Power Analytics Corporation

Edsa Micro Corporation

Ali Nasle

Adib Nasle

Branislav Radibratovic

Kevin Meagher

Ali Moshref

Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. A virtual system model database is operable for providing a virtual system model for the electrical system and continuously update the virtual system model with real-time data from the electrical system. An analytics server comprises an arch flash simulation engine. The arch flash simulation engine is operable to modify the virtual system model to introduce a short-circuit feature to an uninterrupted power supply bypass circuit branch; choose a standard to supply equations used for arc flash event simulation and energy calculation; simulate an arc flash event utilizing the modified virtual system model; calculate a quantity of arc energy released by the arc flash event using results from the simulation; and communicate a report that forecasts an aspect of the arc flash event.

Method for predicting arc flash energy and PPE category within a real-time monitoring system

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. The adaptive prediction engine is further configured to process the real-time data output and automatically optimize the neural network algorithm by minimizing a measure of error between the real-time data output and an estimated data output predicted by the neural network algorithm.

Systems and methods for real-time forecasting and predicting of electrical peaks and managing the energy, health, reliability, and performance of electrical power systems based on an artificial adaptive neural network

A method for simulating an arc flash event on an electrical power system is disclosed. The virtual system model of the electrical system is modified to introduce a short circuiting feature. The standard to supply equations used in the arc flash event calculations is chosen. The arc flash event is simulated using the modified virtual system model in accordance with the chosen standard. The quantity of arc energy released by the arc flash event is calculated using results from the simulation. The report that forecasts an aspect of the arc flash event is communicated.

Method for predicting arc flash energy and PPE category within a real-time  monitoring system

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.

System for comparing real-time data and modeling engine data to predict arc flash events

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.

Systems and methods for real-time system monitoring and predictive analysis

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.

Systems and methods for real-time dynamic simulation of uninterruptible power supply solutions and their control logic systems

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.

Systems and methods for real-time protective device evaluation in an electrical power distribution system

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.

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San Diego, CA, United States of America

Ali Nasle