Abstract: A computer system provides online state estimation (SE) and topology identification (TI) using advanced metering infrastructure (AMI) measurements in a distribution network. The computer system obtains input data including the AMI measurements, a network configuration, and line parameters; solves an SE and TI problem formulated from the input data and power equations of the distribution network; and periodically updates states and topology of the distribution network during power system operation. To solve the SE and TI problem, the computer system constructs a mixed-integer convex approximation programming (MICP) model to obtain an initial topology; generates neighboring spanning trees according to the MICP model and the initial topology; evaluates performance of each neighboring spanning tree with a matching index that is an indication of power flow performance; and chooses a tree topology of a neighboring spanning tree having a minimum matching index as a final network topology of the distribution network.

Abstract: A computer system provides online state estimation (SE) and topology identification (TI) using advanced metering infrastructure (AMI) measurements in a distribution network. The computer system obtains input data including the AMI measurements, a network configuration, and line parameters; solves an SE and TI problem formulated from the input data and power equations of the distribution network; and periodically updates states and topology of the distribution network during power system operation. To solve the SE and TI problem, the computer system constructs a mixed-integer convex approximation programming (MICP) model to obtain an initial topology; generates neighboring spanning trees according to the MICP model and the initial topology; evaluates performance of each neighboring spanning tree with a matching index that is an indication of power flow performance; and chooses a tree topology of a neighboring spanning tree having a minimum matching index as a final network topology of the distribution network.

Abstract: An online static security assessment (SSA) method based on a quasi steady-state (QSS) model is applied to a power system. An input to the method includes a post-contingency state of the power system for each of a set of contingencies. The following operations are performed for each contingency. Using the QSS model of the post contingency state of the power system, a steady-state voltage magnitude is calculated for each bus in the power system by solving a system of equations. The system of equations is formulated according to a time-domain stability model of the power system and includes nonlinear differential algebraic equations (DAE) with continuous and discreet variables. The derivative terms of short-term state variables in the DAE are set to zero. The method compares the calculated voltage magnitude with a limit, classifies each contingency as secure, critical or insecure, and determines a control action in response to the classification.

Abstract: A method for state estimation of a distribution network comprises: (a) obtaining measurements from phasor measurement units (PMUs) placed at buses in the distribution network; (b) constructing a quotient gradient system (QGS) based on a constraint set H that relates the measurements to state variables of the distribution network; (c) integrating the QGS to reach a steady state; (d) identifying one or more of the state variables whose measurement residuals violate a measurement residual constraint in the constraint set H; (e) integrating a reconstructed QGS, which is reconstructed based on the constraint set H by setting the identified one or more state variables to values of corresponding PMU measurements; (f) iterating steps of (d) and (e) until no measurement residuals violate the measurement residual constraint, to thereby obtain the state estimation; and (g) reporting the state estimation to a control system during real-time monitoring of the distribution network.

Abstract: An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to obtain a system of nonlinear equations characterizing a physical system, to construct a flexible embedded system from the system of nonlinear equations, wherein the flexible embedded system is configurable with any of a plurality of arbitrary reference states as respective starting points, to utilize the flexible embedded system to generate at least one output providing a solution to the system of nonlinear equations, and to establish one or more operating parameters of the physical system based at least in part on the output generated utilizing the flexible embedded system. The physical system is configured in accordance with the one or more established operating parameters. The physical system in some embodiments illustratively comprises an electric power system, an integrated circuit, or another type of electrical or electronic system or circuit.

Abstract: Predictive analytic models are hierarchically built based on a training dataset, which includes pairs of input data and output data. First, the input data and the output data are preprocessed. A hierarchical clustering process is performed on the dataset. The hierarchical clustering process comprises level-1 input and output data clustering, level-2 input and output data clustering, and so on, up to level-K input and output data clustering, where K is an integer greater than one. A hierarchical model building process is performed. The hierarchical model building process comprises level-1 model building over level-1 clustered input and output data, level-2 model building over level-2 clustered input and output data, and so on, up to level-K model building over level-K clustered input and output data. At least one level-K predictive model is generated as the resulting built model.

