Solar Photovoltaic (PV) Distributed Generation Systems - Control and Protection Open Access
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This dissertation proposes a comprehensive control, power management, and fault detection strategy for solar photovoltaic (PV) distribution generations. Battery storages are typically employed in PV systems to mitigate the power fluctuation caused by unstable solar irradiance. With AC and DC loads, a PV-battery system can be treated as a hybrid microgrid which contains both DC and AC power resources and buses. In this thesis, a control power and management system (CAPMS) for PV-battery hybrid microgrid is proposed, which provides 1) the DC and AC bus voltage and AC frequency regulating scheme and controllers designed to track set points; 2) a power flow management strategy in the hybrid microgrid to achieve system generation and demand balance in both grid-connected and islanded modes; 3) smooth transition control during grid reconnection by frequency and phase synchronization control between the main grid and microgrid. Due to the increasing demands for PV power, scales of PV systems are getting larger and fault detection in PV arrays becomes challenging. High-impedance faults, low-mismatch faults, and faults occurred in low irradiance conditions tend to be hidden due to low fault currents, particularly, when a PV maximum power point tracking (MPPT) algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. In this dissertation, fault detection challenges in PV arrays are analyzed in depth, considering the crossing relations among the characteristics of PV, interactions with MPPT algorithms, and the nature of solar irradiance. Two fault detection schemes are then designed as attempts to address these technical issues, which detect faults inside PV arrays accurately even under challenging circumstances, e.g., faults in low irradiance conditions or high-impedance faults. Taking advantage of multi-resolution signal decomposition (MSD), a powerful signal processing technique based on discrete wavelet transformation (DWT), the first attempt is devised, which extracts the features of both line-to-line (L-L) and line-to-ground (L-G) faults and employs a fuzzy inference system (FIS) for the decision-making stage of fault detection. This scheme is then improved as the second attempt by further studying the system's behaviors during L-L faults, extracting more efficient fault features, and devising a more advanced decision-making stage: the two-stage support vector machine (SVM). For the first time, the two-stage SVM method is proposed in this dissertation to detect L-L faults in PV system with satisfactory accuracies.Numerous simulation and experimental case studies are carried out to verify the proposed control and protection strategies. Simulation environment is set up using the PSCAD/EMTDC and Matlab/Simulink software packages. Experimental case studies are conducted in a PV-battery hybrid microgrid using the dSPACE real-time controller to demonstrate the ease of hardware implementation and the controller performance. Another small-scale grid-connected PV system is set up to verify both fault detection algorithms which demonstrate promising performances and fault detecting accuracies.