Abstract: A system for allocating resources across equipment that operate to serve one or more loads of a building. The system includes one or more memory devices storing instructions that cause one or more processors to receive operational data defining at least one of planned loads to be served by the equipment or utility rates for one or more time steps within a simulation period, determine whether the operational data define the planned loads or the utility rates for each time step within the simulation period, and in response to a determination that the operational data do not define the planned loads or the utility rates for each time step within the simulation period, identify one or more time steps for which the planned loads or the utility rates are not defined and initiate an action to define the planned loads or the utility rates for the identified time steps.

Abstract: A system and method for optimizing utilization of a plurality of energy sources of a power site are provided. The optimization can involve receiving a weather forecast and expected power output for a predefined time duration, and a power source for one or more time intervals to provide output power for the power site. The determination can be based on a future weather forecast and expected power output. The optimization can involve minimizing an amount of time that generator(s) are the power source and maximizing an amount of time that solar panel(s) are the power source.

Abstract: Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energy usage from source location energy usage can be stored, where the first trained machine learning model is trained to predict an amount of energy usage for the target device and the second trained machine learning model is trained to predict when a target device has used energy. Source location energy usage over a period of time can be received, where the source location energy usage includes energy consumed by the target device. An amount of disaggregated target device energy usage over the period of time can be predicted, using the first and second trained machine learning models, based on the received source location energy usage.

Abstract: An intelligent impedance injection module is for use with transmission lines in a power grid. The intelligent impedance injection module has a plurality of transformer-less impedance injector units and a controller. The controller changes injector gain of the impedance injector units to compensate for current swings in a transmission line.

Abstract: A power supply control system includes a power generator for providing a signal to a load. The power generator includes a power controller controlling a power amplifier. The power generator includes an adaptive controller for varying the output signal controlling the power amplifier. The adaptive controller compares an error between a measured output and a predicted output to determine adaptive values applied to the power controller. The power generator also includes a sensor that generates an output signal that is digitized and processed. The sensor signal is mixed with a constant K. The constant K is varied to vary the processing of the sensor output signal. The value K may be commutated based on the phase, frequency, or both phase and frequency, and the bandwidth of K is determined by coupled power in the sensor output signal.

Abstract: This invention provides an energy storage device manager, a system comprising the energy storage device manager, computer-readable media configured for providing the energy storage device manager, and methods of using the energy storage device manager. The energy storage device manager can optionally control charge buses and/or load buses to modulate the state of charge of an energy storage device. The energy storage device manager can optionally be configured with a plurality of modes that target different states of charge. The plurality of modes can optionally comprise a maintain mode which targets a nominal (e.g. 50%) charge state and a high-charge mode that targets a state of charge greater than the maintain mode. The plurality of modes can optionally further include an in-use mode which targets a state of charge greater than the maintain mode, and turns on a load bus that is turned off in the preparation mode.

Abstract: Disclosed are various embodiments for optimizing energy management. A quantity of renewable power that will be generated by renewable energy generation sources can be forecasted. The energy demand for a building or a cluster of buildings can be forecasted. A pricing model for buying energy from a grid can be determined. A quantity of energy to import from the grid or export to the grid can be scheduled based on the quantity of renewable energy forecasted and the state of charge or health of battery energy storage system, current and future operations of building HVAC, lighting and plug loads system, the forecasted energy demand for the building, and the pricing of the energy from the grid.

Abstract: There is provided a load controller for a system, the system comprising a first sub-system arranged to deliver a first load, the load controller being operable to: acquire a first target load profile, being the load initially desired for delivery by the first sub-system over an operational period; measure in real time during an update window within the operational period: a first parameter of the first sub-system, to obtain a first measured Load Controller monitor signal; and the first load, to obtain a first measured load signal; develop in real time a model of the first sub-system, using the first measured monitor signal and the first measured load signal, the model relating the first load to the first parameter; given the first target load profile, and the model of the first sub-system, generate for a future period a first predicted monitor signal, the future period being ahead of the update window; and determine whether the first predicted monitor signal satisfies at least one predetermined criterion.

Abstract: Systems and methods to test an electric power delivery system include a communication subsystem to transmit test signals to one or more merging units, a test subsystem to transmit a test data stream to the one or more merging units via the communication subsystem, and a processor subsystem to receive looped back data from the one or more merging unit in response to the transmitted test data stream and to determine an operating condition based on the looped back data.