Abstract: A method for state estimation of a distribution network comprises: (a) obtaining measurements from phasor measurement units (PMUs) placed at buses in the distribution network; (b) constructing a quotient gradient system (QGS) based on a constraint set H that relates the measurements to state variables of the distribution network; (c) integrating the QGS to reach a steady state; (d) identifying one or more of the state variables whose measurement residuals violate a measurement residual constraint in the constraint set H; (e) integrating a reconstructed QGS, which is reconstructed based on the constraint set H by setting the identified one or more state variables to values of corresponding PMU measurements; (f) iterating steps of (d) and (e) until no measurement residuals violate the measurement residual constraint, to thereby obtain the state estimation; and (g) reporting the state estimation to a control system during real-time monitoring of the distribution network.

Abstract: A computer system provides real-time control of power dispatch for a power system. The power system includes power generators, renewable power generators, load, and storage devices interconnected by a power grid. The computer system obtains input data, and solves an online multi-period power dispatch problem formulated from the input data and incorporates AC power flow in the power grid. The computer system generates control signals according to a solution of the online multi-period power dispatch problem, and sends the control signals to controllers of the power generators and the storage devices. In every time period during operation of the power system, the computer system updates the solution, generates updated control signals according to the updated solution, and sends the updated control signals to the controllers to continuously operate the power system with minimized operational cost while fully utilizing renewable power output.

Abstract: An online static security assessment (SSA) method based on a quasi steady-state (QSS) model is applied to a power system. An input to the method includes a post-contingency state of the power system for each of a set of contingencies. The following operations are performed for each contingency. Using the QSS model of the post contingency state of the power system, a steady-state voltage magnitude is calculated for each bus in the power system by solving a system of equations. The system of equations is formulated according to a time-domain stability model of the power system and includes nonlinear differential algebraic equations (DAE) with continuous and discreet variables. The derivative terms of short-term state variables in the DAE are set to zero. The method compares the calculated voltage magnitude with a limit, classifies each contingency as secure, critical or insecure, and determines a control action in response to the classification.

Abstract: A computer system provides real-time control of power dispatch for a power system. The power system includes power generators, renewable power generators, load, and storage devices interconnected by a power grid. The computer system obtains input data, and solves an online multi-period power dispatch problem formulated from the input data and incorporates AC power flow in the power grid. The computer system generates control signals according to a solution of the online multi-period power dispatch problem, and sends the control signals to controllers of the power generators and the storage devices. In every time period during operation of the power system, the computer system updates the solution, generates updated control signals according to the updated solution, and sends the updated control signals to the controllers to continuously operate the power system with minimized operational cost while fully utilizing renewable power output.

Abstract: A user-preference-enabling (UPE) method optimizes operations of a system based on user preferences. The operations of the system are modeled as a user-preference-based multi-objective optimization (MOO) problem having multiple object functions subject to a set of constraints. The set of constraints include system constraints and a wish list specifying a respective user-preferred range of values for one or more of the objective functions. The UPE method calculates a wish list feasible solution (WL-feasible solution) to the user-preference-based MOO problem. The UPE method can be performed iteratively to compute targeted Pareto-optimal solutions. The UPE method can be used in a hybrid method in combination with other numerical methods to reliably compute feasible solutions of both conventional MOO problems and user-preference-based MOO problems.

Abstract: The available delivery capability (ADC) of a power distribution network with respect to a power transaction is evaluated in real-time. The power transaction involves simultaneous power deliveries from power sources in a source area to loads in a sink area. First, a list of contingencies are ranked in the power distribution network with respect to static security constraints to obtain a subset of top-ranked contingencies. For each top-ranked contingency in the subset, a representation of the power transaction in a steady state of the power distribution network, in a form of parameterized three-phase power flow equations, is solved to obtain a corresponding power delivery capability (PDC) and a corresponding binding constraint among the static security constraints. The reliability of the power transaction in the power distribution network is then evaluated based on, at least in part, a first contingency PDC which is a smallest PDC among obtained corresponding PDCs.

Abstract: An apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to obtain a system of nonlinear equations characterizing a physical system, to construct a flexible embedded system from the system of nonlinear equations, wherein the flexible embedded system is configurable with any of a plurality of arbitrary reference states as respective starting points, to utilize the flexible embedded system to generate at least one output providing a solution to the system of nonlinear equations, and to establish one or more operating parameters of the physical system based at least in part on the output generated utilizing the flexible embedded system. The physical system is configured in accordance with the one or more established operating parameters. The physical system in some embodiments illustratively comprises an electric power system, an integrated circuit, or another type of electrical or electronic system or circuit.

