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Power Quality Analysis for Industrial and Commercial Sectors Using Data Science

  • In recent years, the demand for reliable power, driven by sensitive electronic equipment, has surged. Even minor deviations from the nominal supply can lead to malfunctions or failure. Despite technological advancements, power quality issues persist due to various factors like short circuits, overloads, voltage fluctuations, unbalanced loads, and non-linear loads. This thesis extensivelyIn recent years, the demand for reliable power, driven by sensitive electronic equipment, has surged. Even minor deviations from the nominal supply can lead to malfunctions or failure. Despite technological advancements, power quality issues persist due to various factors like short circuits, overloads, voltage fluctuations, unbalanced loads, and non-linear loads. This thesis extensively explores power quality anomalies in industrial and commercial sectors, using power system data as the primary analytical resource. It addresses the critical need for power supply reliability in today's evolving power grid industry, affected by non-linear loads, renewable energy integration, and electric vehicles. This field of study is paramount for ensuring power supply reliability and stability in the evolving power grid industry. The core of this thesis involves a comprehensive investigation of power quality, with a focus on frequency, power, and harmonics in voltage and current signals. The research employs Python programming for advanced data analysis, utilizing techniques such as advanced Fast Fourier Transformation (FFT) analysis. The primary objective is to provide valuable insights aimed at elevating power supply quality and enhancing reliability in both industrial and commercial environments.show moreshow less

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Metadaten
Document Type:Master's Thesis
Zitierlink: https://opus.hs-offenburg.de/8021
Bibliografische Angaben
Title (English):Power Quality Analysis for Industrial and Commercial Sectors Using Data Science
Author:Krishan Gopal Sharma
Advisor:Jörg Bausch, Uchenna Johnpaul Aniekwensi
Year of Publication:2023
Granting Institution:Hochschule Offenburg
Page Number:97
Language:English
Inhaltliche Informationen
Institutes:Fakultät Maschinenbau und Verfahrenstechnik (M+V)
Institutes:Abschlussarbeiten / Master-Studiengänge / RED
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
GND Keyword:Anomalieerkennung; Data Science; Datenanalyse; Strom
Tag:Anomaly Detection; Data analysis; Power Quality; Python Programming
Formale Angaben
Open Access: Closed 
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International