Abstract: A system for autonomously validating the topology information of an electrical power distribution system is provided. For example, the system includes a group of meters previously determined to be connected to the same transformer of an electrical power distribution system. The group of meters is configured to perform family check periodically or upon request and to identify orphan meters in the group. The identified orphan meter can contact a community device communicatively connected to meters in more than one group to request a community check. The community device performs the community check by contacting meters in other groups of meters and obtain their family signature data. The community device further determines whether the orphan meter belongs to a new family based on the voltage data of the orphan meter and the family signature data of other groups. The orphan meter can report the community check results to a headend system.

Abstract: A variable air volume controller includes a communications interface and a processing circuit. The communications interface is configured to facilitate communication with an external device and building equipment. The processing circuit is configured to store a plurality of predefined, selectable-applications; receive a selection of one of the plurality of predefined, selectable-applications; and implement the selected application such that the building equipment is controlled according to the selected application.

Abstract: A method may comprise receiving, at a central controller, a command, from a remote control, to adjust a feature of a first component of an air mattress framework; relaying, from the central controller, the command to the first component; receiving from the first component at the central controller, an indication of the success of the command; and relaying the indication from the central controller to the remote control.

Abstract: The present disclosure relates to a power management method and apparatus, a computing device, a medium, and a product. The power management method includes a monitoring step, a prediction step, an error calculation step and an adjustment step including adjusting power supply plan or a power demand of a user when at least one of a first error is greater than a first predetermined threshold or a second error is greater than a second predetermined threshold.

Abstract: A system for controlling a turbine is disclosed. The system includes a turbine control fuel governor that has a plurality of VCPIDs operating in parallel with one another. Each VCPID is associated with a respective turbine parameter and one or more external parameters. Each VCPID incorporates feedback from the parallel operating VCPIDs to feed an integral term of a current VCPID in the following manner: a previous derivative gain and a previous proportional gain are summed and subtracted from a selected output for the turbine to yield a result, and the result is input to an integral gain portion of the current VCPID.

Abstract: Embodiments of the disclosure provide a method for controlling a power generation system during on-line maintenance. The method includes operating the power generation system in a first maintenance mode, which causes a controller of the power generation system to disable an automated response to at least one operational fault of the power generation system; monitoring a risk parameter of the power generation system or at least one sensor within the power generation system while operating the power generation system in the first maintenance mode; and operating the power generation system in a second maintenance mode in response to detecting an override command, an elapsed time exceeding a time limit, or the monitored risk parameter exceeding a safety threshold. The second maintenance mode causes the controller of the power generation system to enable the automated response to the at least one operational fault of the power generation system.

Abstract: A method for validating a preparation of at least one tooth in a prepared set of teeth for determining whether the prepared tooth is capable of accepting a dental restoration includes obtaining a virtual dental preparation guide configured for validating the preparation of the at least one tooth; obtaining a digital 3D representation of the prepared set of teeth; visualizing the virtual dental preparation guide together with the digital 3D representation of the prepared set of teeth; and validating from the visualization of the virtual dental preparation guide together with the digital 3D representation of the prepared set of teeth whether the prepared at least one tooth is shaped such that it can accept the dental restoration.

Abstract: Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.

Abstract: A hybrid electromagnetic transient simulation method for microgrid real-time simulation, wherein a traditional node analysis method (NAM) and a highly parallel latency insertion method (LIM) are combined, so that the microgrid is firstly divided from a filter of a distributed power generation system to form one latency insertion method (LIM) network containing a power distribution line and a plurality of node analysis method (NAM) networks containing the distributed power generation system respectively, the NAM network being simulated by traditional node analysis method, the LIM network being simulated by the latency insertion method, in an initialization stage, one correlation matrix and four diagonal matrixes containing line parameters used for LIM network simulation being formed according to line topology and parameters of the microgrid, in a main cycle of the simulation, the LIM network solved simultaneously with multiple NAM networks, a parallelism of a microgrid simulation being improved.

Abstract: Systems, methods, and frameworks for distribution grid optimal power flow (D-OPF) are provided, to find the optimal droop and mode settings of smart inverters (SIs). The droop and mode settings of SIs can be found as per the Institute of Electrical and Electronics Engineers (IEEE)-1547 in coordination with the operations of legacy voltage control devices for optimal Volt-VAR control performance. The D-OPF framework can utilize two timescale coordination of control of legacy grid voltage control devices, modes and droop settings of SIs, and active/reactive power dispatch of SIs.

Abstract: Methods and systems for monitoring multiple sensors over a distributed sensor network to enhance various monitoring operations. In some implementations, an identification of a sensor located in a property and a token that enables the mobile device to obtain data from the sensor are each received by a mobile device from a server. The mobile device is then determined to be within a communication range with the server that is indicated by the identification from the server. In response, data is obtained from the sensor by the mobile device using the token received from the server.