Power system quality and its reliability continue to grow in importance as consumers become more sensitive and the cost of power quality problems increases. Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Detection and classification of power system disturbances based on visual inspection of waveforms by a human operator are laborious and erroneous. Existing automatic recognition methods need much improvement in terms of their versatility, reliability, and accuracy. Sophisticated systems for automatic classification of power quality disturbances are highly desired for both power suppliers and consumers. The objective of this research is to develop state of the art signal classification algorithms for classifying different types of power quality disturbances, based on the recent advances in signal processing and pattern classification techniques. This research presents a power quality monitoring system for classifying voltage and current waveform events that are related to a variety of power quality problems.
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Power system quality and its reliability continue to grow in importance as consumers become more sensitive and the cost of power quality problems increases. Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Detection and classification of power system disturbances based on visual inspection of waveforms by a human operator are laborious and erroneous. Existing automatic recognition methods need much improvement in terms of their versatility, reliability, and accuracy. Sophisticated systems for automatic classification of power quality disturbances are highly desired for both power suppliers and consumers. The objective of this research is to develop state of the art signal classification algorithms for classifying different types of power quality disturbances, based on the recent advances in signal processing and pattern classification techniques. This research presents a power quality monitoring system for classifying voltage and current waveform events that are related to a variety of power quality problems. Codice articolo 9786208426361
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Power system quality and its reliability continue to grow in importance as consumers become more sensitive and the cost of power quality problems increases. Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Detection and classification of power system disturbances based on visual inspection of waveforms by a human operator are laborious and erroneous. Existing automatic recognition methods need much improvement in terms of their versatility, reliability, and accuracy. Sophisticated systems for automatic classification of power quality disturbances are highly desired for both power suppliers and consumers. The objective of this research is to develop state of the art signal classification algorithms for classifying different types of power quality disturbances, based on the recent advances in signal processing and pattern classification techniques. This research presents a power quality monitoring system for classifying voltage and current waveform events that are related to a variety of power quality problems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch. Codice articolo 9786208426361
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Taschenbuch. Condizione: Neu. Design & Development of Detuned Filters for Power Quality Improvement | Detuned Filters | Chandrasekar Perumal (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208426361 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. Codice articolo 133835120
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