Operation of distribution networks undergoes dramatic changes in the era of smart grid deployment, due to the higher penetration of distributed generation (usually from intermittent resources), to an ever-increased share of power electronics mediated energy transfer, but also thanks to advancements in instrumentation including smart meters (SMs). In this context, alternative power quality (PQ) monitoring and control, based on a lightweight assessment of voltage parameters to be implemented in new SM would allow optimal real-time network operation and market-correlated services. To this, the authors propose a signal analysis framework for simplified PQ informative assessment method using the so-called instrumentation values available in most of today's SM. It is highlighted that the voltage characteristics made available with high reporting rates can be efficiently used in deriving information on quality of the electricity supplied by public electricity networks. Further applications like smart grid synchro-SCADA observability and voltage control are also addressed in a novel design of SM, with negligible impact on cost.

The future power system integrated a large share of distributed generation (DG) may causes a reduction of the system rotational inertia, resulting in high frequency variations with any disturbances. An additional rotational inertia will be provided to the system if many DG units combined with relative small storage systems operate like virtual synchronous generators (VSG). This paper presents the demonstration approach for the VSGs in order to bring the VSG models developed in the laboratory into practice at two distribution test sites for both single- and three-phase applications. The results show promising results where changes in frequency and voltage are clearly counteracted by power flows from the VSG. In addition, VSG may provide a solution for hosting a large share of DG in future grids while maintaining system stability.

Engineering education by its nature is a costly program in university environments. Perhaps the most costly component is the laboratory facility, usually consisting of specialized equipment. Effective instruction of some topics in power engineering education requires experience with actual equipment, rather than small-scale replicas or simulation. In this paper, a new laboratory approach is described, as implemented in a virtual, Internet-based, experimentation platform. This virtual laboratory (VLab) utilizes real equipment distributed among multiple universities from which remotely located students can perform experiments. The software solution is a multiuser, client-server architecture developed in the LabVIEW environment. Implementation details including video, chat, archiving, and the hardware and software platforms are presented in the paper. An example presented herein is the study of current and voltage waveforms while controlling relays and low-voltage contactors. The applications have been tested with student teams enrolled in the electrical engineering department of Politehnica University of Bucharest and the power engineering program at Arizona State University.

The state estimator constitutes the cornerstone of the supervisory control and data acquisition system since it provides the power system operating situation in consecutive time intervals. Furthermore, the output of the state estimator is used by other tools responsible for the monitoring and control of the power system. Therefore, there is a need for the power system state estimator to be as accurate and reliable as possible. The main source of uncertainty that may deteriorate the accuracy of a weighted least squares (WLS) state estimator, provided that the network parameters are perfectly known, is the uncertainty that is encompassed in the measurements. It is well known that the measurement chain is not ideal and this information is passed to the state estimator through the measurement weights. In this paper, the effect of the measurement weights, which are calculated by considering both the standard uncertainties associated with the measurement devices and the instrument transformers, on the WLS state estimator is examined.

This paper computes the uncertainties associated with the power system state variables obtained with the help of phasor measurement units (PMUs). An integer-quadratic-programming-based method is used to determine the minimum number and the optimal locations of the PMUs to ensure complete topological observability of the system. Three approaches are used to estimate the uncertainties in the state variables: the use of the classical uncertainty propagation theory, the Monte Carlo method, and the random fuzzy variables (RFVs). The methods are applied on a test system, and the results are presented. The choice of method depends on the application; suggestions are offered based on the conclusions of the work in this paper.

The parameters of the transmission lines are used in several monitoring and control tools of the power system control center. An accurate knowledge of the power system model (including transmission lines impedances) impacts positively the situational awareness of the power system operators. It also improves the protection and corrective measures that may be applied in case of a disturbance. Nevertheless, line parameters that are stored in the control center database are typically subjected to errors, and therefore it is necessary to regularly refine them. The presence of a phasor measurement unit (PMU) at both ends of the transmission line allows the calculation of the transmission line parameters using synchronized current and voltage phasor measurements. Although the use of PMU measurements is a straightforward way to calculate the transmission line parameters, the uncertainty of the synchronized phasor measurements should be incorporated to the calculated transmission line parameter values. In this paper, an analytical expression for the bounds of the calculated transmission line parameters from PMU measurements is derived, considering both instrument transformers and PMU errors. The calculated bounds of uncertainty are evaluated through Monte Carlo simulations, while the importance of knowing the bounds of the calculated line parameters is underlined through the derivation of power-voltage curves considering the lower and upper bounds of the parameters. The simulation results are obtained using the IEEE 14-bus system.