Research on Optimization of Intelligent Data Driven Monitoring and Status Evaluation Mechanism for Distribution Network and Distributed Resources
DOI:
https://doi.org/10.13052/dgaej2156-3306.3963Keywords:
Distributed distribution network, data driven, state assessment, mechanism optimizationAbstract
With the rapid development of smart grid technology, in-depth research on intelligent data-driven monitoring and status evaluation mechanisms for distribution networks and distributed resources has become a key factor in improving the operational efficiency, safety, and reliability of power systems. This article aims to achieve precise management and optimized scheduling of distribution networks and distributed resources by establishing an efficient and intelligent monitoring and evaluation system. We have collected over 10TB of data from multiple smart distribution network pilot projects, including real-time operational data, equipment status information, user electricity usage behavior, and more. By adopting advanced data preprocessing techniques, including data cleaning, integration, and transformation, low-quality and incomplete data are effectively eliminated, ensuring the integrity and quality of the dataset. Subsequently, the processed data is deeply mined and analyzed using a distributed computing framework. The prediction model proposed in this article provides high-precision predictions of key indicators, such as load changes and power generation within the distribution network, with an average prediction accuracy of over 95%. By utilizing clustering analysis and association rule mining techniques, potential fault points within the distribution network were successfully identified, furnishing scientific decision-making support for operations and maintenance personnel. In the realm of distributed resource state assessment, a novel state assessment model grounded in multi-source data fusion has been introduced. This model comprehensively considers the operational characteristics of distributed energy, environmental factors, and grid constraints and can comprehensively and accurately evaluate the status of distributed resources. The experiment found that this system significantly improved the utilization of distributed resources and the overall operational efficiency of the power grid, with an average increase of over 10%.
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