Long-Range Wireless Mesh Network for Weather Monitoring in Unfriendly Geographic Conditions

In this paper a long-range wireless mesh network system is presented. It consists of three main parts: Remote Terminal Units (RTUs), Base Terminal Units (BTUs) and a Central Server (CS). The RTUs share a wireless network transmitting in the industrial, scientific and medical applications ISM band, which reaches up to 64 Km in a single point-to-point communication. A BTU controls the traffic within the network and has as its main task interconnecting it to a Ku-band satellite link using an embedded microcontroller-based gateway. Collected data is stored in a CS and presented to the final user in a numerical and a graphical form in a web portal

A Survey of Hybridisation Methods of GNSS and wireless LAN based Positioning System

Self-localisation has become a matter of course in our daily life. The emerging market of mobile devices still boosts the demand for seamless, ubiquitous positioning. The repertoire of sensors serviceable for localisation provided by current mobile devices is large; however the main positioning systems used today are based on wireless local area networks (WLAN), cellular networks and certainly global navigation satellite systems (GNSS). Because of the accuracy, the vast deployment and the channel characteristics, researchers have been focused on WLAN based positioning systems (WPS) in particular, to achieve seamless positioning. This paper reviews the latest data fusion approaches to seamless positioning by GNSS and WPS. In accordance to the level of data fusion, several approaches are categorised and briefly presented, differences in performance of these approaches are highlighted and future challenges identified.publishedVersionPeer reviewe

Sensorless FOC Performance Improved with On-Line Speed and Rotor Resistance Estimator Based on an Artificial Neural Network for an Induction Motor Drive

Three-phase induction motor drive requires high accuracy in high performance processes in industrial applications. Field oriented control, which is one of the most employed control schemes for induction motors, bases its function on the electrical parameter estimation coming from the motor. These parameters make an electrical machine driver work improperly, since these electrical parameter values change at low speeds, temperature changes, and especially with load and duty changes. The focus of this paper is the real-time and on-line electrical parameters with a CMAC-ADALINE block added in the standard FOC scheme to improve the IM driver performance and endure the driver and the induction motor lifetime. Two kinds of neural network structures are used; one to estimate rotor speed and the other one to estimate rotor resistance of an induction motor

SCADA-Based Heliostat Control System with a Fuzzy Logic Controller for the Heliostat Orientation

In central receiver systems, there are local controls that modify the position of the heliostats, where the trend is to increase the intelligence of the local controls in order to give them greater autonomy from the central control. This document describes the design and construction of a SCADA (Supervisory Control and Data Acquisition)-based heliostat control system (HCS) with a fuzzy logic controller (FLC) for the orientation control. The HCS includes a supervisory unit with a graphical user interface, a wireless communication network, and a stand-alone remote terminal unit (RTU) implemented on a low-cost microcontroller (MCU). The MCU uses a solar position algorithm with a maximal error of 0.0027° in order to compute the position of the sun and the desired angles of the heliostat, according to a control command sent by the supervisory unit. Afterwards, the FLC orients the heliostat to the desired position. The results show that the RTU can perform all the tasks and calculations for the orientation control by using only one low-cost microcontroller with a mean squared error less than 0.1°. Besides, the FLC orients the heliostat by using the same controller parameters in both axes. Therefore, it is not necessary to tune the controller parameters, as in the traditional PID (Proportional-Integral-Derivative) controllers. The system can be adapted in order to control other two-axis solar-tracking systems

A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks

Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel Multitasking Deep Neural Network (MDL) for the classification and analysis of multiple electrical disturbances. The characteristics are extracted using a specialized and adaptive methodology for non-stationary signals, namely, Empirical Mode Decomposition (EMD). The methodology’s design, development, and various performance tests are carried out with 28 different difficulties levels, such as severity, disturbance duration time, and noise in the 20 dB to 60 dB signal range. MDL was developed with a diverse data set in difficulty and noise, with a quantity of 4500 records of different samples of multiple electrical disturbances. The analysis and classification methodology has an average accuracy percentage of 95% with multiple disturbances. In addition, it has an average accuracy percentage of 90% in analyzing important signal aspects for studying electrical power quality such as the crest factor, per unit voltage analysis, Short-term Flicker Perceptibility (Pst), and Total Harmonic Distortion (THD), among others