Spatial irradiance estimation in a thermosolar power plant by a mobile robot sensor network

Abstract

An algorithm for mapping Direct Normal Irradiance (DNI) in thermosolar power plants using a Mobile Robotic Sensor Network (RSN) is presented. The algorithm selects measurements spots and allocates the RSN accordingly to carry out the dynamic estimation of DNI. A generic thermosolar power plant with a fleet of vehicles is used as a simulated case study to assess the performance of the algorithm. The results show that the proposed method allows us to obtain a spatial estimation of the DNI that improves the flow control in the loops of the plant, outperforming estimations based on a single pyrheliometer.

Reinforcement Optimization Algorithm for Mobile Robot Sensor Networks Drive Motion Improvement

“…Path P(x i , y i ) opted for the mobile robot within the bounded map (x b , y b ) values are chosen randomly from the initial sensor values. The result of the connecting points form a path to decide the preliminary control values to operate the sensor redefines (1). The implementation of the path inside the map agreed from the state of mobile robot with starting and ending values should match with the path.…”

“…The probabilistic path map should be created to perform the free robot moving space with adjustable velocity speed among locations decided from (1). Path calculation from these spots to avoid the boundaries that are present due to the robot's size is dynamically changing.…”

“…The estimation of the path of mobile robot sensor networks is being analyzed for a wide range of applications, considering the wide range of parameters [1]- [3]. These parameters are greatly capable with the working environment and the boundaries with increasing size of obstacles.…”

Control predictivo de sistemas ciberfísicos

“…Actualizar la estimación distribuida de la irradiancia recibida en la planta según la información recolectada por los robots. Martín et al (2021c) abordan esta problemática desde una perspectiva de inferencia Bayesiana considerando que en cada punto del campo hay una distribución de probabilidad asociada a la estimación de la irradiancia local, que puede ser actualizada por un conjunto de M medidas M = {m 1 , m 2 , ..., m j , ..., m M } en el que se asocia una importancia ϕ j a cada medida j en función de aspectos como el tiempo transcurrido desde la última medida en la zona, la velocidad y posición conocida de las nubes o la entropía de Shannon. En particular, se asume que cada medida aporta información sobre el área en el que fue tomada, disminuyendo la incertidumbre del valor de irradiancia estimado con proporcionalidad inversa a la distancia del punto de medida.…”

Ecotourism design and plant protection based on sensor network

“…To achieve this, the proposed model is based on an accurate prediction of the complex permittivity (CDC). Martin et al (2021) proposed an algorithm to map direct normal irradiance (DNI) in thermal solar power plants using a mobile robotic sensor network (RSN). The algorithm selects measurement points and assigns RSN accordingly for dynamic estimation of DNI.…”