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The line parameters of power distribution systems stored in the dispatching center may have large errors due to the line aging and system operational dynamics. Such line parameter errors may directly affect the state estimation accuracy, and the advanced management functionalities rely on these parameters. This paper proposes an augmented state estimation method of erroneous distribution line parameters considering the presence of phasor measurement units (PMUs). The augmented state-space models under single and multiple measurement snapshots are established separately by combining the parameter state equation with the state-space model of the distribution network. The augmented state estimation based on the improved adaptive unscented Kalman filter (IAUKF) is developed. The proposed method can effectively perceive the process noise statistical parameter of the erroneous parameters during the estimation to accurately estimate line parameters. The proposed solution is validated through simulation experiments using the IEEE 33-bus system and the numerical results demonstrated its accurate parameter estimation performance.Also, the scalability of the proposed algorithm is demonstrated based on the simulation of the 118-bus system with distributed generators. [ABSTRACT FROM AUTHOR]

This paper proposes a novel data driven framework for detecting topology transitions in a distribution network. The framework analyzes data from phasor measurement units (PMUs) and relies on the fact that changes in network topology results in changes in the structure and admittance of the network. Using voltage and current phasors recorded by PMUs, the proposed method approximates network parameters using an ensemble-based deep learning model and thus, it does not require any knowledge of network parameters and load models. Using the prediction error of the proposed model, a connectivity matrix which shows the status of switches is constructed. In contrast to other methods, this proposed framework does not require a library of voltage and current transients associated with possible network transitions. It can also detect simultaneous switching actions and is robust to noise and load variations. The model yields a lower error detection rate, and its performance is validated using a modified version of the IEEE 33 bus network and a real feeder located in Queensland, Australia, under full and partial observability conditions. The proposed model has also been compared with another data driven method in terms of inference time and error detection rates. [ABSTRACT FROM AUTHOR]

In recent years, with the expansion of urban scale and construction of energy infrastructure, more cities construct underground cables to replace overhead lines from the perspective of environment and safety. Thus, more distributed capacitive current flows back to distribution networks under fault conditions and affects the accuracy of fault locating. This paper proposes an iterative modification method of capacitive current to decrease the uncertainty of complex equations with micro synchronous phasor measurement unit (μPMU) data. Firstly, the initial fault location is calculated by using phasor measurement data without the shunt effect of grounding capacitance. Then, the capacitive current is estimated by the initial fault location distance, and the location result is modified by an iterative strategy. Finally, after iteration modification, a more accurate real number solution for complex equations can be obtained for complicated distribution networks with the uncertain capacitive current. The simulation results of a real underground distribution network and a modified IEEE 33 node test feeder in MATLAB/Simulink show that the proposed algorithm has higher accuracy than the traditional methods which do not have the iterative modification method of capacitive current under different fault locations, fault resistances, fault types and the neutral point grounding modes. [ABSTRACT FROM AUTHOR]

With the integration of intermittent renewable energy sources in the distribution network, the number of network reconfiguration events has increased significantly. In a medium voltage distribution network, most of the critical circuit breakers’ (CBs’) statuses are monitored by remote terminal units (RTUs). However, in many cases, some of the CBs’ status may not be correctly updated by the supervisory control and data acquisition (SCADA) system because of communication failure and data packet loss issues. Thus, the distribution system operator (DSO) cannot solely rely on CB status provided by the SCADA for topology detection. This paper proposes a data-driven topology tracking algorithm for active distribution networks. The topology of the distribution network is represented with a time-varying connectivity matrix. The changes in network topology are detected by estimating the elements of the bus connectivity matrix using voltage phase angle measurements provided by a sparse set of micro phasor measurement units ($\mu$ PMUs). The algorithm extracts information from previous network topology using an $l_1$ norm regularization on the difference of consecutive connectivity matrices. The changes in topology can be detected by observing a few $\mu$ PMU phasor samples. The algorithm is tested on IEEE benchmark test feeders with real load profiles. [ABSTRACT FROM AUTHOR]

This paper presents an innovative method for fault location in distribution networks based on classical circuit analyses. Two synchronized and few nonsynchronized pre- and during-fault voltages are required at few buses along with the impedancematrix. A new impedance matrix manipulation procedure is proposed so that the distribution system is investigated in partitions across multiple subsystems. This procedure allows the fault location process to be performed by solving systems of determined equations, making the method more technically accessible. The fault is located by analyzing the voltage sag at the terminal-bus of each subsystem separately. The proposed method is validated on the IEEE 33-bus, 12.66 kV distribution system with/without distributed generation (DG). Simulation results show the robustness and accuracy of the method under several pre- and during-fault scenarios and measurements errors. [ABSTRACT FROM AUTHOR]

As rooftop solar PVs installed by residential customers penetrate in low voltage distribution network (LVDN), some issues, e.g. over/under voltage and unbalances, which may undermine the network's operational performance, need to be adequately addressed. To mitigate unbalances in LVDN, phase-switching devices (PSDs) and static var compensator (SVC) are two equipment that is cost-effective and efficient. However, most existing research on operating PSDs is based on inflexible heuristic algorithms or without considering the network formulation, which may lead to strategies that violate operational requirements. Moreover, few pieces of literature have been reported on mitigating unbalances in LVDN via SVC and PSDs together. This paper formulates the decision-making process as a mixed-integer non-convex programming (MINCP) problem after developing an SVC model for dispatch purpose. Compared with existing work, the proposed method aims at minimizing current unbalance based on their phasor values and takes the network's operational requirements into account. To efficiently solve the challenging problem, the MINCP is reformulated as a mixed-integer second order-cone programming (MISOCP) problem based on either exact reformulations or accurate approximations, making it possible to employ efficient off-the-shelf solvers. Simulations based on two modified IEEE systems and a practical Australian LVDN demonstrates the efficiency of the proposed method in mitigating unbalances in LVDN. [ABSTRACT FROM AUTHOR]

To effectively manage the growing number of distributed energy resources, more extensive monitoring is needed at distribution network levels, where a challenging combination of constraints and cost-effectiveness requirements must be taken into account. In this paper, we consider the idea of combining accurate phasor measurement with the capability to analyze harmonic and interharmonic phasors. This creates the need to deal with multiple phasor components having different amplitudes, including interharmonics with unknown frequency locations. For this purpose, we propose an enhanced compressive sensing-based algorithm for the estimation of Taylor–Fourier coefficients in a multifrequency dynamic phasor model. We characterize its accuracy in harmonic and interharmonic phasor measurement and discuss reporting rate and latency performance. We show that very good accuracy can also be achieved as a standard phasor measurement unit, allowing to combine multiple functions into a promising new-generation measurement, monitoring, and diagnostic device. [ABSTRACT FROM AUTHOR]