عنوان

Proactive Monitoring, Anomaly Detection and Forecasting of Solar Photovoltaic Systems Using Artificial Neural Networks

شماره

TL52178

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Proactive Monitoring, Anomaly Detection and Forecasting of Solar Photovoltaic Systems Using Artificial Neural Networks

نام عام مواد

[Thesis]

نام نخستين پديدآور

Almadhor, Ahmad

نام ساير پديدآوران

Matin, Mohammad

نام ناشر، پخش کننده و غيره

University of Denver

تاریخ نشرو بخش و غیره

2019

متن يادداشت

122 p.

جزئيات پايان نامه و نوع درجه آن

Ph.D.

کسي که مدرک را اعطا کرده

University of Denver

امتياز متن

2019

متن يادداشت

The world of energy sustainability landscape is witnessing high proliferation of smartgrids and microgrids, it has become significant to use intelligent tools to design, operate and maintain such crucial systems in our lives. Solar energy is an intermittent source and purely Photovoltaic (PV) based, or PV and storage based smartgrids require characterization and modelling of PV resources for an effective planning and effective operations. This dissertation familiarizes briefly the existing tools for design, monitoring, forecasting and operation of a solar system in smart electric grids infrastructure and proposes a unique application-based infrastructure to monitor, operate, forecast and troubleshoot a working PV of a smartgrid. A resilient smartgrid communication is proposed which enables monitoring and control of different elements in any PV system. This communication architecture is used to facilitate a feedback-oriented monitoring of different elements in a microgrid ecosystem and investigated thoroughly. This integrated architecture which is a combination of sensors, network elements, database and computation elements is designed specifically for solar photovoltaic (PV) powered grids on modular basis. Apart from this, the network resilience and redundancy for smooth and loss less communication is another characteristic factor in this research work. Subsequently, a deep neural network algorithm is developed to diagnose the underperformance in the generation of a PV system connected to a smartgrid. As PV generation is predominantly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing performance of the system at any given instance. To address this challenge, this deep neural network architecture is presented for instantaneous performance diagnosis. The proposed architecture enabled modeling and diagnose of soiling and partial shade conditions prevalent with an accuracy of 90+%. Features of monitoring and regulating the generation and demand side of the grid were integrated through network along with feedback-based measures for effective performance in the PV system of a smartgrid or microgrid using the same network. The novelty in this work lies in real-time calculation of ideal performance and comparison for diagnosing critical performance issues of solar power generation like soiling and partial shading. Furthermore, long-short term memory (LSTM), which is a recurrent neural network model, is created for forecasting the PV solar resources, in which can assist in quantifying PV generation in various time intervals (hourly, daily, weekly). PV based smartgrids often experience expensive or inaccurate resources planning due to the lack of accurate forecasting tools where the projected methodology would eliminate such losses. This research work in its whole provides a different proposition of vertical integration which can transform into a new concept called Internet of Microgrid (IoMG). Planning, monitoring and operation form the core of smartgrids administration and if intelligent tools intertwined with network are being used as integral part in each of these aspects, then it forms a holistic view of smartgrids.

اصطلاح موضوعی

Engineering

مستند نام اشخاص تاييد نشده

Almadhor, Ahmad

مستند نام اشخاص تاييد نشده

Matin, Mohammad

مستند نام تنالگان تاييد نشده

University of Denver

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y