Optimization Analysis of Power Grid Data Quality by Federal Average Algorithm

This paper explores the application and benefits of the Federated Averaging (FedAvg) algorithm in optimizing power grid data quality as the power grid evolves toward more intelligent, data-driven systems, ensuring high-quality data becomes critical to the effective operation and management of the grid. However, optimizing data quality is a complex challenge due to the involvement of multiple data holders, each with privacy concerns that prevent the sharing of sensitive information. The FedAvg algorithm offers a promising solution by enabling the aggregation of data insights across distributed systems without the need to share raw data, thus preserving privacy while improving data quality. This study provides a comprehensive evaluation of the FedAvg algorithm’s impact on power grid data quality through a detailed implementation process. The research outlines the algorithm’s step-by-step optimization procedure, highlighting key design choices, such as model aggregation strategies, communication protocols, and iterative updates. By analyzing real-world application cases, we demonstrate how FedAvg addresses challenges such as data heterogeneity, missing data, and inconsistencies across different grid regions. Additionally, we present a series of experimental results that include a range of data quality metrics — such as accuracy, consistency, and reliability — to assess the algorithm’s effectiveness. The findings of this study show that the FedAvg algorithm can significantly enhance the accuracy and consistency of power grid data. Through its distributed approach, it not only improves the quality of data but also enhances the operational efficiency and reliability of the grid. The paper provides a clearer understanding of how FedAvg can be effectively implemented in power grid systems and its direct impact on data quality. This research contributes to the broader field of grid management by offering practical insights into leveraging federated learning techniques for data optimization while maintaining privacy, thus offering a more comprehensive and scalable solution for modern power grids.