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- W1591128812 abstract "Power quality is becoming increasingly important in power systems. The proliferation ofmodem power electronic devices has both decreased the quality of power on the electricgrid and increased the equipment's sensitivity to power quality disturbances. In order tomaintain an acceptable level of power quality in a power system, power qualitymonitoring devices have been designed. These devices are purchased by power utilitiesand customers in increasing numbers for the purpose of power quality trend logging andpower quality disturbance capturing. Many power quality monitors will provide someinformation on the type of a power quality disturbance that has been recorded, however areliable system for automatically classifying the full range of disturbance types is yet tobe formalised. Recent advances in signal processing and artificial intelligence have putthis goal within reach.A system for the automatic classification of recorded power quality disturbances isdeveloped in this thesis. The system uses a variety of mathematical tools in order toproduce an accurate and robust classification method. These tools include Fouriertransforms, wavelet transforms, artificial neural networks, and fuzzy logic. Transientdisturbances are analysed using the wavelet transform general modulus maximatechnique and classified into their respective disturbance classes using a neuro-fuzzypattern recognition scheme. The remaining disturbance types are analysed using Fouriertransforms and classified into their respective disturbance classes using a simple decisionmaking scheme.Also investigated in this thesis is a method for monitoring the harmonic output from nonlinearloads in a power system using a reduced number of harmonic monitoring stations.The method utilises a state estimation scheme with measurements from installedharmonic monitoring stations combined with pseudo-measurements provided by artificialneural networks." @default.
- W1591128812 created "2016-06-24" @default.
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- W1591128812 date "2003-06-01" @default.
- W1591128812 modified "2023-09-26" @default.
- W1591128812 title "Studies of power quality : disturbance recognition" @default.
- W1591128812 hasPublicationYear "2003" @default.
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