G01R31/08

Remote attestation of system integrity

An apparatus and system for remote attestation of a power delivery network is disclosed. Embodiments of the disclosure enable remote attestation of the power delivery network by storing a trusted golden reference waveform in secure memory. The trusted golden reference waveform characterizes a power delivery network in response to a load generated on the power delivery network. A remote cloud server generates a server-generated remote attestation of the power delivery network by receiving an attestation packet from the power delivery network and verifying whether the attestation packet is consistent with an expected power delivery network identity.

Remote attestation of system integrity

An apparatus and system for remote attestation of a power delivery network is disclosed. Embodiments of the disclosure enable remote attestation of the power delivery network by storing a trusted golden reference waveform in secure memory. The trusted golden reference waveform characterizes a power delivery network in response to a load generated on the power delivery network. A remote cloud server generates a server-generated remote attestation of the power delivery network by receiving an attestation packet from the power delivery network and verifying whether the attestation packet is consistent with an expected power delivery network identity.

Monitoring waveforms from waveform generator at device under test
11693046 · 2023-07-04 · ·

A test and measurement instrument including a signal generator configured to generate a waveform to be sent over a cable to a device under test (DUT) and a real-time waveform monitor (RTWM) circuit. The RTWM is configured to determine a propagation delay of the cable, capture a first waveform, including an incident waveform and a reflection waveform at a first test point between the signal generator and the DUT, capture a second waveform including at least the incident waveform at a second test point between the signal generator and the DUT, determine a reflection waveform and the incident waveform based on the first waveform and the second waveform, and determine a DUT waveform based on the incident waveform, the reflection waveform, and the propagation delay. The DUT waveform represents the waveform generated by the signal generator as received by the DUT.

Corona detection using audio data

Systems, methods, and apparatus for corona detection using audio data are provided. In one example embodiment, the method includes obtaining, by one or more computing devices, audio data indicative of audio associated with an electrical system for at least one time interval. The method includes partitioning, by the one or more computing devices, the audio data for the time interval into a plurality of time windows. The method includes determining, by the one or more computing devices, a signal indicative of a presence of corona based at least in part on audio data collected within an identified time window of the plurality of time windows relative to audio data collected for a remainder of the time interval.

Systems and methods for optimizing waveform capture compression and characterization

A method to automatically optimize waveform captures from an electrical system includes capturing at least one energy-related waveform using at least one Intelligent Electronic Device (IED) in the electrical system. The at least one captured energy-related waveform is analyzed to determine if the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis and/or other use. In response to determining the at least one captured energy-related waveform is capable of being compressed, while maintaining relevant attributes for characterization, analysis, and/or use, the at least one captured energy-related waveform may be compressed using at least one compression technique to generate at least one compressed energy-related waveform. One or more actions may be taken based on or using the at least one compressed energy-related waveform.

Link aggregation with receive side buffering

The present disclosure relates to a communication arrangement (110, 130) adapted for link aggregation of a plurality of communication links (120a, 12b, 120c). The communication arrangement (110, 130) is adapted to communicate via the plurality of communication links (120a, 120b, 120c) and comprises a traffic handling unit (112, 132) that is adapted to obtain data segments (414-417, 419-421, 423-425) to be transmitted, and to identify one or more data flows (401, 402, 403, 404) in said data segments. The traffic handling unit is adapted to attach sequence numbers, SEQ, to data segments associated with each identified data flow (401, 402, 403, 404), wherein sequence numbers are independent between data flows and to select a communication link for transmission of a data segment associated with a certain data flow (401, 402, 403, 404). The selecting comprises selecting a previous communication link that has been used for transmission of a previous data segment from said certain data flow (401, 402, 403, 404) if possible, and selecting any communication link otherwise.

Managing outage detections and reporting

Systems and methods are disclosed for detecting node outages in a mesh network. A tracking node in the mesh network detects a set of signals originating from a tracked node in the mesh network. The set of signals includes beacons and communication messages transmitted by the tracked node. The tracking node determines that a threshold number of the alive beacon intervals have passed since receiving a most recent signal from the tracked node. The tracking node then outputs a ping to the tracked node requesting a response to the ping. When the response to the ping is not received from the tracked node, the tracking node transmits an outage alarm message to a next topologically higher layer of the mesh network, the outage alarm message comprising an identification of the tracked node.

Managing outage detections and reporting

Systems and methods are disclosed for detecting node outages in a mesh network. A tracking node in the mesh network detects a set of signals originating from a tracked node in the mesh network. The set of signals includes beacons and communication messages transmitted by the tracked node. The tracking node determines that a threshold number of the alive beacon intervals have passed since receiving a most recent signal from the tracked node. The tracking node then outputs a ping to the tracked node requesting a response to the ping. When the response to the ping is not received from the tracked node, the tracking node transmits an outage alarm message to a next topologically higher layer of the mesh network, the outage alarm message comprising an identification of the tracked node.

LINE DOUBLE-END STEADY-STATE QUANTITY DISTANCE MEASURING METHOD AND SYSTEM BASED ON AMPLITUDE-COMPARISON PRINCIPLE

A line double-end steady-state quantity distance measuring method and system based on an amplitude-comparison principle. According to the method and system, voltage values and current values of both sides of a line before and after a fault are collected (102), a voltage variable quantity and a current variable quantity of both sides of the line are calculated (103), and after a voltage phasor value and a current phasor value are determined according to the voltage variable quantity and the current variable quantity (104), the position of a short-circuit point is determined by performing iterative calculation on the voltage of the short-circuit point. The method is simple in principle, and can accurately recognize a fault point, achieving precise distance measurement of lines.

MACHINE LEARNING BASED METHOD AND DEVICE FOR DISTURBANCE CLASSIFICATION IN A POWER TRANSMISSION LINE

The present specification provides a method and device for determining a disturbance condition in a power transmission line. The method includes obtaining (302) a plurality of sample values corresponding to an electrical parameter measured in each phase. The method further includes determining (304) a plurality of magnitudes of the electrical parameter corresponding to each phase based on the corresponding plurality of sample values and determining (306) a plurality of difference values for each phase based on the corresponding plurality of magnitudes. The method includes processing (308) the plurality of difference values using a machine learning technique to determine the disturbance condition. The disturbance condition is one of a load change condition, a power swing condition and an electrical fault condition. The method also includes performing (310) at least one of a protection function and a control function based on the disturbance condition.