IoT Enabled Low Cost Distributed Angle Measurement Fault Detection System for LFR Plants Academic Article in Scopus uri icon

abstract

  • IEEEThe advances in renewable solar energy generation have led to the development of Linear Fresnel Reflector (LFR) technology, in which solar energy is focused by means of a set of longitudinal mirrors onto a tubular receiver. The efficiency in the energy generation process is mainly given by the relation between the angle of incidence of incoming sunlight rays and the fixed location of the elevated tubular receiver. In order to maximize efficiency, the tilt angles for each one of the longitudinal mirrors should vary as a function of solar position tracking. The angular variation process is generally based on the use of encoders implementing a closed-loop feedback system to provide information to the controller in relation with actual mirror angular position. An alternative that has recently emerged is the use of inclinometers in order to obtain angular information, providing a cost-effective solution in order to employ angular information for mirror position control. In this work, a low cost monitoring system for LFR plants is presented, based on the use of a distributed configuration to obtain information related to mirror inclination angles. The proposed system is based on the use of accelerometers coupled to low cost wireless transceivers, enabling distributed real time data collection. The system allows fault or damage detection at all monitored mirror sections, avoiding long periods of efficiency loss of the LFR plant. The configuration and location of the wireless sensor nodes has been analyzed by means of deterministic 3D Ray Launching channel modeling, in order to optimize coverage/capacity relations within the scenario under test. The proposed system has been implemented enabling remote LFR facility monitoring by means of cloud-based infrastructure. In addition, the system is highly scalable, allowing the inclusion of other interesting applications such as intruder detection for security.

publication date

  • January 1, 2021