Computational intelligence based analysis of power distribution data

能源生产过程的进步以及客户的需求和期望导致电网的快速变化和修改。由于越来越多的清洁能源和小型独立能源生产商的潜在贡献，在发电端观察到配电网的可变性和不确定性；在负荷方面，由于能源消耗概况的变化，连接到电网的设备的性质，以及能源存储的增加。监测和数据收集实践的改进为更全面地建模和管理网格的操作提供了机会。电网可以通过集成人工智能技术来分析和优化其运行，从而增强和提高效率。高级数据分析方法应该能够解决由于中断、天气模式和资产相关性能而导致的服务平等退化问题。该项目旨在应用机器学习/数据挖掘和计算智能方法分析配电系统数据，并提出一种处理这些数据的方法，以预测和识别配电系统中导致停电的潜在问题。预计开发的方法将导致更快的分析时间和发现新的见解，旨在增加和预测配电网的可靠性。

Advances in energy generation processes as well as in customers' needs and expectations lead to fast changesand modifications of a power grid. Variability and uncertainties in a power distribution grid are observed on thegeneration side due to growing involvement of clean energy sources and potential contributions of small,independent energy producers; and on the load side due to changes in profiles of energy consumption, nature ofdevices connected to a grid, and increased presence of energy storages. Improvements in monitoring and datacollection practices provide opportunities to more comprehensive modelling and managing operations of a grid.A power grid can be enhanced and become more efficient by integrating techniques of artificial intelligence inanalysis and optimization of its operations. Advanced data analysis methods should be able to address a servicequality degradation due to outages, weather patterns and asset related performance.The proposed project aims at applying Machine Learning/Data Mining and Computational Intelligencemethods for analysis of power distribution system data, and proposing a methodology for processing this datain order to foresee and identify potential problems in power distribution system leading to power outages.It is expected that developed methodology will lead to faster analysis time and discovery of new insightsaiming at augmenting and predicting reliability of a power distribution grid.