Power systems
Hamid Reza Sezavar; Saeed Hasanzadeh
Abstract
Insulator pollution levels are critical for ensuring the operational stability and safety of power transmission systems. Traditional methods for detecting pollution are often invasive, inaccurate, and time-consuming. To address these issues, this study investigates the application of Artificial Intelligence ...
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Insulator pollution levels are critical for ensuring the operational stability and safety of power transmission systems. Traditional methods for detecting pollution are often invasive, inaccurate, and time-consuming. To address these issues, this study investigates the application of Artificial Intelligence (AI), specifically Gradient Boosting Machines (GBM), to classify insulator pollution levels based on Partial Discharge (PD) characteristics. We utilize a combination of time-domain and frequency-domain features extracted from PD signals to train a predictive model. The results indicate that the proposed model achieves a high classification accuracy, averaging between 92% and 95% across various contamination levels. Furthermore, the study analyzes the model's sensitivity to environmental factors, including humidity and Hydrophobicity Class (HC), revealing important insights that could influence classification performance. By employing this AI-driven approach, we aim to significantly enhance the efficiency of power grid maintenance, ultimately contributing to the long-term stability and reliability of transmission systems. The findings from this research underscore the potential of AI in revolutionizing pollution assessment methods and optimizing maintenance practices in power infrastructure.
Power systems
Hamid Reza Sezavar
Abstract
This paper presents a comparative study on the application of artificial intelligence for optimizing External Lightning Protection Systems (ELPS) in photovoltaic power (PV) plants. The research addresses the critical need for advanced protection systems in solar installations, which are particularly ...
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This paper presents a comparative study on the application of artificial intelligence for optimizing External Lightning Protection Systems (ELPS) in photovoltaic power (PV) plants. The research addresses the critical need for advanced protection systems in solar installations, which are particularly vulnerable to lightning strikes due to their expansive outdoor configurations. Through a detailed comparative analysis, the study evaluates multiple AI approaches, including metaheuristic algorithms and machine learning models. The investigation reveals that metaheuristic algorithms often have lower accuracy compared to modern AI techniques. All comparisons are based on a multi-level optimization framework, systematically addressing air termination design, grounding system configuration, and overall system integration. The results show superiority in sensitivity analysis in the transformer model. Compared to other models, the random forest (RF) model, along with the artificial neural network (ANN) model, has a higher speed in data analysis. However, physics-informed neural networks (PINN) achieve remarkable improvements, delivering 93% protection coverage with only 3.2% grounding error while significantly reducing design convergence times.
