37 results about "Power Analytics" patented technology

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.

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.

A system for an intelligent web-based monitoring and management of an electrical system is disclosed. The system comprises a data acquisition component communicatively connected to a sensor configured to acquire real-time data output from the electrical system; a web application server communicatively connected to the data acquisition component, the web application server configured to transmit a user interface to a client terminal, the web application server comprising: a virtual system model database communicatively connected to the data acquisition component, the virtual system model database configured to store a virtual system model of the electrical system, a power analytic simulation engine comprising a virtual system modeling engine communicatively connected to the virtual system model database, the virtual system modeling engine configured to generate a predicted data output for the electrical system utilizing the virtual system model of the electrical system, and an analytics engine communicatively connected to the virtual system model database, the analytics engine configured to monitor the real-time data output and the predicted data output of the electrical system, and to 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, and a client terminal communicatively connected to the web application server, the client terminal configured to display the user interface.

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.

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.

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.

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.

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

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 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.

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

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.

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.

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.

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.

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.

A working condition automatic interpreting system of satellite power source subsystem comprises a power source data collecting module, a power source uniformization processing module, a power source data processing module, a power source working condition scheduling module, a power source working condition interpreting module, a power source working condition abnormality alarming module, and an imaging displaying module. The invention automatically calls collected and processed corresponding data according to the telemetering data engineering value of the power source subsystem and the data engineering value of the ground test equipment, and the working conditions of power source analysis is judged in real time, reported and displayed according to the criterion, thus reducing work intensity of test staff, solving the problems faults are easy to occur and diverse from person to person in the manual monitoring and judging processes, ensuring uniformity of power source subsystem working condition interpretation and enhancing operational reliability of the satellite power source subsystem.

An electronic circuit for cryptographic processing, comprising a first combinatorial logical circuit, arranged to perform a first set of logical operations on input data and to produce output data, the output data having a functional relation to the input data, further comprising at least a second combinatorial logical circuit, arranged to perform a second set of logical operations on the same input data and to produce output data, the output data having an identical functional relation to the input data, wherein the first set of logical operations is different from the second set of logical operations, and wherein the electronic circuit is arranged to dynamically select one combinatorial logical circuit, of a set comprising at least the first combinatorial logical circuit and the second combinatorial logical circuit, for performing logical operations on the input data and producing output data.

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.

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.

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.

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.

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.

A storage system comprises one or more storage devices, power supplies supplying power to the storage device, a processor that performs in response to determining that the total power consumption of the one or more storage devices is less than a first percentage threshold of a load of the active power supplies, deactivating one or more of the active power supplies until the total power consumptionis equal to or greater than the first percentage threshold of a load of each of the active power supplies, and in response to determining that the total power consumption is equal to or greater thana second percentage threshold of a load of each of the active power supplies, activating one or more of the deactivated ones of the power supplies until the total power consumption is less than the second percentage threshold of the load of each of the active power supplies.

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.

Methods and systems for controlling power supplied to a plurality of appliances on a power line. Poly phase power measurements are collected from the power line by a dedicated energy metering chip are sampled at frequencies in the range of 0 kilo samples per second to 32 kilo samples per second and converted to digital power measurements. The digital power measurements are received by a real-time microcontroller. The poly phase power measurements are analyzed in real time, the poly phase power measurements are disaggregated, and a power report regarding a power usage of each appliance is generated by a computer processing unit, CPU. A power report is transmitted to a mobile application, power on / off commands for each appliance are received from the mobile application, and each appliance is powered on / off based on the power on / off commands.

The present invention relates to the technical field of dynamic control of air conditioners, and discloses a dynamic control method for an efficient and energy-saving air-conditioning refrigerating machine, including: obtaining the current refrigeration value of a target device, judging whether the target device is working properly, forming power analysis data, forming power consumption analysis data, forming power consumption adjustment data, and performing off-peak power consumption adjustment on users. This dynamic control method for an efficient and energy-saving air-conditioning refrigerating machine judges whether the device is working properly, judges whether there are abnormalities in the power supply and circuit of the air-conditioning refrigerating machine, and judges whether it is due to users using too many high-power devices simultaneously that causes the air-conditioning refrigerating machine to malfunction. According to the user's historical usage habits and shutdown selection habits, determine the devices that can be shut down, timely determine the reason for the abnormal operation of the air-conditioning refrigerating machine, and shut down the devices that are not needed, avoiding waste of electric energy and damage to the devices, reducing the number of equipment inspections, shortening the equipment inspection time, and reducing the inconvenience caused to users in their lives.

Systems and apparatuses are provided for on-die memory power analytics and management are described. In some examples, the memory analytics and management may include a frequency-dependent analysis orsimulation model of a memory die to determine an operating characteristic of the die. A set of ports of the memory die may be selected and one or more alternating current (AC) excitation signals maybe applied to the ports to determine an impedance associated with the ports. The impedance may be used to determine one or more parameters (e.g., scattering, impedance) to analyze a die and for subsequently managing power distribution on the die. Analytics on a subset of ports on a die may be used to simulate the electrical response of the entire memory die and thus manage power delivery for the die.