Described herein are methods and systems for detection and notification of electrical power outages and power quality. A sensor coupled to a circuit transmits a keepalive packet to a server. The sensor detects an input signal generated by electrical activity. The sensor generates an output signal based upon the input signal. The sensor monitors the output signal. During a clock cycle, the sensor determines whether a rising edge occurred and transmits a fault packet to the server when the rising edge occurred prior to a predetermined clock value or when no rising edge occurred. The server receives the fault packet from the sensor and listens for keepalive packets. The server transmits a power outage notification when no keepalive packets are received for at least a defined time period after the fault packet is received. The server transmits a power restoration notification when one or more keepalive packets are subsequently received.

Systems, apparatuses, methods, and computer program products are disclosed for power theft detection. An example method includes receiving, by a control system, telemetry data from a transformer adjacent to a customer premise and a meter at the customer premise and storing, by the control system, the telemetry data in a memory. The example method further includes calculating, by the control system and using the telemetry data, a change in impedance in an electric line segment between the transformer and the meter, and determining, by the control system, whether the change in the impedance in the electric line segment is anomalous. Corresponding apparatuses and computer program products are also disclosed.

A current sense system may include a relay, a load conductor, and an integrator sub-circuit. Current may be provided to an electrical load via the load conductor and a latch of the relay. The current carried via the load conductor may induce a sense voltage in a coil of the relay. Based on the sense voltage induced in the relay coil, the integrator sub-circuit may determine a load sense voltage that indicates a level of the current carried via the load conductor. In some implementations, a current indication module may provide an indicator signal based on the load sense voltage. In addition, the indicator signal may be provided to additional components or devices, such as a relay controller configured to activate the latch. In some implementations, the relay controller may be configured to open the latch based on the current level described by the indicator signal.

Systems and methods for detecting faults in a power distribution network are described. In an aspect, the systems and methods determine a probability that each node of the network is powered and a probability that each distribution line in the network is faulted. In another aspect, the systems and methods determine the probabilities by transmitting a signal over a power distribution network with an active sounding system. In an additional aspect, the systems and methods determine the probabilities by utilizing collected data coupled to the power distribution network.

The present invention discloses A method of single-phase-to-ground fault line selection for a distribution line based on the comparison of phase current traveling waves, comprising: sampling three phases current traveling waves on the distribution line, and taking the busbar pointing to the line as the current positive direction; when a single-phase-to-ground fault occurs on the distribution lines, comparing the amplitude and polarity of the difference between the three phases current traveling waves before and after the fault, wherein when the amplitude of one of the three phases current traveling wave is higher than 1.5 times of the amplitude of the other two phases current traveling waves, and the polarity of the one of three phases current traveling wave of the largest amplitude is opposite to the polarity of the other two phases current traveling waves, it is determined that the fault occurs on the load side of the measuring point of the line, and the phase with the largest amplitude of the current traveling wave is the fault phase; if the difference of the amplitudes of the three phases current traveling waves is within a predetermined value and the polarity is the same, it is determined that the fault occurs on the power source side of the measuring point of the line. By the technical solution of The present invention, the precise line selection of the single-phase ground fault of the distribution line can be realized.

A current signal segmentation algorithm is provided. The segmentation algorithm divides a current signal waveform into mutually different segments according to a physical feature thereof, extracts shape distribution, statistical and harmonic features of the segments, and calculates a similarity between a segment pair. The segmentation algorithm includes the following steps: segmenting a current signal to separate a standby current and an overshoot current, only leaving a working current; extracting a shape distribution feature of a working current segment; extracting a statistical feature of the working current segment; extracting a harmonic feature of the working current segment; calculating a similarity between a segment pair; and deriving a maximum clique set in a similarity graph through a maximum clique search algorithm as a class from automatic segmentation. The algorithm can quickly and accurately classify current signals generated by different electrical appliances in different working states so as to facilitate subsequent processing.

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.

A system includes an orthogonal coordinate signal generator that generates an orthogonal two-phase voltage signal from a three-phase voltage signal of three-phase alternating current power of a power system; and a frequency calculator including an angular frequency calculator calculating an angular frequency of the power system based on the two-phase voltage signal, and an arithmetic unit calculating a system frequency of the power system from the angular frequency. A prediction calculator calculates a predicted value of the angular frequency after a time has elapsed based on the angular frequency and a differential of the angular frequency. In a state in which the phase jump of the power system is not detected, the frequency calculator calculates the system frequency based on the angular frequency. When the phase jump of the power system is detected, the frequency calculator calculates the system frequency based on predicted value for a constant amount of time.

Voltage samples are collected for a source and loads connected thereto by an electrical network. Respective negative sequence voltage difference values are generated for respective source/load pairs from the voltage sample. A connection (e.g., a loose connection) in the electrical network is identified based on the generated negative sequence voltage difference values. The identified connection is reported to a user. Identifying a connection in the electrical network may include identifying at least one source/load pair having an associated negative sequence voltage difference value that meets a predetermined criterion and identifying the connection based on the identified at least one source/load pair. Identifying the connection may include identifying at least one source/load pair having an associated negative sequence voltage difference value with a magnitude falling outside of at least one range associated with the at least one source/load pair.

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.