Abstract: A system for an intelligent web-based monitoring and management of an electrical system is disclosed.

Abstract: A system and method for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time electrical system security index engine that calculates real-time system security index values from stability indices data generated from a virtual system model of the electrical system. The client terminal displays the system security index values to assess the security and stability of the electrical system.

Abstract: A system for intelligent monitoring and management of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a real-time energy pricing engine, virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The real-time energy pricing engine generates real-time utility power pricing data. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: A system for automatically generating a schematic user interface of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, a machine learning engine and a schematic user interface creator engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system.

Abstract: A system for real-time modeling of uninterruptible power supply (UPS) control elements protecting an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine and a UPS transient stability simulation engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The UPS transient stability simulation engine stores and processes patterns observed from the real-time data output and utilizes a user-defined UPS control logic model to forecast an aspect of the interaction between UPS control elements and the electrical system subjected to a simulated contingency event.

Abstract: A system for making real-time predictions about an arc flash event on an electrical system is disclosed. The system includes a data acquisition component, an analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and an arc flash simulation engine. The arc flash simulation engine is configured to utilize the virtual system model to forecast an aspect of the arc flash event.

Abstract: A system for filtering and interpreting real-time sensory data from an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics sewer is comprised of a virtual system modeling engine, an analytics engine, and a decision engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-lime data output and predicted data output of the electrical system. The decision engine compares the real-time data output against the predicted data output to filter out and interpret indicia of electrical system health and performance.

Abstract: A system for real-time modeling of electrical system performance is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The power analytics server is comprised of a virtual system modeling engine, an analytics engine and a power system simulation engine. The virtual system modeling engine is configured to generate predicted data output utilizing a first virtual system model. The analytics engine is configured to synchronize the first virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The power system simulation engine is configured to store and process patterns and facilitate modification of parameters on the first virtual system model to create a second virtual system model; and forecast an aspect of the electrical system operating under parameters of the second virtual system model. The client terminal displays the forecasted aspects.

Abstract: A system for real-time optimization of power resources on an electrical system is disclosed. The system includes a data acquisition component, an analytics server, a control element and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a power flow optimization engine. The virtual system modeling engine is configured to generate predicted data output for the electrical system utilizing a virtual system model of the electrical system. The control element is interfaced with an electrical system component and communicatively connected to the analytics server. The client terminal is communicatively connected to the analytics server.

Abstract: A system for real-time three-dimensional (3D) visualization of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component acquires real-time data output from the electrical system. The power analytics server is comprised of a virtual system modeling engine, an analytics engine, a machine learning engine and a 3D visualization engine. The virtual system modeling engine generates predicted data output for the electrical system. The analytics engine monitors real-time data output and predicted data output of the electrical system. The machine learning engine stores and processes patterns observed from the real-time data output and the predicted data output to forecast an aspect of the electrical system. The 3D visualization engine renders the virtual system model and the forecasted aspect into a 3D visual model.

Abstract: A system for making real-time predictions about an arc flash event on an electrical system is disclosed. The system includes a data acquisition component, an analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and an arc flash simulation engine.

Abstract: A system for performing real-time failure mode analysis of a monitored system is disclosed. The system includes a data acquisition component, an analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the monitored system. The analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine.

Abstract: A system for conducting performing real-time harmonics analysis of an electrical power distribution and transmission system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting harmonic distortions in the electrical system subjected to a simulated contingency event.

Abstract: A system for conducting a real-time power capacity assessment of an electrical system is disclosed. The system includes a data acquisition component, a power analytics server and a client terminal. The data acquisition component is communicatively connected to a sensor configured to acquire real-time data output from the electrical system. The power analytics server is communicatively connected to the data acquisition component and is comprised of a virtual system modeling engine, an analytics engine and a machine learning engine. The machine learning engine is configured to store and process patterns observed from the real-time data output and the predicted data output, forecasting power capacity of the electrical system subjected to a simulated contingency event.

Abstract: A system for providing real-time modeling of protective device in an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from protective devices within the system under management. The virtual system modeling engine is configured to update a virtual mode of the system based on the status of the protective devices and to generate predicted data for the system using the updated virtual model. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output. The analytics engine is also configured to determine the bracing capabilities for the protective devices.

Abstract: A system for providing real-time modeling of an electrical system under management is disclosed. The system includes a data acquisition component, a virtual system modeling engine, and an analytics engine. The data acquisition component is communicatively connected to a sensor configured to provide real-time measurements of data output from an element of the system. The virtual system modeling engine is configured to generate a predicted data output for the element. The analytics engine is communicatively connected to the data acquisition system and the virtual system modeling engine and is configured to monitor and analyze a difference between the real-time data output and the predicted data output.