A supercapacitor having multiple graphene layers that are separated by separator layers. The graphene layers and the separator layers are enclosed within a housing that is filled with electrolyte

The present invention discloses a method and device for formulating a coordinated action strategy of SSTS and DVR for voltage sag mitigation. The influence of voltage sag on a whole industrial process of a sensitive user is analyzed, and the sensitive loads of SSTS and DVR which satisfy a governance need are grouped; a practical governance scenario of installing a plurality of DVR is considered to install a minimum-capacity DVR to realize a target of a minimum interruption probability of the whole industrial process of the user; an optimal coordinated governance solution of SSTS and DVR based on sensitive load grouping is proposed; a classification result is obtained for duration time at a time when a voltage sag event occurs through a decision tree constructed based on historical data.

Disclosed techniques include using machine learning to detect an electrical anomaly in a power distribution system. In an example, a method includes accessing voltage measurements measured at an electric metering device and over a time period. The method further includes calculating, from voltage measurements and for each time window of a set of time windows, a corresponding average voltage and a corresponding minimum voltage. The method further includes applying a machine learning model to the average voltages and the minimum voltages. The machine learning model is trained to identify one or more predetermined electrical anomalies from voltages. The method further includes receiving, from the machine learning model, a classification indicating an identified anomaly. The method further includes based on the classification, sending an alert to a utility operator.

A system for transferring energy based on predicted outages is described herein.

The present application discloses a flexible excitation system and a method for controlling the same. The flexible excitation system consists of a plurality of groups of flexible excitation power units connected in parallel, a deexcitation circuit unit and a flexible excitation control unit. The method for controlling the flexible excitation system includes: realizing the internal fault-tolerant operation control of the flexible excitation power units by cooperatively controlling AC circuit breakers and DC circuit breakers of the flexible excitation power units and a deexcitation switch; dynamically controlling ceiling voltage when terminal voltage drops by using the fast response control ability of the flexible excitation system, so as to improve the forced excitation output capacity of the self-shunt excitation system. Aiming at the possible overload problem of an excitation transformer during the operation of flexible excitation, an excitation transformer overload limiter for the flexible excitation system is provided to limit the operation state of the excitation transformer within an allowable overload operation range of equipment.

A computer-implemented method for power grid anomaly detection using a convolutional neural network (CNN) trained to detect anomalies in electricity demand data and electricity supply data includes receiving (i) electricity demand data comprising time series measurements of consumption of electricity by a plurality of consumers, and (ii) electricity supply data comprising time series measurements of availability of electricity by one or more producers. An input matrix is generated that comprises the electricity demand data and the electricity supply data. The CNN is applied to the input matrix to yield a probability of anomaly in the electricity demand data and the electricity supply data. If the probability of anomaly is above a threshold value, an alert message is generated for one or more system operators.

A method for evaluating resonance stability of a flexible direct current (DC) transmission system in an offshore wind farm includes: establishing an s-domain equivalent circuit of a flexible DC transmission system in an offshore wind farm, constructing an s-domain node admittance matrix of the flexible DC transmission system in the offshore wind farm, determining a resonant mode of the system based on a zero root of a determinant of the node admittance matrix, and determining stability of the system. In the method, an s-domain impedance model is used to describe dynamic characteristics of a wind turbine, a flexible DC converter, and other power devices, avoiding coupling between device modeling and an operation mode of the system. In addition, the node admittance matrix is used for analysis so as to fully consider a plurality of power electronic devices and a grid structure of the offshore wind farm, realizing comprehensive analysis.

Systems and methods comprising a metering device located on an electricity distribution grid, the metering device comprising one or more processors and memory. The metering device can detect a drop in characteristic of electricity below a threshold indicating a fault on the electricity distribution grid. The metering device can generate, responsive to the drop in the characteristic of electricity below the threshold, a time series of a rate of change of the characteristic of electricity for a predetermined number of cycles subsequent to the detection of the drop. The metering device can determine, based on a comparison of the time series of the rate of change with a predetermined pattern, a location of the metering device on the electricity distribution grid relative to a location of the fault on the electricity distribution grid.

Waveforms in power grids typically reveal a certain pattern with specific features and peculiarities driven by the system operating conditions, internal and external uncertainties, etc. This prompts an observation of different types of waveforms at the measurement points (substations). An innovative next-generation smart sensor technology includes a measurement unit embedded with sophisticated analytics for power grid online surveillance and situational awareness. The smart sensor brings additional levels of smartness into the existing phasor measurement units (PMUs) and intelligent electronic devices (IEDs). It unlocks the full potential of advanced signal processing and machine learning for online power grid monitoring in a distributed paradigm. Within the smart sensor are several interconnected units for signal acquisition, feature extraction, machine learning-based event detection, and a suite of multiple measurement algorithms where the best-fit algorithm is selected in real-time based on the detected operating condition. Embedding such analytics within the sensors and closer to where the data is generated, the distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. The smart sensor achieves a promising classification accuracy on multiple classes of prevailing conditions in the power grid and accordingly improves the measurement quality across the power grid.

A method and an apparatus are presented which enable the identification of a capacitor fault in a given string of a capacitor bank, based on the computation of the string impedance by measuring the string AC current and voltages, where each string includes a plurality of capacitor elements connected in series. The method consists of measuring the string capacitive impedance and comparing this value with a previously measured capacitive impedance of the same string. If a difference between these two values is obtained, which exceeds a given threshold for a certain duration, a fault is recorded.