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Leveraging Predictive Analytics to Control and Coordinate Operations, Asset Loading, and Maintenance

机译：利用预测分析来控制和协调操作，资产加载和维护

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This paper aims to advance decision making in power systems by proposing an integrated framework that combines sensor data analytics and optimization. Our modeling framework consists of two components: first, a predictive analytics methodology that uses real-time sensor data to predict future degradation and remaining lifetime of generators as a function of the loading conditions, and second, a mixed integer optimization model that transforms these predictions into cost-optimal maintenance and operational decisions. We model the key balance between meeting demand with very high confidence and at the same time prolonging the lifetime of generation assets. To do so, we encapsulate stochastic loading-dependent predictive models for asset condition within our optimization model. The methodology is validated and evaluated using IEEE 118-bus system that has been augmented using real-world sensor-based vibration signals from rotating machinery to emulate physical degradation of generators. Our experiments suggest that the proposed framework provides considerable improvements over conventional methods in terms of cost and reliability.
机译：本文旨在通过提出一个结合了传感器数据分析和优化的集成框架来推进电力系统的决策。我们的建模框架包括两个部分：第一，一种预测分析方法，该方法使用实时传感器数据根据负载条件预测发电机的未来退化和发电机剩余寿命，第二，混合整数优化模型可转换这些预测成本最优的维护和运营决策。我们在满足非常高的信心满足需求和同时延长发电资产的使用寿命之间建立关键的平衡模型。为此，我们将随机负荷相关的资产状况预测模型封装在优化模型中。该方法论使用IEEE 118总线系统进行了验证和评估，该系统已使用来自旋转机械的基于现实世界的传感器的振动信号进行了增强，以模拟发电机的物理退化。我们的实验表明，所提出的框架在成本和可靠性方面比传统方法有了很大的改进。

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