Neuro-Fuzzy-Based IoT Assisted Power Monitoring System for Smart Grid

Abstract

The Internet of Things (IoT) is commonly utilized for intelligent energy control, industrial automation, and a host of other applications. IoT sensors are installed in various stages of the smart grid (SG) to track and manage network statistics for safe and efficient power delivery. The challenges in the integration of IoT-SG must be overcome for the network to function efficiently. An IoT-based smart grid energy monitoring system depending on neuro-fuzzy is proposed in this paper. At the core of the operator, a wireless sensor network (WSN) is employed to calculate and transfer the necessary parameters for the prediction model. This project revolves around an IoT-based energy monitor, which can track and analyze electrical parameters, including current, voltage, active power, and load power consumption. For the collection of realtime electrical data from users, the IoT-based program is used. Based on this data, consumers and electric power companies in the SG model can better control their usage to minimize billing costs. The results obtained show that the performance of hybridized solar/wind power plants will be improved with the help of ANFIS controller to a great extent. Results indicate efficiency of 99.74% in the proposed ANFIS control system.

IoT-Based Alternating Current Electrical Parameters Monitoring System

“…Some works measured parameters that this document does not present in our proposal. In [28,31], the authors obtained harmonic distortion, but these proposals do not compare their readings over a considerable period of time with respect to monitors with similar features. Similarly, there is a compact device for measuring electrical parameters [41], which describes a power-quality detector built with a low-cost microcontroller; in this case, the device is limited to data acquisition.…”

“…In [28], the authors developed an IoT-based energy monitor using an Arduino board, which can track and analyze electrical parameters, including current, voltage, active power, and energy consumption; these data are used to control hybrid solar and wind power plants through a smart grid. On the other hand, in [29], a smart household distribution system was developed that allows the collection and storage of voltage, current, and power data, presenting the information locally on two LCD screens and remotely on a mobile and web application.…”

Integration of Renewable Energy and Microgrid Systems to Enhance Voltage Quality and Minimize Harmonic Distortion Losses Using Advanced Control Techniques

“…Microgrids, equipped with renewable sources such as solar and wind power, offer decentralized solutions to energy generation and distribution, promising improved energy reliability and efficiency. However, the variability and intermittency inherent in renewable sources pose significant challenges to voltage regulation and can lead to harmonic distortions within the electrical grid [1]. Addressing these challenges requires sophisticated control strategies capable of dynamically managing system operations under varying conditions.…”

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

“…A complementary model, called a neuro-fuzzy system, was proposed by combining the advantages of fuzzy models and neural networks to address the abovementioned issues [1][2][3][4][5]. Studies are actively being conducted on neuro-fuzzy inference systems [6][7][8][9][10][11][12][13][14][15]. Similar to FIS, neuro-fuzzy inference systems are expressed by fuzzy rules.…”