Various embodiments of the teachings herein include a nonlinear model modeling method. The method may include: determining complete design point data for each of multiple target nonlinear underlying process of multiple types of equipment; establishing a descriptive formula of the process with the ratio of a similarity parameter supported by a similarity criterion to a similarity parameter based on design point data, to obtain a universal model of the process; constructing a machine learning algorithm between the parameter of the actual working condition and the variable parameter and establishing a correlation between the machine learning algorithm and the universal model; and taking the universal models of all the target nonlinear underlying processes of each type of equipment and the correlated machine learning algorithms as a universal model of the type of equipment. The universal model comprises a variable parameter that changes as a parameter of an actual working condition changes.

A circuit breaker distribution system is configured to provide selective coordination. The system comprises a solid-state switch disposed as a main or upstream breaker and a switch with an over current protection disposed as a branch or downstream breaker. The microcontroller to: allow repeated pulses of current through to the branch or downstream breaker in an event of an overload or short circuit, choose a maximum current limit for the solid-state switch as a “chop level” such that the chop level is chosen higher than a rated current of the solid-state circuit breaker but low enough that the solid-state switch is not damaged from repeated pulses over a period of time needed to switch OFF the branch or downstream breaker and add a pulse interval after the current chops to zero but before the solid-state circuit breaker returns to an ON state for a next pulse to begin.

A method and device for training an energy management system in an on-board energy supply system simulation, includes: simulating a driving cycle having defined recuperation; plotting state variables of the on-board energy supply system; calculating a recuperation power from a recu-peration current and a battery voltage; producing input vectors for a neural network; producing a reward function; and training the neural network.

One embodiment provides a system and method for predicting network power usage associated with workloads. During operation, the system configures a simulator to simulate operations of a plurality of network components, which comprises embedding one or more event counters in each simulated network component. A respective event counter is configured to count a number of network-power-related events. The system collects, based on values of the event counters, network-power-related performance data associated with one or more sample workloads applied to the simulator; and trains a machine-learning model with the collected network-power-related performance data and characteristics of the sample workloads as training data 1, thereby facilitating prediction of network-power-related performance associated with a to-be-evaluated workload.

Various embodiments of the teachings herein include a method for simulating an integrated energy system including nonlinear devices. The method may include: receiving a simulation task; building a system of nonlinear equations on the basis of the simulation task and a simulation model of the integrated energy system; and solving a system of nonlinear equations using a linear programming algorithm to obtain a simulation result. The process of establishing the simulation model may include: determining the topological structure of an integrated energy system, the topological structure comprising the devices of the integrated energy system and the connection attributes between the devices; determining general models of the devices and a connector model corresponding to the connection attributes; connecting the general models by means of the connector model to form a simulation model of the integrated energy system; and training the simulation model.

A transient stability assessment method for an electric power system is disclosed. Transient stability tags and steady-state data of the electric power system before a failure occurs are collected from transient stability simulation data. Data sets under different predetermined failures are obtained based on a statistical result of the transient stability tags and a maximum-minimum method. A similarity evaluation index between different predetermined failures is constructed based on a Jaccard distance and a Hausdorff distance. Different predetermined failures are clustered based on a clustering algorithm. A parameters-shared siamese neural network is trained for different predetermined failures in each cluster to obtain a multi-task siamese neural network for the transient stability assessment. Transient stability assessment results of the electric power system under all the predetermined failures are obtained based on the statistical result of the transient stability tags and the multi-task siamese neural network for the transient stability assessment.

A method for creating a photovoltaic system comprising several interconnected solar panels. The method includes the steps of generating an image of an installation site of the photovoltaic system; receiving, from a user, image coordinates corresponding to points on the image; defining an installation area of the solar panels using the image coordinates; receiving site-specific data of the installation site and solar panel specification data of one or more types of solar panels; and generating a layout of the solar panels within the installation area using the installation area, the site-specific data, and solar panel specification data.

A computer-implemented method executed by one or more processors includes providing, for presentation by a display, a user interface including graphics depicting one or more fields for receiving input for simulations of electrical grid scenarios; receiving input for a scenario including a proposed modification to the electrical grid; performing a simulation by modeling the input in a virtual model of an electrical grid; modifying the user interface to include graphics depicting: one or more visualizations of results of the simulation for the scenario; and a menu of options for modifying the input; receiving a selection from the menu of options for modifying the input; performing a modified simulation by modeling the modified input in the virtual model of the electrical grid; and modifying the user interface to include graphics depicting one or more visualizations of the results of the simulation compared to the results of the modified simulation.

A model management apparatus for managing a power grid model having, as a model value, a parameter of a power grid connecting a plurality of sites includes: a storage unit configured to store the model value; a monitoring unit configured to acquire a measured value from each site in the power grid; and a model correction unit configured to calculate a parameter in the power grid based on the measured value obtained from the monitoring unit, and correct the model value using the calculated parameter.

A Multi-Phase Simulation Environment (“MPSE”) is provided. In one embodiment, a simulation environment controller is configured to select a waveform from a waveform playlist and initiate a trigger signal to one or more waveform generators of a plurality of waveform generators to generate the waveform. The plurality of waveform generators are configured to generate the waveform under a phase lock.