Design and analysis of hybrid power system and IoT SCADA system for remote sites
Bibliographic record
Abstract
In many developing countries, fossil fuels remain the primary energy source for power generation due to their relative affordability and availability. However, this reliance on fossil fuels significantly con-tributes to CO₂ emissions and environmental issues, such as floods driven by climate change. Addition-ally, the cost of power generation from fossil fuels is often high, posing economic challenges. Therefore, there is an urgent need to transition towards renewable energy sources, which offer a more sustainable and cost-effective solution. To address this issue, the design and analysis of hybrid power systems for remote sites have been carried out in Pakistan. These sites are in isolated areas without access to electricity and other infrastructure, where power is generated by burning fossil fuels. This practice is a significant source of CO₂ emissions and contributes to flooding. The aim is to integrate renewable energy sources, such as solar energy, with traditional generators to mitigate emissions and enhance energy access in remote sites in Pakistan. The system is designed using a DC-DC converter, Maximum Power Point Tracking, LCL filter, and a DC-AC inverter. Utilizing software tools like PVsyst 7.4 and HOMER Pro-3.18.1 the study evaluates system sizing, energy consumption patterns, sensitivity analysis, and optimization strategies tailored to site-specific data. The analysis of connected and peak loads demonstrates the system’s promise of providing reliable electricity with minimal environmental impact. The estimated capital cost and energy generation per unit underscore its economic feasibility. Dynamic modeling of the proposed hybrid power system has been performed using MATLAB R2023b, and validation through Hardware in the Loop examines the system’s behavior in response to variations in solar irradiance and temperature, offering insights into operational efficiency and reliability. For monitoring and control, an Internet of Things-based Supervisory Control and Data Acquisition system has been developed. Utilizing Blynk alongside Arduino Nano, GSM SIM800L, and ESP-32, the system integrates essential components like ZMPT101B voltage sensor, ACS712 current sensors, MPPT for optimizing power output, a relay, battery bank, buck converter, and PV panels. Operating on both GSM and Wi-Fi, it ensures seamless data transfer. Data transmission between Arduino Nano, GSM SIM800L, and ESP-32 employs Universal Asynchronous Receiver/Transmitter (UART) serial communication protocols, programmed in Arduino IDE 1.8.19 using C++. As an open-source SCADA platform, it enables remote load management via the Blynk app and console, compatible with Android and iOS, using Transmission Control Protocol/Internet Protocol (TCP/IP) and Hypertext Transfer Protocol (HTTP) protocols for data exchange. Real-world testing validated its reliability across GSM and Wi-Fi networks, demonstrating robust performance in rural environments. Live data monitoring and control through the Blynk app showcased its capabilities. With a budget-friendly cost of CAD 35.52 and mini-mal power consumption at 3.462 W, this approach underscores IoT's potential to enhance efficiency and user engagement within renewable energy management systems. The thesis demonstrates that the designed hybrid power system is economically feasible, reliable, and safe, which will help reduce carbon emissions and provide low-cost electricity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".