Abstract: Various embodiments include a computer-aided method for state estimation of a power network using an artificial neural network with one or more inputs technically associated with the power network. The method may include calculating a state estimation using captured temporal measurement signals of the power network as inputs for the neural network. At least an amplitude of a harmonic of one of the measurement signals is used as an input for the neural network.

Abstract: The teachings herein include methods for operating a low- or medium-voltage power supply network including lines and a plurality of systems connected thereto. The method may include: providing GIS data with geographic location information relating to the power supply network; producing a branch model of the lines on the basis of the GIS data, including determining a starting probability for connections; providing system data characterizing at least one system; producing a prior model of the network by combining the branch model with the system data, including determining a starting probability for the system; generating an input state describing a possible state of the power supply network described by the prior model; carrying out a load flow simulation calculating a state variable on the basis of the input state; determining a state model of the power supply network on the basis of the state variable; and operating the power supply network based on the state model.

Abstract: A method operates a technical or non-technical system. At least one information element of a first type which relates to the system and is dependent on the respective system state of the system is transmitted according to the method from at least one first facility of the system to at least one second facility. The second facility uses at least one information element of a second type which originates neither from the first facility nor from a different facility of the system, i.e. it comes from a source other than the system, to estimate the system state, checks, on the basis of the estimated system state, whether the received information matches the estimated system state to a predefined extent, and, in the event of a match to the predefined extent, regards the information as trusted, and otherwise generates a warning signal indicating a possible data attack.

Abstract: Various embodiments of the teachings herein include a method for ascertaining a state of an electrical grid. The state of the electrical grid is represented by ascertained voltages and ascertained phase angles at one or more grid nodes of the electrical grid. The method may include calculating the state using a computing unit, wherein the computing unit employs an iterative, numerical method on the basis of a plurality of measured values associated with the electrical grid. The numeral method begins with an initial value. The initial value includes an initial state ascertained from the measured values by an artificial neural network.

Abstract: A method for creating for training an artificial neural network for a state estimation of a power network from a first training dataset, said dataset comprising a plurality of training pairs, each pair formed by a measurement dataset and an associated state of the power network, and the measurement dataset comprises complex apparent powers associated with the power network. The method may include: determining a first training pair, an associated measurement dataset and state with an error greater than or equal to a defined error limit; calculating a second state using a load flow calculation with a complex apparent power modified in comparison with the first dataset; calculating a second measurement dataset from the second state using a measurement model; and creating the second training dataset from the first by adding a second training pair formed from the second measurement dataset and the associated second state.

Abstract: The present disclosure relates to charging energy stores, for example for charging electrical vehicle batteries. Various embodiments may include methods for controlling charging operations. For example a method for controlling charging operations of a plurality of electric vehicles may include: analyzing a data record including arrival times, departure times, and charging energies of the plurality of electric vehicles during a first time period; determining an upper limit for charging power based on the data record, the determined upper limit for charging power defined by an amount of power required to charge each electric vehicle between its respective arrival time and departure time; and charging the electric vehicles without exceeding the determined upper limit for charging power during a second time period following the first time period.

Abstract: Systems and methods for probability forecasts and an energy transmission and/or energy distribution network are provided. Operational management may be carried out using a network control system with systematic consideration of forecast uncertainties. The probability of a distribution network being operable in a stable manner (e.g., with N-1 certainty) in a planning period is included. The system includes a forecaster for forecasts for a planning period, a forecast analyzer connected to the forecasts from the at least one forecaster, and elements for further information for outputting estimated forecast uncertainties. The system also includes a stability probability analyzer connected to the forecasts from the at least one forecaster, the estimated forecast uncertainties from the forecast analyzer, and elements for further information for outputting at least one item of information relating to an N-1 stability of the distribution network in the planning period.

Abstract: A method operates a technical or non-technical system. At least one information element of a first type which relates to the system and is dependent on the respective system state of the system is transmitted according to the method from at least one first facility of the system to at least one second facility. The second facility uses at least one information element of a second type which originates neither from the first facility nor from a different facility of the system, i.e. it comes from a source other than the system, to estimate the system state, checks, on the basis of the estimated system state, whether the received information matches the estimated system state to a predefined extent, and, in the event of a match to the predefined extent, regards the information as trusted, and otherwise generates a warning signal indicating a possible data attack.

Abstract: The present disclosure relates to charging energy stores, for example for charging electrical vehicle batteries. Various embodiments may include methods for controlling charging operations. For example a method for controlling charging operations of a plurality of electric vehicles may include: analyzing a data record including arrival times, departure times, and charging energies of the plurality of electric vehicles during a first time period; determining an upper limit for charging power based on the data record, the determined upper limit for charging power defined by an amount of power required to charge each electric vehicle between its respective arrival time and departure time; and charging the electric vehicles without exceeding the determined upper limit for charging power during a second time period following the first time period.

Abstract: Systems and methods for probability forecasts and an energy transmission and/or energy distribution network are provided. Operational management may be carried out using a network control system with systematic consideration of forecast uncertainties. The probability of a distribution network being operable in a stable manner (e.g., with N-1 certainty) in a planning period is included. The system includes a forecaster for forecasts for a planning period, a forecast analyzer connected to the forecasts from the at least one forecaster, and elements for further information for outputting estimated forecast uncertainties. The system also includes a stability probability analyzer connected to the forecasts from the at least one forecaster, the estimated forecast uncertainties from the forecast analyzer, and elements for further information for outputting at least one item of information relating to an N-1 stability of the distribution network in the planning period.