In one or more embodiments, techniques are provided for modeling overhead line structures of electric railways that utilize a flexible, reusable structure template to automatically generate a 3D model of the overhead line structure. Each structure template includes a set of points that represent joints of the overhead line structure and components that represent elements of the overhead line structure. A feature definition of each joint and component includes properties, constraints and cell mappings. By mapping key points of reference lines for an overhead line structure to key points in an applicable structure templet for the overhead line structure, and applying the constraints and, in some cases the cell mappings, a 3D model of the overhead line structure is automatically generated. The 3D model may be a “low detail” stick representation for fast modeling, or, using the cell mappings, a “high detail” cell-based representation for very realistic modeling.

Systems and methods for demonstrating power control system (PCS) operating sequences provide a model builder and a sequence builder. The PCS model builder allows a user to build a graphical model of a PCS using drag-and-drop objects representing PCS components, such as generators, circuit breakers, transfer switches, and the like. The PCS sequence builder allows the user to enter plain language commands that represent operations performed by the model objects. The model builder then graphically animates the model according to the plain language commands. Animation may include changing an image color, shape, configuration, position, orientation, or size for the model objects. This allows any user to graphically demonstrate how a PCS would work before resources are invested toward developing the PCS.

A converter parameterized constant admittance modeling method based on a cross initialization including the following steps: (1) performing parameterized modeling on a converter, wherein switches are modeled using a parametric historical current source constant admittance model and other components are modeled using a traditional electromagnetic transient simulation integral model in the converter; (2) detecting whether state switching occurs, performing cross initialization correction when occurring; (3) determining model parameters, and establishing an equivalent admittance matrix and an injection current source of a whole grid, to obtain an electromagnetic transient simulation equivalent model; (4) solving a network tide according to a basic solving equation I=YU to obtain an electromagnetic transient model simulation result of the converter at current time; and (5) calculating an equivalent admittance matrix and an injection current source at next time through a current network state quantity, and returning to step (2) until a simulation terminates.

A method determines measurement locations in an energy grid. In the energy grid, use is made of a controllable device for wide-range voltage control. A model of the energy grid is provided which specifies a voltage distribution within the energy grid by a system of equations and/or a system of inequalities depending on the control position of the controllable device. A simulation for minimizing the number of measurement locations is carried out on the basis of the model, and in that as a result of the simulation a minimum number and the respective position of measurement locations and also the control position of the controllable device are specified in order that the energy grid complies with a predefined voltage band during operation.

A Multi-Phase Simulation Environment (“MPSE”) is provided which simulates the conductor current and voltage or electric field of multiple phases of an electrical power distribution network to one or more sensing or measuring devices and includes independent control of wireless network connectivity for each sensing or measuring device, independent control of GPS RF to each device, and interface to a back-end analytics and management system.

A computer-implemented method executed by one or more processors includes receiving interconnection data for a proposed interconnection to a power grid; accessing a power grid model including a topological representation of the power grid, electrical specifications of grid components, and empirical operation characteristics; and generating, using the interconnection data for the proposed interconnection to the power grid, and the power grid model, simulated power grid data. The simulated power grid data is based on simulating operation of the power grid with the proposed interconnection coupled to a location of the power grid identified by the interconnection data during a simulated time period. The simulated power grid data includes a plurality of different temporal and spatially dependent characteristics of the power grid. The method includes evaluating, using one or more metrics, the simulated power grid data; and outputting evaluation results of the one or more metrics.

Systems and methods for making real-time predictions about an arc flash event on an electrical system are disclosed. A virtual system model database is operable for providing a virtual system model for the electrical system and continuously update the virtual system model with real-time data from the electrical system. An analytics server comprises an arch flash simulation engine. The arch flash simulation engine is operable to modify the virtual system model to introduce a short-circuit feature to an uninterrupted power supply bypass circuit branch; choose a standard to supply equations used for arc flash event simulation and energy calculation; simulate an arc flash event utilizing the modified virtual system model; calculate a quantity of arc energy released by the arc flash event using results from the simulation; and communicate a report that forecasts an aspect of the arc flash event.

To improve coordination of processing that uses power demand data. A detail level determination unit 102 calculates, based on detail level format data D2, a detail level dataset D3 from a power demand dataset D1 generated by a power demand simulator 106. A data detail level conversion unit 103 calculates a converted power demand dataset D4 so that a detail level of the power demand dataset D1 matches a detail level required for a simulation in a power system simulator 105. A notification unit 104 displays a detail level conversion result indicated by the converted power demand dataset D4 and notifies the power system simulator 105 of the converted power demand dataset D4.

A method includes a gateway node that obtains a data transmission parameter between a first-level node and each node in a first node set, where the first-level node is located on a first-level power distribution device in a power distribution grid, and the first node set includes a node located on a power distribution device at another level in the power distribution grid other than the first-level power distribution device; determines a second-level node directly coupled to the first-level node from the first node set based on the data transmission parameter between the first-level node and each node in the first node set; and generates a physical network topology of the power distribution grid.

The disclosure discloses a single-ended fault positioning method and system for a HVDC power transmission line based on a hybrid deep network. The method comprises the following: collecting rectification side bus output voltage and current signals of a HVDC power transmission system under different fault types, fault distances and transition resistances as an original data set; eliminating electromagnetic coupling of the bipolar direct-current line by using phase-mode transformation, extracting IMF components of fault voltage and current signals under different fault scenes by using variational mode decomposition, and calculating TEO of the IMF components to obtain a fault data set after feature engineering; normalizing the fault data set, and dividing the fault data set into a training set and a test set; and successively inputting the training set and the test set into a hybrid network of a convolutional neural network and a long short-term memory network for training and testing.