead of time, fix those circuits' operating conditions, and improve their performances in adequate time frames.