Fault Detection and Diagnosis in Distributed Energy Systems using Machine Learning

Introduction: In the evolving landscape of energy systems, the transition from centralized to distributed energy systems (DES) presents new challenges. With the increasing popularity of renewable energies, the demand for reliable and efficient power systems is on the rise. However, this shift brings forth complexities, particularly in fault detection within decentralized systems. Traditional methods of manual inspection are time-consuming and prone to human error, and the unpredictability introduced by renewable sources further complicates fault identification.

Problem Statement: The project addresses the critical issue of fault detection in distributed energy systems. Detecting faults is paramount for maintaining microgrids, ensuring power continuity, and stabilizing the grid. The consequences of energy system faults range from localized blackouts to large-scale grid failures. Differentiating between major and minor faults is challenging, with false alarms leading to operational interruptions and resource waste. The diversity of fault types adds complexity, requiring specific reactions. Handling datasets of simulated measurements adds another layer of challenge, demanding careful data preparation for machine learning model training.

Approach: To tackle these challenges, the project proposes a novel approach using machine learning (ML) for fault detection in DES. ML’s ability to learn from data and analyze patterns in electrical data, such as line voltages and currents, makes it a promising solution. The project emphasizes the relevance of ML in the context of the global shift towards smart grids and the broader digitization and automation of the energy industry.

Results: The project demonstrated significant progress and promising results. The RandomForestClassifier model exhibited remarkable accuracy, achieving 99.99% in the initial binary classification dataset. When applied to the more complex multi-class dataset, accuracy slightly decreased to 87%, highlighting the challenges of identifying multiple faults. The observed difference in accuracy emphasizes the importance of testing ML models in diverse situations. Recognizing the need for further tuning, the project sets the groundwork for enhancing DES through refined fault detection.

Conclusion: In conclusion, the project contributes valuable insights into fault detection in distributed energy systems using machine learning. The success of the RandomForestClassifier model in achieving high accuracy underscores the potential impact of ML on improving fault detection efficiency. As the energy industry progresses towards digitalization and automation, incorporating ML into systems becomes essential for robust and reliable performance.

 

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