Fault Location in Grid Connected Ungrounded PV Systems Using Wavelets
Karmacharya, Indra Man 1985-
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Solar photovoltaic (PV) power has become one of the major sources of renewable energy worldwide. This thesis develops a wavelet-based fault location method for ungrounded PV farms based on pattern recognition of the high frequency transients due to switching frequencies in the system and which does not need any separate devices for fault location. The solar PV farm used for the simulation studies consists of a large number of PV modules connected to grid-connected inverters through ungrounded DC cables. Manufacturers report that about 1% of installed PV panels fail annually. Detecting phase to ground faults in ungrounded underground DC cables is also difficult and time consuming. Therefore, identifying ground faults is a significant problem in ungrounded PV systems because such earth faults do not provide sufficient fault currents for their detection and location during system operation. If such ground faults are not cleared quickly, a subsequent ground fault on the healthy phase will create a complete short-circuit in the system, which will cause a fire hazard and arc-flashing. Locating such faults with commonly used fault locators requires costly external high frequency signal generators, transducers, relays, and communication devices as well as generally longer lead times to find the fault. This thesis work proposes a novel fault location scheme that overcomes the shortcomings of the currently available methods. In this research, high frequency noise patterns are used to identify the fault location in an ungrounded PV farm. This high frequency noise is generated due to the switching transients of converters combined with parasitic capacitance of PV panels and cables. The pattern recognition approach, using discrete wavelet transform (DWT) multi-resolution analysis (MRA) and artificial neural networks (ANN), is utilized to investigate the proposed method for ungrounded grid integrated PV systems. Detailed time domain electromagnetic simulations of PV systems are done in a real-time environment and the results are analyzed to verify the performance of the fault locator. The fault locator uses a wavelet transform-based digital signal processing technique, which uses the high frequency patterns of the mid-point voltage signal of the converters to analyze the ground fault location. The Daubechies 10 (db10) wavelet and scale 11 are chosen as the appropriate mother wavelet function and decomposition level according to the characteristics of the noise waveform to give the proposed method better performance. In this study, norm values of the measured waveform at different frequency bands give unique features at different fault locations and are used as the feature vectors for pattern recognition. Then, the three-layer feed-forward ANN classifier, which can automatically classify the fault locations according to the extracted features, is investigated. The neural network is trained with the Levenberg-Marquardt back-propagation learning algorithm. The proposed fault locating scheme is tested and verified for different types of faults, such as ground and line-line faults at PV modules and cables of the ungrounded PV system. These faults are simulated in a real-time environment with a digital simulator and the data is then analyzed with wavelets in MATLAB. The test results show that the proposed method achieves 99.177% and 97.851% of fault location accuracy for different faults in DC cables and PV modules, respectively. Finally, the effectiveness and feasibility of the designed fault locator in real field applications is tested under varying fault impedance, power outputs, temperature, PV parasitic elements, and switching frequencies of the converters. The results demonstrate the proposed approach has very accurate and robust performance even with noisy measurements and changes in operating conditions.
DegreeMaster of Science (M.Sc.)
DepartmentElectrical and Computer Engineering
CommitteeKariki, Rajesh; Mehr, Aryan Saadat; Chen, Daniel
Copyright DateAugust 2016
Artificial neural networks (ANNs)
Discrete wavelet transform (DWT)
Multi-resolution analysis (MRA)
Ungrounded photovoltaic (PV) system.