Research Information System
Thesis Type: Doctorate
Institution Of The Thesis: Gazi University, Fen Bilimleri Enstitüsü, Turkey
Approval Date: 2019
Thesis Language: English
Student: Ebrahim Balouji
Supervisor: ÖZGÜL SALOR DURNA
Open Archive Collection: AVESIS Open Access Collection
Abstract:The aim of this thesis is to develop tools which will provide complementary solutions to develop a complete and smart power quality (PQ) monitoring system. Based on the survey carried out on the current literature on PQ analysis and commercial devices, three main problems have been defined and solutions have been provided: the need of memory- and operation-efficient algorithms to estimate flicker, online detection of harmonics and interharmonics for highly time-varying load cases, and accurate PQ event classification. To estimate flicker from voltage RMS waveform, a digital realisation of the IEC (International Electrotechnical Committee) flickermeter using root mean square (RMS) of the voltage waveform as its input, instead of the voltage waveform, is presented. The aim is to compute the flicker severity according to the IEC flickermeter standard, IEC 61000-4-15, when only the RMS values of the voltage waveform are available. It has been shown on simulation and field data that short-term flicker severity can be computed by the proposed method with an average error rate of 0.021%. For the real-time detection of harmonics and interharmonics of current waveform, a multiple synchronous reference frame (MSRF) based analysis method is used together with order-optimized exponential smoothing (ES) to accurately obtain the time-varying harmonics and interharmonics of electric arc furnace (EAF) currents. Parallel processing of all harmonics and interharmonics are achived by graphical processing unit (GPU). It has been shown on field data that the implemented system is capable of successfully estimating all harmonics up to the 50th and all interharmonics at 5 Hz resolution in real-time. Moreover, active filtering of certain harmonics and interharmonics has been successfully achieved in the simulation environment. Finally, a new method for the classification of PQ events based on deep learning (DL) approach is presented. Novelty of the proposed method is that, image files of the voltage waveforms of the three phases of the power grid are classified. PQ events obtained from four transformer substations of the electricity transmission system for a year are used for training and testing the proposed classification method. The proposed method is shown to be able to classify the PQ events collected in the field.