Abstract: A user-preference-enabling (UPE) method optimizes operations of a system based on user preferences. The operations of the system are modeled as a user-preference-based multi-objective optimization (MOO) problem having multiple object functions subject to a set of constraints. The set of constraints include system constraints and a wish list specifying a respective user-preferred range of values for one or more of the objective functions. The UPE method calculates a wish list feasible solution (WL-feasible solution) to the user-preference-based MOO problem. The UPE method can be performed iteratively to compute targeted Pareto-optimal solutions. The UPE method can be used in a hybrid method in combination with other numerical methods to reliably compute feasible solutions of both conventional MOO problems and user-preference-based MOO problems.

Abstract: The available delivery capability (ADC) of a power distribution network with respect to a power transaction is evaluated in real-time. The power transaction involves simultaneous power deliveries from power sources in a source area to loads in a sink area. First, a list of contingencies are ranked in the power distribution network with respect to static security constraints to obtain a subset of top-ranked contingencies. For each top-ranked contingency in the subset, a representation of the power transaction in a steady state of the power distribution network, in a form of parameterized three-phase power flow equations, is solved to obtain a corresponding power delivery capability (PDC) and a corresponding binding constraint among the static security constraints. The reliability of the power transaction in the power distribution network is then evaluated based on, at least in part, a first contingency PDC which is a smallest PDC among obtained corresponding PDCs.

Abstract: An optimal power flow (OPF) problem formulates constraints and operation of an electric power system. A method and system is provided for generating a secure OPF solution that solves the OPF problem. A list of contingencies is created from system data. An OPF solution is computed for the electric power system to optimize an objective function value under the constraints of the electric power system. Voltage stability analysis is performed on the electric power system that operates in states represented by the OPF solution. Then the contingencies are ranked according to load margins of the electric power system. If there is at least an insecure contingency with a non-positive load margin in the list of contingencies, a set of preventive controls are computed and applied to control components in the electric power system. The method is performed iteratively to obtain the secure OPF solution.

Abstract: Predictive analytic models are hierarchically built based on a training dataset, which includes pairs of input data and output data. First, the input data and the output data are preprocessed. A hierarchical clustering process is performed on the dataset. The hierarchical clustering process comprises level-1 input and output data clustering, level-2 input and output data clustering, and so on, up to level-K input and output data clustering, where K is an integer greater than one. A hierarchical model building process is performed. The hierarchical model building process comprises level-1 model building over level-1 clustered input and output data, level-2 model building over level-2 clustered input and output data, and so on, up to level-K model building over level-K clustered input and output data. At least one level-K predictive model is generated as the resulting built model.

Abstract: A smart power flow solver integrates the TRUST-TECH based power flow methodology into a power flow solution. A first solver is applied to a power flow problem of a power system. If results of the first solver diverge, A Trust-Tech based solver is applied to the power flow problem by: transforming the power flow problem to an unconstrained global optimization problem; iteratively solving a dynamical system associated with the unconstrained global optimization problem; and determining whether a solution exists for the power flow problem based on whether the iteratively solving of the dynamical system converges. If a solution exists for the power flow problem, a second solver is then applied to the power flow problem. An operating state of the power system is generated based on results of the second solver to enable proper operation of the power system.

Abstract: A data monitoring system detects an anomaly condition of a device having attached sensors. The system builds one or more models to establish normal behaviors of the device by analyzing historical sensor data, and apply the models to target sensor data of the device to compute one or more anomaly scores of the device. The system reports the condition of the device based on an analysis of the anomaly scores. To build the one or more models, the system identifies at least one optimization problem for each of the models; constructs a dynamical system such that stable equilibrium points (SEPs) of the dynamical system have one-to-one correspondence with local optimal solutions of the at least one optimization problem; finds the local optimal solutions by computing the SEPs of the dynamical system; and identifies a global optimal solution to the at least one optimization problem among the local optimal solutions.

Abstract: A global optimizer system optimizes an objective function of an industrial system subject to a set of constraint functions. The objective function and the constraint functions are modeled as a constrained nonlinear optimization problem. The global optimizer system computes a global optimal solution to the constrained nonlinear optimization problem by performing the steps of: (a) constructing a dynamical system associated with optimality conditions of the constrained nonlinear optimization problem; (b) starting from an initial point, performing a deterministic, tier-by-tier dynamical search on the dynamical system and obtaining a complete set of stable equilibrium points (SEPs) of the dynamical system; (c) identifying a complete set of local optimal solutions to the constrained nonlinear optimization problem from the complete set of SEPs; and (d) identifying the global optimal solution to the constrained nonlinear optimization problem from the complete set of local optimal solutions.