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- All Subjects: Electrical Engineering
- Genre: Doctoral Dissertation
- Member of: Theses and Dissertations
Description
Large number of renewable energy based distributed energy resources(DERs) are integrated into the conventional power grid using power electronic interfaces. This causes increased need for efficient power conversion, advanced control, and DER situational awareness. In case of photovoltaic(PV) grid integration, power is processed in two stages, namely DC-DC and DC-AC. In this work, two novel soft-switching schemes for quadratic boost DC-DC converters are proposed for PV microinverter application. Both the schemes allow the converter to operate at higher switching frequency, reducing the converter size while still maintaining high power conversion efficiency. Further, to analyze the impact of high penetration DERs on the power system a real-time simulation platform has been developed in this work. A real, large distribution feeder with more than 8000 buses is considered for investigation. The practical challenges in the implementation of a real-time simulation (such as number of buses, simulation time step, and computational burden) and the corresponding solutions are discussed. The feeder under study has a large number of DERs leading to more than 200% instantaneous PV penetration. Opal-RT ePHASORSIM model of the distribution feeder and different types of DER models are discussed in detailed in this work. A novel DER-Edge-Cloud based three-level architecture is proposed for achieving solar situational awareness for the system operators and for real-time control of DERs. This is accomplished using a network of customized edge-intelligent-devices(EIDs) and end-to-end solar energy optimization platform(eSEOP). The proposed architecture attains superior data resolution, data transfer rate and low latency for the end-to-end communication. An advanced PV string inverter with control and communication capabilities exceeding those of state-of-the-art, commercial inverters has been developed to demonstrate the proposed real-time control. A power-hardware-in-loop(PHIL) and EID-in-loop(EIL) testbeds are developed to verify the impact of large number of controllable DERs on the distribution system under different operational modes such as volt-VAr, constant reactive power and constant power factor. Edge level data analytics and intelligent controls such as autonomous reactive power allocation strategy are implemented using EIL testbed for real-time monitoring and control. Finally, virtual oscillator control(VOC) for grid forming inverters and its operation under different X/R conditions are explored.
ContributorsKorada, Nikhil (Author) / Ayyanar, Raja (Thesis advisor) / Lei, Qin (Committee member) / Wu, Meng (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2022
Description
Operational efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of photovoltaic (PV) arrays under various conditions. This dissertation describes a project that focuses on the development of machine learning and neural network algorithms. It also describes an 18kW solar array testbed for the purpose of PV monitoring and control. The use of the 18kW Sensor Signal and Information Processing (SenSIP) PV testbed which consists of 104 modules fitted with smart monitoring devices (SMDs) is described in detail. Each of the SMDs has embedded, a wireless transceiver, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. Data is obtained in real time using the SenSIP PV testbed. Machine learning and neural network algorithms for PV fault classification is are studied in depth. More specifically, the development of a series of customized neural networks for detection and classification of solar array faults that include soiling, shading, degradation, short circuits and standard test conditions is considered. The evaluation of fault detection and classification methods using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN) is performed. The examination and assessment the classification performance of customized neural networks with dropout regularizers is presented in detail. The development and evaluation of neural network pruning strategies and illustration of the trade-off between fault classification model accuracy and algorithm complexity is studied. This study includes data from the National Renewable Energy Laboratory (NREL) database and also real-time data collected from the SenSIP testbed at MTW under various loading and shading conditions. The overall approach for detection and classification promises to elevate the performance and robustness of PV arrays.
ContributorsRao, Sunil (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2021