Methods, systems, and computer readable mediums determining a system state of a power system using a convolutional neural network using a convolutional neural network are disclosed. One method includes converting power grid topology data corresponding to a power system into a power system matrix representation input and applying the power system matrix representation input to a plurality of convolutional layers of a deep convolutional neural network (CNN) structure in a sequential manner to generate one or more feature maps. The method further includes applying the one or more feature maps to a fully connected layer (FCL) operation for generating a respective one or more voltage vectors representing a system state of the power system.

Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energy usage from source location energy usage can be stored, where the first trained machine learning model is trained to predict an amount of energy usage for the target device and the second trained machine learning model is trained to predict when a target device has used energy. Source location energy usage over a period of time can be received, where the source location energy usage includes energy consumed by the target device. An amount of disaggregated target device energy usage over the period of time can be predicted, using the first and second trained machine learning models, based on the received source location energy usage.

A method for remote monitoring includes (1) generating first sensor data from a first sensor at a first network node of a communications network and (2) sending the first sensor data from the first sensor to a second network node that is remote from the first network node, via the communications network. The first network node is powered from an electrical power grid that is separate from the communications network. The first sensor data may be raw sensor data and/or lossless sensor data.

Disclosed is a device for the certified measurement of electric parameters and customers' flexibility behaviour, and for communicating with a distribution system operator, comprises a System on Module, or SoM, a Certification Module, a Customer Communication Module and a Communication Module.

A data update device for electronic control devices includes: a charge schedule setting unit for setting a charge schedule of a battery of an electric vehicle; a charge control unit for charging the battery according to the charge schedule; and a data update control unit for updating data of one electronic control device by acquiring update data from a center device on a condition that an acknowledgement of the user is obtained when receiving a notification of the update data from the center device together with information about time required for the updating, and determining that the updating is possible even if the charge control unit charges the battery according to the charge schedule.

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.

A method for electrical grid monitoring using aggregated smart meter data includes receiving, from an electric grid monitoring device, electrical data aggregated from a plurality of electric meters each connected to a secondary side of at least one electrical transformer within a local geographic region. Each electric meter is configured to generate measurement data based on measurements taken at a respective electrical load. The electrical data indicates one or more characteristics of a secondary electrical distribution system connected to the at least one electrical transformer. The method includes determining, based on the electrical data aggregated from the plurality of electric meters, one or more characteristics of the primary electrical distribution system connected to the electrical transformer.

A software-defined power management and distribution system for spacecraft and other remote systems, which maximizes adjustability of the electrical power systems thereof using software, and without having to change the associated hardware is described. Embodiments of the present apparatus are remotely adjustable, thereby enabling more rapid configuration of spacecrafts during construction as well as reconfiguration thereof while in orbit.

A power supply is disclosed for an industrial control system or any system including a distributed power supply network. In embodiments, the power supply comprises: a battery module including a battery cell and a battery monitor configured to monitor the battery cell; and a self-hosted server operatively coupled with the battery module, the self-hosted server being configured to receive diagnostic information from the battery monitor and provide network access to the diagnostic information. In implementations, the diagnostics stored by the self-hosted server can be broadcast to or remotely accessed by enterprise control/monitoring systems, application control/monitoring systems, or other remote systems via a secured network (e.g., secured access cloud computing environment).

A power supply is disclosed for an industrial control system or any system including a distributed power supply network. In embodiments, the power supply comprises: a battery module including a battery cell and a battery monitor configured to monitor the battery cell; and a self-hosted server operatively coupled with the battery module, the self-hosted server being configured to receive diagnostic information from the battery monitor and provide network access to the diagnostic information. In implementations, the diagnostics stored by the self-hosted server can be broadcast to or remotely accessed by enterprise control/monitoring systems, application control/monitoring systems, or other remote systems via a secured network (e.g., secured access cloud computing environment).