MPPT Optimization using Ant Colony Algorithm: Solar PV applications

Solar photovoltaic systems' power output depends on the local solar radiation levels among other prevailing climatic parameters such as temperature, dust, and humidity. In fact, the same radiation levels at low temperatures would increase the solar panel output compared to higher ambient temperatures. In this case, the voltage, the current, and the power levels would fluctuate accordingly. Among the three quantities, the voltage and current levels are monitored and often controlled to give regulated voltage and optimal power at the same time. To do so, various techniques are used to enhance the efficiency of solar PV by means of maximum power point tracking (MPPT). The main aim is to set the PV array to operate at optimal power or close to it even when environmental conditions fluctuate. The conventional incremental conductance (INC) algorithm is one of the MPPT approaches, but its fixed step size response speed is low under rapidly changing in the solar irradiation level and or load. To improve response time, a new MPPT technique using the Ant Colony (ACO) approach for photovoltaic (PV) systems is developed and discussed in this work. Artificial ants are released into the problem space to forage, and those that discover a better source of food are kept while the number of ants that are unable to successfully forage is deleted.