Khalid, Raja Rehan; Mayer, Angelika; Meiers, Josef; Beck, Horst P.; Frey, Georg.: Predictive Power Management for a Solar-Powered Off-Grid Surface Water Quality Monitoring System. 11th IEEE Conference on Industrial Electronics and Applications (ICIEA 2016) Hefei, China, June. 2016.
The paper investigates the mobile real time measuring station incorporating renewable energies to monitor the surface water quality. This type of measuring stations are designed to establish the Water Framework Directive (WFD) perceived by European Commission. The core goal in the discussed work is to attain self-reliance by the mobile monitoring station, by maximizing the use of renewable source energy and limit the utilization of energy produced by conventional ways. To attain such goal, further enhancement is achieved in such system by realizing Power Management System (PMS) incorporating predictive controls, to overcome the fluctuating behaviour for automated data collection for surface water quality and power failure in renewable energy system. To include the prediction within the measuring station PMS, Support Vector Machines (SVM) as a machine learning method is considered. The paper also presents architecture for predictive power management system for real time measuring station as a case study model.
Surface Water Quality, Power Management System, Predictive Controls, Renewable Energy System.