Distribution System State Estimation Using the Hamiltonian Cycle Theory

Abstract

Since the origin of energy management systems, state estimation applications have aided in automatic power system operations, mainly for transmission systems. Currently, however, smart grid concepts are modifying the behavior of distribution systems through a rapid increase of controllable distributed generators, demand response, and electric vehicles. Consequently, the advanced metering infrastructure is providing a large amount of synchronized metering data with high accuracy and resolution, which favors the development of state estimation procedures to sustain distribution management systems. Therefore, this paper presents the formulation of a novel algorithm for state estimation solution in distribution networks using the Hamiltonian cycle theory, where the network states are quickly obtained through a calculation scheme under the normal operating conditions. Index Terms-Advanced metering infrastructure (AMI), automatic operations, distribution management system (DMS), Hamiltonian cycle, smart grid, state estimation. 1949-3053

Multi-Phase State Estimation Featuring Industrial-Grade Distribution Network Models

“…With the base quantities defined for the compensator circuit as in (7)- (9), the compensator impedance values in ohms and in per-unit are given by (10) and (11), respectively. The phasor voltage on the voltage relay (in per-unit) can be then expressed in terms of the line circuit voltage and current (12); with the compensator impedance properly calibrated, the per-unit relay voltage would match the per-unit voltage at the load bus. The magnitude of the relay voltage V R φ j is therefore set to the desired voltage magnitude at the load bus.…”

“…Squared voltage magnitude at the compensator voltage relay (40), where the V R φ j has a set value; (40) is derived from (12).…”

“…However, in comparison with the voltage-based estimator, the current-based estimator neglects voltage magnitude measurements (except at the substation slack bus) and its computational advantage, which is attributed to decoupling, is limited to radial networks. Recent DSSE approaches are based on meta-heuristics [11] and Hamiltonian cycle theory [12].…”

Sensor Transmission Power Schedule for Smart Grids

“…Amount of research on RTSE has been developed based on the observation data of PMU [7][8][9][10][11][12]. Due to the nonlinearity of power system, nonlinear filter is wildly applied in RTSE.…”

Real-life distribution state estimation integrated in the distribution management system

“…The main reason for this is the relatively low level of telemetry available and huge dimensions of DNs. Various models of DSE can be found, for example, models of traditional (transmission) state estimation (TSE) that are adjusted to the application in DN environment; models that are highly specialized for DN; models that offer procedures for state estimation of both transmission networks and DN; models that are based on heuristic rules; models that are based on probabilistic rules; models that consist of state vector buses voltages; models that consist of state vector branch currents; models that processed measurements of active and reactive powers, currents, and voltages simultaneously; models that disregarded measurements of voltages; and models that include synchronous measurements of voltages and currents phasors …”