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 method for providing frequency balancing in a power network grid by an RBS of a radio communication network. The RBS includes one or more power supply units, PSUs, connected to the power network grid. The method includes detecting a deviation of a power network grid frequency measured in a PSU of the one or more PSUs, the power network grid frequency is measured upstream of a power factor correction, PFC, unit of the PSU, deactivating the one or more PSU in response to the detected deviation, determining one or more further PSU to be deactivated based on the detected deviation, the RBS is in a first frequency containment reserve, FCR, zone and the one or more further PSU are in a second FCR zone, other than the first FCR zone, and sending a deactivation indication to the determined one or more further PSU.

A method and apparatus for autonomous charge balancing of an energy storage device of the microgrid. In one embodiment the method comprises obtaining, at a droop control module of a power conditioner coupled to an energy storage device in a microgrid, an estimate of a state of charge (SOC) of the energy storage device; introducing a bias, the bias based on (I) the estimate of the SOC and (II) a target SOC value for each energy storage device of a plurality of energy storage devices in the microgrid, to a droop control determination made by the droop control module; and generating, by the power conditioner, an output based on the droop control determination.

An energy management system for an off-electric-grid solar house includes a battery pack that outputs a voltage based on load and has a linear relationship between output voltage and remaining capacity, and a solar energy power source that supplies electric power to be stored in the battery pack. One or more electric devices connected to the battery pack produce the load by drawing electric power from the battery pack. One or more sensors monitor conditions in the house. A control circuit is configured to control the one or more electric devices based on the monitored conditions and the remaining capacity in the battery pack, as the battery pack is charged by electricity from the solar energy power and discharged by load from the electric devices. The control circuit manages priority among the electric devices for changing operating status depending on remaining battery capacity.

The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

Provided in the present invention are a microgrid control system and a microgrid, the microgrid control system comprising: a grid-connected switch, an energy router, a first controller and a second controller; the first controller controls the grid-connected switch and sends a first control instruction; the second controller receives the first control instruction and responds to the first control instruction for controlling the energy router.

A system may include a transformer that may convert a first voltage to a second voltage, such that the second voltage is output via a conductor. The system may also include a wireless current sensor that may detect current data associated with current conducting via the conductor and a processor. The processor may receive the current data, determine one or more temperature measurements associated with the transformer based on the current data, and send a signal to a component in response to the one or more temperature measurements exceeding one or more respective threshold values.

A system for delivering power and data over a single wire via a hub, wherein the hub can control and power multiple low-power Class 2 circuits. The hub can be controlled remotely through a computing device such as a mobile phone or a computer.

A system for monitoring and analyzing electrical operating parameters of a load (10) in a electric network (20), said system comprising a smart socket (110) arranged to be placed in series between the load (10) and the electric network (20), said smart socket (110) comprising a voltage detection module arranged to measure a voltage value in the electric network (20), as an electric potential difference between the ends of the load (10), a current detection module in the electric network (20) arranged to measure a current value adsorbed by the load (10), when the load (10) is connected to the electric network (20), a control unit connected to the voltage detection module and to the current detection module. In particular, the control unit is arranged to carry out a periodic acquisition of the voltage value in said electric network (20), obtaining a voltage trend over time and a periodic acquisition of the current value adsorbed by the load (10), obtaining a current trend over time. In particular, the control unit comprises a neural network arranged to carry out a training comprising the steps of definition of a number n of events E.sub.i′ association, to each event E.sub.i′ of a number m.sub.i of patterns p.sub.ij of predetermined current and/or voltage trends, extrapolation of characteristic parameters c.sub.ik distinguishing the pattern p.sub.ij associated with the classified event E.sub.i′. The neural network is then arranged to carry out an analysis of the acquired voltage and/or current trend by the definition and the classification of possible anomalous patterns with respect to predetermined voltage and/or current trends.

A method identifies asymmetrical vibrations during the operation of an electric device which is connected to a high-voltage grid. Vibrations of the electric device are detected using vibration sensors which provide measured values on the output side. The measured values and/or values derived from the measured values are transmitted to a communication unit via a close-range communication connection. The measured values and/or the values are transmitted by the communication unit to a data processing cloud via a far-range communication connection. The measured values are separated into frequency components by the data processing cloud using a Fourier transformation, thereby obtaining a frequency spectrum. Odd and even frequency components of the frequency spectrum are ascertained based on a base frequency of the high-voltage supply grid and put into a ratio R relative to one another. The presence of asymmetrical vibrations is indicated if the ratio R exceeds a specified threshold.