Browsing by Author "Orchard, Marcos"

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    A full compensating system for general loads, based on a combination of thyristor binary compensator, and a PWM-IGBT active power filter
    (IEEE, 2001) Dixon Rojas, Juan, 1948-; Del Valle, Yamille; Orchard, Marcos; Ortúzar Dworsky, Micah Etan; Morán, Luis; Maffrand, Carlos
    A full compensating system for distribution networks which is able to eliminate harmonics, correct unbalanced loads and generate or absorb reactive power is presented. The system is based on a combination of a thyristor binary compensator (TBC), and a PWM-IGBT active power filter (APF) connected in cascade. The TBC compensates the fundamental reactive power and balances the load connected to the system. The APF eliminates the harmonics and compensates the small amounts of load unbalances or power factor that the TBC cannot eliminate due to its binary condition. The TBC is based on a chain of binary-scaled capacitors and one inductor per phase. This topology allows, with an adequate number of capacitors, a soft variation of reactive power compensation and a negligible generation of harmonics. The capacitors are switched on when the line voltage reaches its peak value, avoiding inrush currents generation. The inductor helps to balance the load, and absorbs reactive power when required. The APF works by measuring the source currents, forcing them to be sinusoidal. The two converters (TBC and APF) work independently, making the control of the system simpler and more reliable. The system is able to respond to many kinds of transient perturbations in no more than a couple of cycles. The paper analyzes the circuit proposed, the way it works and the results obtained under operation with different types of loads.
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    Sentiment analysis and prediction of events in TWITTER
    (IEEE, 2015) Montesinos, Lucas; Rodríguez, Juan Pablo; Orchard, Marcos; Eyheramendy Duerr, Susana
    Sentiment analysis, also known as opinion mining, is a mechanism for understanding the natural disposition that people possess towards a specific topic. This type of information is very valuable for certain industries - digital marketing companies use sentiment analysis to track the public's mood about a particular product, the view of elected authorities in a given country, or to explain sports allegiances, among many other goals. A common approach to sentiment analysis consists of systematically reviewing content from websites, especially social networks like Facebook, Twitter, and Google+, and using an algorithm to determine the opinions of the masses. For this work, the main body of analysis came from the "Twittersphere." On the Twitter platform, users send 140-character messages to the social network as a means of expressing their viewpoints on certain issues. These messages, or "tweets," are then shown in the user's homepage. Twitter is used widely in Chile. This work analyzed the public's opinions on the presidential primaries for the Alliance political party between Andres Allamand "Renovación Nacional" (RN) and Pablo Longueira from "Union Democrata Independiente" (UDI) using information collected from Twitter in that country. After gathering all relevant data, researchers used sentiment analysis to predict the outcome of the primaries. This data identified citizens who were in favor (positive) of either Allamand or Longueira and people who were against (negative) any political party or persuasion. Researchers designed a dictionary algorithm to aid in these predictions. This was comprised of certain positive and negative words, which, when applied to the Twitter data, was able to determine the polarity of the message: positive, negative, and/or neutral. In addition, an exponential function was used for analyzing the distance between the words, which is useful to gather opinions where both candidates are mentioned, identifying polarity for each of them separately. Later, a score was assigned to each Twitter user. Those cumulative scores were ultimately used to predict the way those given users would vote in the primary elections.