Data-driven system for predicting outages and their severity

能源生产过程的进步以及客户需求和期望的进步导致电网的快速变化和修改。在发电侧，由于越来越多的清洁能源的参与和小型独立能源生产商的潜在贡献，在配电网中观察到了变异性和不确定性；在负荷侧，由于能源消耗概况、与电网连接的设备的性质以及更多的储能装置的存在而变化。监测和数据收集做法的改进为更全面地模拟和管理网格的运作提供了机会。通过将人工智能技术集成到电网运行分析和优化中，可以提高电网的性能和效率。先进的数据分析方法应该能够解决因停机、天气模式和资产相关性能而导致的服务质量下降问题。 该项目旨在将机器学习/数据挖掘和计算智能方法应用于配电系统数据分析，并开发一种处理这些数据的方法。其目标是构建一个预测停电及其严重程度的系统。拟议的系统将利用天气数据和系统拓扑描述来进行预测。 预计开发的预测系统将导致识别停电发生的可能性及其对客户和电力系统的影响。此外，预计在本项目期间进行的工作将导致发现旨在增强和预测配电网可靠性的新见解。

Advances in energy generation processes as well as in customers' needs and expectations lead to fast changes and modifications of a power grid. Variability and uncertainties in a power distribution grid are observed on the generation 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 of devices connected to a grid, and increased presence of energy storages. Improvements in monitoring and data collection 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 in analysis and optimization of its operations. Advanced data analysis methods should be able to address a service quality degradation due to outages, weather patterns and asset related performance. The proposed project aims at applying Machine Learning/Data Mining and Computational Intelligence methods for analysis of power distribution system data, and developing a methodology for processing this data. The goal is to construct a system for predicting power outages and their severity. Weather data and description of system's topology will be utilized by the proposed system for predicting purposes. It is expected that the developed prediction system will lead to identifying probability of outage occurrences as well as their impact on customers and the power system. Additionally, it is anticipated that the work perform during this project will lead to discovery of new insights aiming at augmenting and predicting reliability of a power distribution grid.