A Variable Speed Synchronous Motor Approach for Smart Irrigation using Doubly Fed Induction Motor

Department of Electrical Engineering, College of Engineering, Jazan University, Jizan 45142, Saudi Arabia.Doubly Fed Induction Motor (DFIM) is a popular machine used in variable speed drives, and its ruggedness, reliability and simplicity of speed control make it a suitable candidate for use in smart irrigation systems. This paper studies and evaluates the performance of DFIM at different operating conditions and shows that it can be viewed as a variable speed synchronous motor. The research results reveal that DFIM can be used to control the flow rate of water in irrigation systems, by adjusting the speed of the motor to match the desired flow rate. A mathematical model has been developed to optimize the performance of the DFIM in smart irrigation systems, taking into account the specific conditions of the application. In addition, an experimental setup was built and tested to enhance the theoretical results, which showed good correlation between the theoretical and experimental results. The results of this research demonstrate the potential of using the DFIM in smart irrigation systems to improve the performance and efficiency of irrigation and to provide better control and lower costs

Dr.

Department of Electrical Engineering, College of Engineering, Jazan University, Jizan 45142, Saudi Arabia

Enhancing Smart Irrigation Efficiency: A New WSN-Based Localization Method for Water Conservation

The shortage of water stands as a global challenge, prompting considerable focus on the management of water consumption and irrigation. The suggestion is to introduce a smart irrigation system based on wireless sensor networks (WSNs) aimed at minimizing water consumption while maintaining the quality of agricultural crops. In WSNs deployed in smart irrigation, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a new localization method for localizing unknown sensor nodes in WSN-based smart irrigation systems using estimated range measurements. The proposed method can accurately determine the positions of unknown nodes, even when they are located at a distance from anchors. It utilizes the Levenberg–Marquardt (LM) optimization algorithm to solve a nonlinear least-squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the proposed method, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes. Specifically, it achieves 19% and 58% improvement in estimation accuracy when compared to distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. Additionally, the method exhibits robustness against measurement noise and scalability for large-scale networks. Ultimately, integrating the proposed localization method into the smart irrigation system has the potential to achieve approximately 28% reduction in water consumption