A real-time algorithm is developed for the detection of series dc arc faults in a grid-tie solar photovoltaic (PV) installation. The sensed dc bus current, which is sampled using an analog-to-digital converter with Galvanic isolation, is filtered using a wavelet-based, two-level filter bank. The filter bank, referred to as the post-processing filter, improves the robustness of the algorithm to any false tripping by rejecting power converter harmonics that are added to the dc bus current. To determine if a fault has occurred, the algorithm calculates the variance of the filter bank output and sees if the calculated variance exceeds an upper threshold value. If the upper threshold is exceeded, and the dc bus voltage falls below a predefined lower limit for a set number of instances, the algorithm trips. The algorithm can detect a series arc fault in under two seconds and does not rely on machine learning techniques to process the sensed signal. The detection algorithm is implemented on a commercial microcontroller using C code, and the filter bank convolutions are implemented using 32-bit floating point variables. / Master of Science / A device is developed for the detection of series dc arc faults in solar photovoltaic installations. Dc arc faults that result from loose connections or worn cable insulation can go unnoticed by most conventional fault detectors. Once it has ignited, the series arc can generate considerable amounts of heat and poses a significant fire risk. By contributing to the development of a dc arc fault detection system, the intention is that dc renewable energy distribution systems, most notably solar photovoltaic installations, can gain even more widespread adoption. This would make a significant impact towards decarbonizing the energy sector and tackling the threat to society posed by climate change.

Yes / This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 sam-ples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window sam-ples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Infor-mation in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequen-cy WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed meth-od, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the pro-posed method is independent of the types of motors used and their ages.

As electronics are getting both smaller and more advanced, the need to verify and validate remains and the means are getting more complex the more functions and components are added. Traditionally, in-circuit tests (ICTs) are performed by probing dedicated test points on the Printed Circuit Board Assembly (PCBA) in a test sequence that is unique to each product. But as the density of components increases, the choice between component and test point must be considered. Instead of decreasing the reliability during verification by having to remove less system-critical test points, this thesis suggests the use of a near-field probe (NFP) based around a Giant Magneto-Resistance (GMR) sensor to possibly replace the need for a physical test point by instead performing contactless testing. The use of a GMR sensor allows for bandwidth from 0 Hz up to the MHz range, whereas commercial NFPs are based on a different technique and are operational from the MHz range and up. The goal of this project was to improve the non-linearity of typically 15% present in the AAH002-02 model from NVE by the use of an analogue closed-loop magnetic feedback circuit. The project successfully improved the linearity to 99.8% by the use of an instrumentation amplifier, a subtractor and a push-pull amplifier in conjunction with a 3x30 turn planar coil embedded in a PCB, located beneath the sensor Integrated Circuit (IC). The resulting linearity was verified by a Helmholtz coil where a uniform magnetic field was produced with linearly increased field strength, and calculated using the R2 value from a linear regression analysis on the acquired data. In the future, the data acquired from this kind of NFP could be used together with a Machine Learning (ML) model to remove the manual labour required when constructing these product-unique test sequences. / Dagens elektronik blir både mindre och mer avancerad, men behovet av verifiering och validering av dessa kvarstår och metoderna för detta ökar i komplexitet ju fler funktioner och komponenter som läggs till. Dagens kretskortstester genomförs genom att sondera dedikerade testpunkter strategiskt utplacerade på kretskortet enligt en testsekvens som är unikt skapad för varje produkt. Men med att densiteten av komponenter ökar måste valet mellan komponent och testpunkt tas i beaktning. Instället för att minska tillförlitligheten vid validering genom att ta bort mindre kritiska testpunkter föreslår denna avhandling användandet av en närfältssond baserad runt en Giant Magneto-Resistance (GMR)-sensor för att möjligen ersätta behovet av en fysisk testpunkt genom att istället genomföra kontaktlös testning. Användandet av en GMR-sensor tillåter en bandbredd från 0 Hz upp till MHzområdet, där kommersiella närfältssonder är baserade på annan teknik och är funktionsdugliga från MHz-området och uppåt. Målet med detta projekt var att förbättra olinjäriteten på typiskt 15% som är närvarande hos en sensor av modell AAH002-02 från NVE genom en analog magnetisk återkopplingskrets med sluten slinga. Projektet lyckades förbättra linjäriteten till 99.8% genom användandet av en intrumentförstärkare, en subtraherare och en push-pull-förstärkare i samverkan med en plan spole på 3x30 varv inbyggd i ett mönsterkort placerd under sensorns integrerade krets. Den resulterande linjäriteten validerades med hjälp av en Helmholtz-spole där ett uniformt magnetfält producerades med linjärt ökande fältstyrka och beräknades genom R2 -värdet från en linjär regression-analys på den inhämtade datan. I framtiden kan datan som inhämtats från den här sortens närfältssond kunna användas tillsammans med en maskininlärningsmodell för att ersätta det manuella arbetet som idag krävs för att konstruera dessa produktunika testsekvenser