Non intrusive load monitoring & identification for energy management system using computational intelligence approach

Includes bibliography.Electrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval

Analysis and utilization of reverse power flow of wind energy source using multi-port power electronic transformer.

Doctoral Degree. University of KwaZulu- Natal, Durban.The recent liberalization of the electricity market and increased environmental concerns as well as an increase in energy demand across the globe have brought the use of renewable energy sources such as wind energy to the fore. Some of the potential benets of renewable energy sources (RESs) are: localized generation, environmental-friendliness, generation of clean energy, reduction in greenhouse gas (GHG) emissions, increase in energy generation for increasing demand, and reduction in transmission losses. However, high penetration of RESs exposes power grids to several challenges. Some of these challenges for RESs are: increases in voltage prole level, high power losses, reverse power ow (RPF), protection and control issues. The main concern of this research work is RPF. RPF is a situation whereby excess power generated on a grid as a result of high integration or penetration of RES is fed back to the source of generation. RPF exposes power grids to various challenges; aside from causing grid instability. RPF incurs additional losses on the grid, causing over-voltage and overloading of the connecting elements such as conductors and transformers. In recent times, various control strategies have been deployed to mitigate these effcts on the grid. Energy management systems (EMSs) with energy storage devices (ESDs) are the most commonly applied strategies. However, intrusion into consumers' privacy and the high cost of energy storage devices poses a challenge to this approach. Voltage rise (VR) is one of the consequences of RPF. Line impedance reduction and reactive power compensation using exible AC transmission system (FACTS) devices are some of the methods use for voltage rise control. On-load tap changer transformers (OLTCs), generation curtailment and reverse power relay are also deployed to control RPF. However, reactive power compensation and generation curtailment approaches lead to power losses and voltage instability respectively. This thesis proposes a more secure method for utilising reverse power to supply power to modern electric vehicle (EV) charging stations through a multi-port power electronic transformer (MPPET). The proposed method consists of a RPF detection stage (RPFDS) electrically coupled to the point of common coupling (PCC), which discriminates between the total power generated on the grid and the actual load demand. A smart circuit breaker operates as soon as it picks up signal from RPFDS. The MPPET receives power from RPF utilization substation which is then used for electric vehicle (EV) charging. The method was validated experimentally in the laboratory. The results of the research work proved the ectiveness of the MPPET involtage regulation and in RPF utilisation