Design and Development of Non-Intrusive Load Monitoring Techniques and Solar Photovoltaic-Thermoelectric Hybrid Energy Conversion Systems

Abstract

Standalone Solar Photovoltaic (PV) Systems are deployed where access to the conventional Electric Grid is not present. It is necessary to extract most of the available power from the Solar PV modules as the supply is limited. In such cases, the breakdown of energy demand must be monitored in real-time. Energy Disaggregation is employed to understand the components of the loads connected to the system. One of the techniques used for Energy Disaggregation is Non-Intrusive Load Monitoring (NILM). In this approach, only a single, smart energy meter upstream of the demand side is used to estimate the individual constituents of the load, removing the need for multiple sensors to monitor each load. NILM works by training a Machine Learning model through historical data or power signatures of different appliances and deducing individual components in real-time. In the thesis work, the Factorial Hidden Markov Model is applied for NILM wherein the Hidden States of the model, which are the same as the Appliance States, are estimated based on the power signal from the Smart Energy Meter. Training of the Factorial Hidden Markov model requires historical sub-metered data of each appliance which may be challenging to acquire. Significant contributions of this work are to synthetically develop training datasets based on known parameters of appliances using an Energy Demand Model. The appliance datasets are compared with the Indian Dataset for Ambient Water and Energy (iAWE). With the advent of the Internet of Things (IoT) technology, it is now possible to remotely control individual circuits. Another contribution of this work is providing a single platform to monitor and control Appliances using IoT devices. The data regarding the states of these IoT devices is fed back to the NILM algorithm to improve the accuracy and reduce computation times. The other part of the thesis work mainly consists of Solar PV systems with maximum power point tracking. The Optimum value of Voltage and Current needs to be tracked independently of the load and weather conditions. Amongst the Control Strategies, Voltage Control is most prevalent, and there was a need to investigate Current Control as literature is limited. A framework for Current Control Strategies for Boost Converters was developed and experimentally verified. The Hill-Climbing or the Perturb and Observe method was used for implementation, in which momentum based on past perturbations, was introduced which showed improved performance. Further, a domain-independent Bond Graph approach to model hybrid thermoelectric systems was established. A modular Thermoelectric System is designed, which shows potential for Improvement in the Cooling Capacity and Coefficient of Performance of the system. An Active Heating and Cooling controlled chamber with a Standalone Solar PV System including Energy Disaggregation to Monitor the demand is fabricated and installed.