College of Engineering and Applied Sciences
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Item Comparison of Machine Learning Algorithms for Classification of Partial Discharge Signals in Medium Voltage Components(IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Espoo, Finland, 2021-10-18) Hussain, GhulamPartial discharge (PD) diagnosis is an effective tool to track the condition of electrical insulation in the medium voltage (MV) power components. Machine Learning Algorithms (MLAs) promote automated diagnosis solutions for large scale and reliable maintenance strategy. This paper aims to investigate the performance of two MLAs: Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) for the classification of different types of PD sources. Suitable features are extracted by applying statistical parameters on the coefficients of discrete wavelet transform (DWT) for observing the performance of both MLAs. The performance of the algorithms is evaluated using key performance indicators (KPIs); accuracy, prediction speed and training time. Besides KPIs, a confusion matrix is presented to highlight the accurately classified and misclassified PD signals for the SVM algorithm. Comparative study of both algorithms demonstrates that SVM provides better results as compared to the KNN algorithm. The proposed solution can be valuable for the development of automated classification.Item Method and apparatus for measuring prospective short circuit current(US Patents, 2021-09-01) Hussain, GhulamA method of determining a prospective short circuit current for an electrical system including a source includes connecting a test load between either: (i) a first phase line and a second phase line of the electrical system or (ii) the first phase line and the neutral line of the electrical system, employing a sensor coupled to the electrical system to measure a voltage drop across the test load, determining a voltage value based on at least the measured voltage drop across the test load, determining a total effective impedance for the first phase line to the source, and determining the prospective short circuit current based on the voltage value and the total effective impedance.Item An Interval Type-2 Fuzzy Logic System for the Simulation of Fused Deposition(INTERNATIONAL CONFERENCE ON RESEARCH IN SCIENCE, ENGINEERING AND TECHNOLOGY, Barcelona, Spain, 23rd June 2022, 2022-06-23) Gharaibeh, BelalItem Introduction to Robotics Remote/Voice Controlled Car(AIMT International Conference, 2022-08-05) Rababaah, Aaron; Meulien, Alexandre; Al-Abdulsalam, Ibrahim; AlTurkait, FarahItem Quantifying the Probability of Partial Discharge in VFD Fed Electric Motors under Voltage Harmonics Concentration(2022 20th International Conference on Harmonics & Quality of Power (ICHQP), Naples, Italy, 2022-05-29) Hussain, GhulamPartial discharges (PD) diagnostics are considered as reliable techniques for the insulation health assessment of various electrical components of the power system. Generally, PD diagnostic tests are performed in laboratory by ignoring the effect of harmonic pollution in test voltage waveform. However, the operation of electric motors (EMs) fed by variable frequency drives (VFD) produces harmonic distortion in the sinusoidal test voltage waveform. During low speed operation of VFD, the influence of harmonic distortion in test voltage waveform is increased. The additional distortion parameters in the test voltage waveform can significantly increase the PD activity in the EM insulation. Therefore, for correct understanding of PD data, it is essential to consider the actual harmonic spectrum of the test voltage waveform during online PD measurement. This paper presents a methodology for the quantification of the probability of PD in VFD-fed EMs under different concentration of voltage harmonics. For the experimental work, PD diagnostic tests have been performed under variable operating conditions of eight EMs and PD severity is investigated by calculating the PD characteristic parameters at different harmonic distortion levels. Also, the probability density functions and cumulative distribution functions for several probability distributions were evaluated to statistically process the PD data at different harmonic levels. Finally, the distribution fitting tools have been implemented to find an appropriate distribution that can precisely characterize the probability of PD under different concentration of voltage harmonics. The proposed method may be utilized for studying the effect of harmonic distortion on PD activity and estimating the lifetime of stator insulation in EMs.