Data-driven system for predicting outages and their severity

发电过程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，以及在负载侧，由于能源消耗曲线的变化，连接到电网的设备的性质，以及能源存储的增加，观察到配电网中的可变性和不确定性。监测和数据收集实践的改进为更全面的建模和管理网格操作提供了机会。通过将人工智能技术集成到电网运行的分析和优化中，可以增强电网并提高其效率。先进的数据分析方法应该能够解决由于停电、天气模式和资产相关性能而导致的服务质量下降。拟议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，并开发处理这些数据的方法。我们的目标是建立一个系统，预测停电及其严重程度。天气数据和系统的拓扑结构的描述将被用于预测的目的，预计开发的预测系统将导致识别停电发生的概率，以及他们对客户和电力系统的影响。此外，预计在该项目期间执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.