Gridpoint, Inc.

Admmicro Properties, L.l.c.

Silverback Advanced Motor Monitoring, LLC

Mark Wayland Vinson

Armand J Tamagni, Jr

Donald W Howell

Danny K Dyess

Frank Oakley Blevins

Michael L Campbell

Raymond M Hayes

Arnie J Tamagni

Frank O Blevins

Samuel A Taylor

A light performance monitoring device and optionally integrated controller includes a monitor module that directly monitors energy usage of at least one energy load to generate at least one measurement of energy usage; a storage module stores a series of baseline values of energy usage of the energy load, a comparator module compares energy measurements made at predetermined intervals with the baseline values, and a notification module notifies a designated recipient that there is a deviation from the baseline values consistent with a burned out or non-operational light fixture, including but not limited to light bulbs or ballast devices. A control module optionally integrated with the light performance monitoring device can be operatively coupled to the monitor module to control energy usage by the at least one energy load via a data link in a pre-determined manner that is based on the at least one measurement of energy usage.

Lighting performance power monitoring system and method with optional integrated light control

A system and method are disclosed for dynamically learning the optimum energy consumption operating condition for a building and monitor/control energy consuming equipment to keep the peak demand interval at a minimum. The dynamic demand limiting algorithm utilized employs two separate control schemes, one for HVAC loads and one for non-HVAC loads. Separate operating parameters can be applied to the two types of loads and multiple non-HVAC (control zones) loads can be configured. The algorithm uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within the user configured parameters. When a new peak is inevitable, the algorithm strategically removes and/or introduces loads in a fashion that limits the new peak magnitude and places the operating conditions within the user configured parameters. In an embodiment, the algorithm that examines the previous seven days of metering information to identify a peak demand interval. The system then uses real-time load information to predict the demand peak of the upcoming interval, and strategically curtails assigned loads in order to limit the demand peak so as not to set a new peak.

Dynamic load curtailment system and method

A system obtains respective measurements of relevant electrical parameters of a motor during operational stages including a start-up stage, a transition stage, a steady-state stage, idle stage, or a shutdown stage of the motor driven system driven by the motor. Based on the respective measurements, the monitoring system then determines respective electrical patterns corresponding to the operational stages. Next, the monitoring system compares the respective electrical patterns corresponding to the operational stages with respective baseline electrical patterns modeled for the operational stages to yield a comparison. Then, the monitoring system determines a status of the motor driven system based on a comparison between baseline and observed operating parameters. Trend pattern monitoring is used to eliminate storing massive volumes of trend data by capturing and characterizing the important moments when data values change in a significant manner.

System and method for monitoring an electrical pattern and pattern trends in electrically driven systems

Systems, methods, and computer-readable storage media for monitoring electrical patterns of a motor-driven system. The system first obtains respective measurements of relevant electrical parameters of a motor during operational stages including a start-up stage, a transition stage, a steady-state stage, idle stage, or a shutdown stage of the motor driven system driven by the motor. Based on the respective measurements, the monitoring system then determines respective electrical patterns corresponding to the operational stages. Next, the monitoring system compares the respective electrical patterns corresponding to the operational stages with respective baseline electrical patterns modeled for the operational stages to yield a comparison. Then, the monitoring system determines a status of the motor driven system based on a comparison between baseline and observed operating parameters.

Electrical pattern monitor

A light performance monitoring device and optionally integrated controller includes a monitor module that directly monitors energy usage of at least one energy load to generate at least one measurement of energy usage; a storage module stores a series of baseline values of energy usage of the energy load, a comparator module compares energy measurements made at predetermined intervals with the baseline values, and a notification module notifies a designated recipient that there is a deviation from the baseline values consistent with a burned out or non-operational light fixture, including but not limited to light bulbs or ballast devices. A control module optionally integrated with the light performance monitoring device can be operatively coupled to the monitor module to control energy usage by the at least one energy load via a data link in a predetermined manner that is based on the at least one measurement of energy usage.

A system that dynamically learns the optimum energy consumption operating condition for a building and monitors/controls energy consuming equipment to keep the peak demand interval at a minimum. The algorithm employs two separate control schemes, one for HVAC loads and one for non-HVAC loads, and uses historical peak demand measurements in its real-time limiting strategy. The algorithm continuously attempts to reduce peak demand within user configured parameters. When a new peak is inevitable, the algorithm removes and/or introduces loads to limit the new peak magnitude and places the operating conditions within the user configured parameters. The algorithm can examine the previous seven days of metering information to identify a peak demand interval, use real-time load information to predict the demand peak of the upcoming interval, and curtail loads in order to limit the demand peak so as not to set a new peak.

A system that establishes a local dynamic data link between an energy management system (EMS) and a security system (SS) within a building. A power management device, including a monitor module that directly monitors energy usage of at least one energy load to generate at least one measurement of energy usage by said at least one energy load; and a security module operatively coupled to the monitor module.

Energy management system with security system interface

A current transformer assembly includes at least one Rogowski coil having a first closeable loop with an electrically conductive coil member and a first pair of terminals. An integrator unit has respectively a cable connected across the first pair of terminals of a respective Rogowski coil. Each respective Rogowski coil provides an output voltage received by the integrator assembly caused by when a respective electrical conductor on a phase of a multiphase circuit is arranged within an opening of the closeable first loop of the respective Rogowski coil, and provides an output signal proportional to a current in a conductor arranged in an opening of a Rogowski coil. At least one of the Rogowski coils includes an inline calibration unit for fast calibration and recalibration when retrofitting a monitor module that monitors a value of the current in one or more conductors.

Flexible current transformer assembly

A power management device, including: a monitor module that directly monitors energy usage of at least one energy load to generate at least one measurement of energy usage by the at least one energy load; and a control module operatively coupled to the monitor module to control energy usage by the at least one energy load in a pre-determined manner that is based on the at least one measurement of energy usage, wherein the control module controls the at least one energy load via a data link.

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Roanoke, VA, United States of America

Mark Wayland Vinson