ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Spanias, Andreas
Description
Utility scale solar energy is generated by photovoltaic (PV) cell arrays, which are often deployed in remote areas. A PV array monitoring system is considered where smart sensors are attached to the PV modules and transmit data to a monitoring station through wireless links. These smart monitoring devices may be used for fault detection and management of connection topologies. In this thesis, a compact hardware simulator of the smart PV array monitoring system is described. The voltage, current, irradiance, and temperature of each PV module are monitored and the status of each panel along with all data is transmitted to a mobile device. LabVIEW and Arduino board programs have been developed to display and visualize the monitoring data from all sensors. All data is saved on servers and mobile devices and desktops can easily access analytics from anywhere. Various PV array conditions including shading, faults, and loading are simulated and demonstrated.
Additionally, Electrical mismatch between modules in a PV array due to partial shading causes energy losses beyond the shaded module, as unshaded modules are forced to operate away from their maximum power point in order to compensate for the shading. An irradiance estimation algorithm is presented for use in a mismatch mitigation system. Irradiance is estimated using measurements of module voltage, current, and back surface temperature. These estimates may be used to optimize an array’s electrical configuration and reduce the mismatch losses caused by partial shading. Propagation of error in the estimation is examined; it is found that accuracy is sufficient for use in the proposed mismatch mitigation application.
Additionally, Electrical mismatch between modules in a PV array due to partial shading causes energy losses beyond the shaded module, as unshaded modules are forced to operate away from their maximum power point in order to compensate for the shading. An irradiance estimation algorithm is presented for use in a mismatch mitigation system. Irradiance is estimated using measurements of module voltage, current, and back surface temperature. These estimates may be used to optimize an array’s electrical configuration and reduce the mismatch losses caused by partial shading. Propagation of error in the estimation is examined; it is found that accuracy is sufficient for use in the proposed mismatch mitigation application.
ContributorsPeshin, Shwetang (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2016
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