Abstract: Systems and methods for performing power analytics on a microgrid. In an embodiment, predicted data is generated for the microgrid utilizing a virtual system model of the microgrid, which comprises a virtual representation of a topology of the microgrid. Real-time data is received via a portal from at least one external data source. If the difference between the real-time data and the predicted data exceeds a threshold, a calibration and synchronization operation is initiated to update the virtual system model in real-time. Power analytics may be performed on the virtual system model to generate analytical data, which can be returned via the portal.

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: Systems and methods for a cloud computing engineering application that can provide new interaction, visualization and interaction techniques along with advertising messaging and business social networking are disclosed.

Abstract: Systems and methods for making real-time predictions about the health, reliability, and performance of a monitored system are disclosed. A data acquisition component acquires real-time data output from the monitored system. A power analytics server comprises a virtual system modeling engine, an analytics engine, and an adaptive prediction engine. The virtual system modeling engine is operable to generate predicted data output for the monitored system utilizing a virtual system model of the monitored system. An analytics engine is operable to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold. The adaptive prediction engine is operable to forecast an aspect of the monitored system based on an adaptive neural network algorithm and automatically minimize a measure of error between the real-time data output and a corresponding forecasted data output by the adaptive prediction engine.

Abstract: Systems and methods for modeling an electrical power system are disclosed. A computer and an analytics server are in network connection. The computer comprises a processor coupled with a memory. The memory is configured to maintain at least one engine element and a component database. The analytics server comprises a virtual system modeling engine and an analytics engine. The component database is operable to store power system components. The at least one engine element is operable to generate a virtual system model of the electrical power system and generate predicted output based on the virtual system model. The analytics engine is operable to monitor the predicted output and real-time output from at least one sensor of the electrical power system, and calibrate the virtual system model based on a difference between the predicted output and the real-time output.

Abstract: A system for utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are 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, an adaptive prediction 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 adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm.

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 modeling a topology of an electrical power system may include a memory device to maintain a component database, a component control engine, and a power system topology modeling engine. A display device can be configured for displaying the topology of the electrical power system. An input device can be operative to select one of a plurality of power system components stored in the component database as a selected component, position the selected component within a framework, and interface the selected component with other selected components within the framework. A processor can be operative to execute instructions within the component control engine to control the position of the selected components within the framework and execute instructions within the power system topology modeling engine to render the topology of the electrical system after the selected components have been positioned.

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 acid 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 utilizing a neural network to make real-time predictions about the health, reliability, and performance of a monitored system are 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, an adaptive prediction 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 adaptive prediction engine can be configured to forecast an aspect of the monitored system using a neural network algorithm.

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 acid 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 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 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 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 server 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-time 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. The client terminal is communicatively connected to the power analytics server and configured to display the filtered and interpreted indicia.

Abstract: A system for intelligent web-based monitoring and management of an electrical system is provided. The system is configured to acquire real-time data output from the electrical system, and to transmit a user interface to a client terminal which is configured to display the user interface. In an embodiment, the system is configured to store a virtual system model of the electrical system. The system is configured to generate a predicted data output for the electrical system utilizing the virtual system model of the electrical system, monitor the real-time data output and the predicted data output of the electrical system, and initiate a calibration and synchronization operation to update the virtual system model when a difference between the real-time data output and the predicted data output exceeds a threshold.

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 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 real-time verification of protective device system configuration settings on a monitored system, is disclosed. The system includes a data acquisition component, a virtual system model database, a protective device system verification engine. The data acquisition component is communicatively connected to a sensor configured to real-time protective device configuration data output from a protective device that is part of the monitored system. The virtual system model database is configured to update a virtual mode of the system based on the status of the protective devices and to store a virtual system model of the monitored system, including preset protective device configuration settings for the protective device.

Abstract: A computer system for converting a computer aided design drawing file of an electrical power system into one or more component objects for power analytic analysis and simulation, is disclosed. The computer system can include a processor, a memory, a display device, and an input device. The memory device can be coupled to the processor and configured to maintain a component classification database, an import engine, computer aided design drawing parser, and a symbol classification engine. The display device can be coupled to the processor and configured for displaying the computer aided design drawing file of the electrical power system.