Multiple turbine flow distribution control using intelligent controller in mini hydro power plant

Abstract

Mini hydropower plants have been located at relatively small streams such streams often found upstream of a large river system. With a small catchment and high runoff, those stream parameters such as flow, debris content and water purity are highly dynamic with seasonal intense rainfall. To generate maximum electricity from river available flow, mini hydropower plants are equipped with multiple turbines. Thus, different flow combinations can be used to harvest maximum energy. The Present flow distribution control technique consists of a PID loop and a pre-tuned flow distribution table which allocates flow to each turbine by changing wicket gate opening depending upon available river flow. But as a result of variable stream parameters, water density and penstock friction losses will drastically change and therefore a flow distribution combination given by the existing controller may not be the optimum combination for the given moment. with alternating upstream rainfalls, most mini hydropower plants are not able to reach maximum design capacity even with 100% flow until clear water appears in the river. This research aims to introduce an artificial neuron network based intelligent controller which will measure flow variables and update the flow distribution table frequently ensuring optimum flow distribution and maximum power generation. “Moragaha Oya” mini hydropower plant located in Kandy district, Sri Lanka is selected as the test site for this research. Required additional hardware has been installed for the water quality and rainfall measurements. Developed intelligent controller with data collection and training software has been installed in power plant control computer enabling the update of flow distribution table. The intelligent controller has been trained and performance validation completed. A significant improvement of total output power has been obtained during performance validation, especially during the flow conditions where the existing controller could not obtain the optimum flow condition. the maximum output power increment is recorded as 3% of the previous maximum output power. By using energy harvest data of 47 days and rainfall data of the last 4 years, a 2% increment of annual energy harvest was also predicted during performance validation. Required further developments were also identified. The findings of this research may very important to renewable energy developers to optimize their powerplant yield.