Photovoltaic (PV) systems need measurements of incident solar irradiance and PV surface temperature for performance analysis and monitoring purposes. Ground-based network sensor measurement is preferred in many near real-time operations such as forecasting and photovoltaic (PV) performance evaluation on the ground. Hence, this study proposed a Fuzzy compensation scheme for temperature and solar irradiance wireless sensor network (WSN) measurement on stand-alone solar photovoltaic (PV) system to improve the sensor measurement. The WSN installation through an Internet of Things (IoT) platform for solar irradiance and PV surface temperature measurement was fabricated. The simulation for the solar irradiance Fuzzy Logic compensation (SIFLC) scheme and Temperature Fuzzy Logic compensation (TFLC) scheme was conducted using Matlab/Simulink. The simulation result identified that the scheme was used to compensate for the error temperature and solar irradiance sensor measurements over a variation temperature and solar irradiance range from 20 to 60 °C and from zero up to 2000 W/m2. The experimental results show that the Fuzzy Logic compensation scheme can reduce the sensor measurement error up to 17% and 20% for solar irradiance and PV temperature measurement.
Weather data monitoring is important due to the drastic changes of weather around the world in recent years. Weather-related parameters are commonly measured using weather stations. Low-cost Arduino based weather station is suitable for public use to monitor local weather conditions in the vicinity of their residential area. This will enable quick response in natural disaster caused by the weather. In this work, the integration of water level and waterflow rate detection to the weather station allows hydrological data measurement in addition to the meteorological data. This capability makes the weather station suitable for further expansion into a wireless sensor network for flood monitoring and prediction. This weather station was developed using NodeMCU ESP32 microcontroller, ultrasonic sensor, BMP280 sensor, rain sensor and waterflow sensor. The remote data monitoring capability was realized using Blynk IoT platform. A prototype of this weather station was developed and tested in a simulated environment. Results show that this weather station is able to provide good measurement of meteorological and hydrological data.
Biomass concentration is an important indicator of production rate in polyhydroxyalkanoates (PHA) fermentation process. In current practice, measurement of biomass concentration is done off-line by laboratory analysis that is unsuitable for online process monitoring and control. Soft-sensor is often used as an alternative that provides an estimate of hard to measure parameters from easy to measure process data. However, most of these studies use simulated data or data generated from mathematical model that was developed without full consideration of process and measurement uncertainty. In this study, a soft-sensor is developed from real production data for PHA fermentation in pilot-scale bioreactor with the appropriate data pre-processing techniques applied to process data that was obtained from this system. Multilayer perceptron (MLP) neural network is used to estimate biomass concentration using secondary process parameters such as dissolved oxygen (DO), temperature, pH and agitation speed as inputs. Different models are developed based on different batches of production data and various network architecture in order to study the appropriate integration of process data and network topology that gives the best model accuracy. Results indicate that the biomass soft-sensor developed using MLP-ANN provides a better estimate of biomass in comparison to radial basis function (RBF) neural network and support vector regression (SVR) methods. The developed soft-sensor can be further used in monitoring and control of production output.
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