Palo Alto Research Center Inc.

Xerox Corporation

Novity, Inc.

Saman Mostafavi

Ajay Raghavan

Hong Yu

Peter Kiesel

Deokwoo Jung

Warren Bruce Jackson

David Eric Schwartz

Eric Allan Bier

Qiushu Chen

Jacob N Chamoun

Kathryn F Murphy

Raman Goyal

Muhammad Adil

Subhashis Hazarika

A physics model of a building is linearized around an operating point. Measurements received from sensors define a system state of the HVAC system. The linearized physics model is used as an equality constraint for a model predictive controller that determines a next control input to the HVAC system based on the system state by solving an optimization problem for a time horizon of size N. A constraint matrix H of the equality constraint is decomposed into factors of U and V matrices such that UUand VV are both diagonal matrices. An objective function of the model predictive controller is optimized by iteratively solving a linear system of equations that includes inverses of UUand VV to determining a sequence of inputs for a horizon of the model predictive controller. A first input of the sequence of inputs to is used to control the HVAC system.

Method and system for scalable embedded model predictive control of HVAC systems

A system and method for determining HVAC set points are provided. A present season is determined. An allowable temperature comfort range for each occupant in a room in a space is also determined. For each room, an energy saving set point temperature is calculated for the space based on the allowable temperature comfort range for that room. A set point temperature for the space is determined based on the energy saving set point temperatures for each of the rooms in the space. The set point temperature is provided to a thermostat or HVAC system to regulate temperature in the building.

System and method for determining HVAC set points

A system for evaluating a high voltage asset (HV asset) comprises a PD detector disposed in the HV asset. The PD detector comprises an electrical coupler configured to couple electrical disturbances indicative of a partial discharge from a high voltage conductor of the HV asset to an electrical-to-optical converter. The electrical-to-optical converter comprises a light emitter, and is configured to convert the electrical disturbances to a light signal. An optical power receiver is disposed in the high voltage asset and coupled to the PD detector. The optical power receiver is configured to receive optical power from an external optical power source via a non-conducting optical fiber arrangement. The electrical-to-optical converter is configured to communicate the light signal indicative of the partial discharge to an electronic device external of high voltage asset via the non-conducting optical fiber arrangement.

Partial discharge detection system and method

A monitoring system includes an array of optical sensors disposed within a transformer tank. Each optical sensor is configured to have an optical output that changes in response to a temperature within the transformer tank. An analyzer is coupled to the array of optical sensors. The analyzer is configured to determine a sensed temperature distribution based on the sensed temperature. The sensed temperature distribution is compared to an expected distribution. Exterior contamination of the transformer tank is detected based on the comparison.

Optical monitoring to detect contamination of power grid components

A system and method are provided for determining a causal inference in a manufacturing process. During operation, the system can receive data associated with a processing system which includes a set of interconnected machines and an associated set of processes. The system can generate, based on the data, a graph indicating flows of outputs between the machines as part of the processes. The system can determine, based on a set of variables, one or more candidate clusters in the graph. The system can perform, based on one or more variables of interest, root cause analysis on the one or more candidate clusters by: applying an additive noise model to prune the one or more candidate clusters from the graph; and determining, based on the pruned graph, a candidate pathway likely to cause an issue in at least one process, thereby facilitating improved efficiency in the processing system.

System and method for casual inference in manufacturing process

A system is provided for determining causes of faults in a manufacturing system. The system stores data associated with a processing system which includes machines and associated processes, wherein the data includes timestamp information, machine status information, and product-batch information. The system determines, based on the data, a topology of the processing system, wherein the topology indicates flows of outputs between the machines as part of the processes. The system determines information of machine faults in association with the topology. The system generates, based on the machine-fault information, one or more fault parameters which indicates frequency and severity of a respective fault. The system constructs, based on the topology and the machine-fault information, a system model which includes the one or more fault parameters, thereby facilitating diagnosis of the processing system.

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San Francisco, CA, United States of America

Saman Mostafavi