A power distribution system comprises a first Smart Meter device for metering the power absorbed or yielded by a domestic utility, in which said first Smart Meter device is connected to a power distribution grid and dialogs in real time with other Smart Meters connected to other domestic utilities, in which said other Smart Meters are connected to the same power grid of said first Smart Meter device, in which said power distribution system is provided with multiple and comprehensive interconnections which connect said first Smart Meter device to the other Smart Meter nodes so as to form a network, in which the information on the energy production and consumption status of the network is available in each Smart Meter node of the network at all times, thus allowing the information on the energy status of the network to be instantaneously available to an operator of the power grid by querying a single Smart Meter among those present in the network.

A current detecting device includes a sense resistor through which a sense current corresponding to a consumption current supplied to a load flows, and an A/D converter configured to perform A/D conversion on a voltage drop generated by the sense current flowing through the sense resistor to detect the consumption current. A resistance value of the sense resistor is variably provided.

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.

An apparatus according to an embodiment includes a control circuit to control operations of an inverter and a switch. The control circuit judges whether or not a power system has a power failure, based on values of the system voltage and a frequency of the power system; and calculates a phase difference between a phase of the output voltage of the inverter and a phase of the system voltage and generate, by means of the phase difference, an output frequency pattern for changing a frequency of the output voltage of the inverter. The control circuit, when it is judged that the power system has recovered from the power failure, controls the inverter to change the frequency of the output voltage of the inverter in line with the output frequency pattern, and closes the switch after the phase difference becomes smaller than or equal to a threshold.

Various examples are directed to a solar power electrolyzer system comprising a first electrolyzer stack, a second electrolyzer stack, a first converter and a first converter controller. The first electrolyzer stack may be electrically coupled in series with a photovoltaic array. The first converter may be electrically coupled in series with the first electrolyzer stack and electrically coupled in series with the photovoltaic array. The second electrolyzer stack electrically may be coupled at an output of the first converter. The first converter controller may be configured to control a current gain of the first converter.

An abnormality detection method according to one aspect of the present disclosure is a method of detecting an abnormality in an AC signal to be input from an AC power supply. The method includes, where an ideal AC signal is represented as V.sub.0 sin ωt (V.sub.0: amplitude, co: angular frequency, t: time), calculating an arithmetic value including a value represented by sin.sup.2ωt+cos.sup.2ωt and determining that the AC signal is abnormal when the arithmetic value is out of a threshold range.

Methods, systems, and apparatus, including computer programs stored on a computer-readable storage medium, for obtaining, from an electric field sensor, measurements of a net electric field resulting from a combination of respective electric fields from two or more electrical power conductors that are proximate to the electric field sensor. The apparatus detects a change in successive measurements of the net electric field. The apparatus determines, based on the change, that an electrical fault has occurred in one of the two or more electric power conductors. The apparatus sends to a server system, data indicating that the electrical fault has occurred in one of the two or more electric power conductors.

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.

A method and system are provided for anomaly detection in energy systems. Non-contact sensing of an energy system based on electric and magnetic fields uses non-contact electric- and magnetic-field sensors to produce electric- and magnetic-field signals. The electric and magnetic field signals are filtered to remove noise. Features are extracted and normalized from the magnetic and electric field signals to characterize parameters of each signal. Density-based spatial clustering of extracted features is performed using a selected minimum number of points required to form a cluster and a parameter indicating the distance within which data are considered to fall within the cluster. An anomaly is determined from data point(s) that do not fall within the cluster formed by data points in normal operation. The density-based spatial clustering of extracted features may be performed using a Density-Based Spatial Clustering of Application with Noise (DBSCAN) algorithm. Features may be extracted using Fourier analysis.

A computational current sensor, that enhances traditional Kalman filter based current observer techniques, with transient tracking enhancements and an online parasitic parameter identification that enhances overall accuracy during steady state and transient events while guaranteeing convergence. During transient operation (e.g., a voltage droop), a main filter is bypassed with estimated values calculated from a charge balance principle to enhance accuracy while tracking transient current surges of the DC-DC converter. To address the issue of dependency on a precise model parameter information and further improve accuracy, an online identification algorithm is included to track the equivalent parasitic resistance at run-time.