Toward smarter solar: IoT and fuzzy logic for real-time maximum power point tracking and monitoring of photovoltaic systems

Design and Implementation of an IoT based Solar Plant Monitoring and Performance Analysis System

The increasing demand for renewable energy has highlighted the need for efficient and reliable management of solar power plants. Solar plants, whether residential or industrial, require continuous monitoring to ensure optimal performance, detect faults, and maximize energy generation. Manual supervision is often time-consuming, prone to errors, and insufficient for detecting real-time anomalies. To address these challenges, this paper presents an IoT-based Solar Plant Monitoring System that enables remote, real-time observation and management of solar energy systems. The proposed system integrates sensors, microcontrollers, and IoT-enabled communication modules to collect key parameters such as solar panel voltage, current, temperature, and battery status. These parameters are transmitted over the internet to a centralized cloud platform, allowing plant operators to monitor system performance from any location using smartphones, laptops, or tablets. Alerts are generated automatically in case of abnormal readings, such as low voltage, panel overheating, or battery faults, facilitating proactive maintenance and minimizing downtime. By leveraging IoT technology, the system not only improves the efficiency and reliability of solar power generation but also reduces operational and maintenance costs. The proposed system supports data logging, historical analysis, and performance optimization. Additionally, the integration of cloud-based monitoring ensures scalability and flexibility, allowing expansion to multiple solar plants and large-scale installations.

Power Quality Management for Solar-Based Microgrids Using Integrated Artificial Intelligence and Internet of Things Technologies

An integrated artificial intelligence and Internet of Things-based smart power quality management system for a solar microgrid, incorporating a DC–DC boost converter and a maximum power point tracking system for improving the power conversion efficiency.

INTEGRATED PERFORMANCE AND IOT-BASED RELIABILITY ASSESSMENT OF OFF-GRID PV SYSTEMS

Island regions often face unreliable electricity supply due to limited grid infrastructure, resulting in high dependence on diesel generators with significant economic and environmental drawbacks. This study aims to evaluate the performance and reliability of an Internet of Things (IoT)-integrated off-grid solar photovoltaic (PV) system implemented in a small island setting. A 400 Wp off-grid PV system was deployed on Pulau Puteri, Indonesia, and equipped with an IoT-based monitoring platform to enable real-time acquisition of operational data. The methodology includes system design, installation, and continuous monitoring over a 30-day period with 5-minute data intervals. System performance was assessed using Performance Ratio (PR) and Capacity Factor (CF). The system produced 1.6–2.1 kWh/day with PR values of 0.75–0.82 and CF of 16.7%–21.9%, indicating efficient and stable operation under tropical conditions. IoT monitoring enabled continuous data acquisition and early anomaly detection, improving operational reliability. The results confirm that the system meets international performance benchmarks and provides a feasible solution for decentralized energy systems in remote islands. The study contributes an integrated framework combining real-time IoT monitoring and standardized performance evaluation using field data.  

Real-Time Monitoring and Fault Detection System for Solar Photovoltaic Installations

Intelligent IoT based Load Management System with Real Time Monitoring in Renewable Energy System

IoT is one of the significant enabling technologies in the contemporary energy landscape. This work addresses intelligent load management under fault, under-load, and overload conditions. Electrical equipment requires automatic and rapid response to avoid damage and prevent service interruption. In contrast to traditional circuit breakers that disconnect the entire system, the proposed architecture employs ESP32-based intelligent control, together with ACS712 current sensors, to provide accurate per-load current measurements and load-specific overcurrent protection. The system is also capable of selective load isolation, meaning that it maintains the operation of healthy circuits and automatically disconnects only the faulty load without affecting other loads. Cloud-based monitoring is implemented via the ThingSpeak platform, enabling real-time remote system surveillance through any internet-connected device. The faulty section of the system is readily identified from the current readings visualised on the cloud dashboard and mobile interface. The proposed system is powered primarily by solar photovoltaic sources and can also operate from the utility grid, providing flexibility across renewable and conventional supply scenarios. Experimental testing demonstrates protection response times in the sub-200 ms range and high-accuracy current monitoring with a mean absolute error below 0.05 A. The key contributions include: selective load protection, real-time IoT-based energy analysis, and a cost-effective open-hardware architecture. The work advances the safety and efficiency of smart energy control systems in distributed renewable energy environments.

Smart Energy Optimization and Protection System with IoT

The rapid growth in electricity consumption has increased the need for intelligent energy management and protection systems. Conventional electrical monitoring systems lack real-time analysis and remote accessibility, leading to energy wastage and delayed fault response. This paper presents a Smart Energy Optimization and Protection System using Internet of Things (IoT) technology for efficient monitoring, fault detection, and energy management. The proposed system continuously monitors electrical parameters such as voltage, current, and power consumption using sensors interfaced with an embedded controller. The collected data is transmitted to a cloud platform through IoT connectivity, enabling real-time monitoring and remote access through mobile or web applications. The system identifies abnormal conditions such as overload, short circuit, over-voltage, and excessive energy consumption using threshold-based analysis. Upon detecting faults, immediate alerts and protection mechanisms are activated to prevent equipment damage and improve system safety. Additionally, the system provides energy optimization suggestions to reduce unnecessary power consumption. The integration of IoT enhances system scalability, accessibility, and efficiency, making the proposed model suitable for residential, commercial, and industrial applications.,