Load monitoring is the practice of measuring electrical signals in a domestic environment in order to identify which electrical appliances are consuming power. One reason for developing a load monitoring system is to reduce power consumption by increasing consumers' awareness of which appliances consume the most energy. Another example of an application of load monitoring is activity sensing in the home for the provision of healthcare services. This paper outlines the development of a load disaggregation method that measures the aggregate electrical signals of a domestic environment and extracts features to identify each power consuming appliance. A single sensor is deployed at the main incoming power point, to sample the aggregate current signal. The method senses when an appliance switches ON or OFF and uses a two-step classification algorithm to identify which appliance has caused the event. Parameters from the current in the temporal and frequency domains are used as features to define each appliance. These parameters are the steady-state current harmonics and the rate of change of the transient signal. Each appliance's electrical characteristics are distinguishable using these parameters. There are three Types of loads that an appliance can fall into, linear nonreactive, linear reactive or nonlinear reactive. It has been found that by identifying the load type first and then using a second classifier to identify individual appliances within these Types, the overall accuracy of the identification algorithm is improved.
Hybrid power infrastructure is often viewed as an alternative system configuration for off-grid telecom base stations. These systems are used to reduce the fuel consumption of diesel generator sites and often rely on wind or solar to supplement some of the power to the load. The ability of a site to use the renewable energy efficiently becomes more critical on sites where the quality of the wind or solar resource is poor or highly variable. The long term and short term effects of wind and solar power contributions to the system are demonstrated using historical wind speed/solar irradiation data over a 3 year period. A feasible approach is proposed to improve the efficiency of the system by analysing the rate of change of the renewable energy source(s). Using several successive measurements, the battery charging system can be restrained temporarily to allow for the initial stages of diurnal weather patterns. These patterns may later negate the need for the charging cycle which can result in wasted power should a renewable power source increase its power output during a generator charging cycle.
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Off grid telecom base stations in developing nations are powered by diesel generators. They are typically oversized and run at a fraction of their rated load for most of their operating lifetime. Running generators at partial load is inefficient and, over time, physically damages the engine. A hybrid configuration uses a battery bank, which powers the telecoms' load for a portion of the time. The generator only operates when the battery bank needs to be charged. Adding a wind turbine further reduces the generator run hours and saves fuel. The generator is oblivious to the current wind conditions, which leads to simultaneous generator-wind power production. As the batteries become charged by the generator, the wind turbine controller is forced to dump surplus power as heat through a resistive load. This paper details how the relationship between barometric pressure and wind speed can be used to add intelligence to the battery charger. A Simulink model of the system is developed to test the different battery charging configurations. This paper demonstrates that if the battery charger is aware of upcoming wind conditions, it will provide modest fuel savings and reduce generator run hours in small-scale hybrid energy systems.
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