Smart Grids in Industrial Paradigms: A Review of Progress, Benefits, and Maintenance Implications: Analyzing the Role of Smart Grids in Predictive Maintenance and the Integration of Renewable Energy Sources, Along With Their Overall Impact on the Industri

Chinedu Alex Ezeigweneme,; Chinedu Nnamdi Nwasike,; Adedayo Adefemi,; Abimbola Oluwatoyin Adegbite,; Joachim Osheyor Gidiagba,

doi:10.51594/estj.v5i1.719

Eng. sci. technol. j.

2024

DOI: 10.51594/estj.v5i1.719

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Smart Grids in Industrial Paradigms: A Review of Progress, Benefits, and Maintenance Implications: Analyzing the Role of Smart Grids in Predictive Maintenance and the Integration of Renewable Energy Sources, Along With Their Overall Impact on the Industri

Chinedu Alex Ezeigweneme,

Chinedu Nnamdi Nwasike,

Adedayo Adefemi

et al.

Abstract: This study provides a comprehensive analysis of the integration and impact of smart grids in the industrial sector, focusing on the evolution from traditional grids, the role of predictive maintenance, and the integration of renewable energy sources. The primary objectives were to understand how smart grids are reshaping industrial energy management and to assess their impact on efficiency, sustainability, and operational practices. A systematic literature review was employed as the methodology, utilizing elec… Show more

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Cited by 3 publications

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Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

Nachiappan

2024

International Journal of Innovative Science and Research Techno

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This paper explores innovative strategies for enhancing system efficiency in modern infrastructure by integrating artificial intelligence (AI), edge computing, and resource optimization techniques. As the complexity of infrastructure systems increases, traditional methods often fall short in addressing the evolving demands of operational efficiency and reliability. By leveraging AI algorithms for predictive analytics and resource allocation, and utilizing edge computing for real-time data processing, organizations can significantly improve performance and responsiveness. The study examines case studies that highlight successful implementations of these technologies across various sectors, including infrastructure monitoring, and grid maintenance. Insights from this research provide a framework for practitioners to adopt these advanced methodologies, ultimately leading to more resilient and efficient infrastructure systems.

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Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

Nachiappan

2024

International Journal of Innovative Science and Research Techno

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Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Painam

et al. 2024

JMC

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Machinery Fault Detection (MFD) is an important process in contemporary industrial systems, where it predicts possible physical failures before they lead to a serious problem. This uses multiple technologies to monitor machine statuses (algorithms, data gathering systems and sensors) Using a servo-motor driven actuator for deployment, the Locking Mechanism is pre-assembled into an OEM ATE and will enable predictive failure mode identification (via monitoring and warnings of operational parameters i.e., vibration, temperature or auditory signals in-built to MFD systems) leading to Prophylactic maintenance before critical bottlenecks can occur. The dataset we used in our study was collected from Kaggle and it is called the SpectraQuest Machinery Fault Simulator (MFS) Alignment-Balance-Vibration (ABVT). We used LSTM Autoencoder, KNN, SVM and DNN to analyzed the data. Our LSTM Autoencoder model was very accurate and achieved a precision, recall, accuracy and F-score of 99%. We worked on very large scale datasets. It will help the system detect faults and predict their evolution over time, so you save maintenance costs and increase production in your factory. More research on the practical efficiency of these models in real-time across different industrial settings can create a path towards improved and scalable MFD solutions.

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Development and Implementation of an ESP32 IOT-Based Smart Grid for Enhanced Energy Efficiency and Management

Sabo,

Suleiman,

Dahiru

et al. 2024

ejtas

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The advent of the Internet of Things (IoT) has ushered in transformative changes across diverse sectors, notably the energy domain, spawning the innovative concept of smart grids. This research delves into the development and deployment of an IoT-based prototype smart grid system, aiming to augment energy efficiency, reliability, and management. The system integrates current and voltage sensors, coupled with an ESP32 microcontroller, enabling real-time monitoring, control, and optimization of the prototype electrical grid. Leveraging Google Firebase as a cloud service for storing real-time data (current, voltage, and power), the prototype includes an architectural model simulating industrial, commercial, and residential areas within a city. The model features illumination controlled by three output relays linked to the ESP32 via a 2N222 transistor. A grid control interface, developed with JavaScript and React, interfaces with the Firebase real-time server to manage relay states. This interface empowers a distribution company to remotely designate powered sections, mimicking scenarios like sectional maintenance or compulsory load shedding. The collaborative effort in mini-grid design underscores the efficiency gains achieved through IoT implementation in conventional electrical grid systems, emphasizing time and labor savings in energy management.

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Smart Grids in Industrial Paradigms: A Review of Progress, Benefits, and Maintenance Implications: Analyzing the Role of Smart Grids in Predictive Maintenance and the Integration of Renewable Energy Sources, Along With Their Overall Impact on the Industri

Cited by 3 publications

References 33 publications

Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

Enhancing System Efficiency through AI, Edge Computing, and Resource Optimization in Modern Infrastructure

Intelligent Fault Diagnosis in Industrial Machinery: Leveraging AI with LSTM Autoencoder for Enhanced Fault Detection

Development and Implementation of an ESP32 IOT-Based Smart Grid for Enhanced Energy Efficiency and Management

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