Systems and methods for real-time DC microgrid power analytics for mission-critical power systems
摘要
說明書
As discussed above, the HTM Machine Learning Engine 551 is configured to work in conjunction with a real-time updated virtual system model of the monitored system to make predictions (forecasts) about certain operational aspects of the monitored system when it is subjected to a contingency event. For example, where the monitored system is an electrical power system, in one embodiment, the HTM Machine Learning Engine 551 can be used to make predictions about the operational reliability of an electrical power system in response to contingency events such as a loss of power to the system, loss of distribution lines, damage to system infrastructure, changes in weather conditions, etc. Examples of indicators of operational reliability include but are not limited to failure rates, repair rates, and required availability of the power system and of the various components that make up the system.
In another embodiment, the operational aspects relate to an arc flash discharge contingency event that occurs during the operation of the power system. Examples of arc flash related operational aspects include but are not limited to quantity of energy released by the arc flash event, required personal protective equipment (PPE) for personnel operating within the confines of the system during the arc flash event, and measurements of the arc flash safety boundary area around components comprising the power system. In still another embodiment, the operational aspect relates to the operational stability of the system during a contingency event. That is, the system's ability to sustain power demand, maintain sufficient active and reactive power reserve, operate safely with minimum operating cost while maintaining an adequate level of reliability, and provide an acceptably high level of power quality while being subjected to a contingency event.
