With the introduction of more non-linear loads, e.g., compact fluorescent lamps, electric vehicles, photovoltaics, etc., the need to determine the harmonic impact of the residential load is rising, illustrated by the many studies performed on their harmonic impact. Traditionally, these studies are performed for a single new device and single penetration level, neglecting the harmonic interaction between new types of devices, as well as giving little information at which moment in time possible problems may arise. A composite approach to access the impact of harmonic sources on the distribution network is therefore proposed. This method combines a bottom-up stochastic modeling of the residential load with harmonic measurement data and harmonic load-flows all based on a scenario analysis. The method is validated with measurement data and shows a good prediction of the current level of harmonics in a residential neighborhood for the current situation. To demonstrate the applicability of the proposed method, case studies are performed on the IEEE European Low Voltage Test Feeder. These case studies show a marked difference between applying individual device-based models and a composite modeling approach, demonstrating why the proposed approach is an adequate method for the determination of the impact of new devices on the harmonics.

Voltage dip state estimation (VDSE) tries to estimate the voltage dip characteristics at nonmonitored buses from measured voltage dip values at monitored buses. In this paper, the VDSE is addressed through the method based on Bayesian inference. A priori including the fault position among other grid conditions is used to estimate the residual voltage at each bus based on the measurement quantities, including their uncertainties. The dip duration is calculated with the time setting of protection system incorporating the uncertainties due to dip detection algorithm of the root mean square values. The proposed method has been applied to the IEEE 13-bus and IEEE 123-bus distribution test systems for multiple simulation scenarios, such as with or without distributed generation and different types of faults. The simulation results show good observability of the network.

The magnitudes of voltage dips on non-monitored buses can be estimated by applying Bayes’s theorem with measured voltage dips on a limited number of monitored buses. Bayes’s theorem is used to obtain the grid conditions, including the network impedance, load, distributed generation vector, and short-circuit location, from which the voltage magnitudes along the feeder can be calculated. In order to implement this approach effectively, it is necessary to understand which parameters among the grid conditions are more significant for the estimation performance and how the uncertainties associated with these parameters affect the estimation results. Moreover, since the measurement structure provides observations to calculate the posterior distribution from the prior distribution of the grid conditions. The measurement parameters, quantities, and accuracy levels are varied in simulations to obtain an optimum measurement structure balancing between the cost and the measurement performance. This paper presented the results of the sensitivity studies which have been done on the IEEE 13-Bus and 123-Bus test systems. The results shows the performance of the voltage dip state estimation approach is less sensitive to the uncertainty level of the load while more sensitive to those of the DG, the short-circuit power, the zero sequence impedance of the cable and the short-circuit resistance. The voltage measurement accuracy is more important than that of current and phase angles. The proposed method shows much stronger robustness compared to the conventional one and the estimation performance can be improved through several measures, e.g. adding more measurement points.

The harmonic impedance of the grid needs to be calculated for the harmonic assessment of new connections in the high voltage (HV) network. For these studies, one of the elements is the modeling of the downstream distribution networks. These distribution networks could have a significant impact on the network impedance. For the HV network operator,it is almost impossible to model these networks in detail as not all information of the distribution network operators is readily available. The aim of this paper is to present a simplified representation for the distribution networks and develop a method to determine the parameters to be used in the model via a clustering method. To verify the method of clustering and compare the various equivalents,a comparison is made with an exact representation of a distribution grid. A sensitivity analysis has been performed on the most important parameters.It has been found that the Type-2 load model gives the best result compared the conventional ones. If the network information is known by the transmission system operators (TSOs), the Type-2 load model can be constructed directly. Otherwise the approach based on the clustering process as described in this paper can be used as an alternative.

The performance of the system regarding voltage dips is commonly characterized with the SARFIx index, which gives the average frequency of voltage dips. Due to the limited number of measurement points, the actual residual voltages of many nodes are not taken into account when the index is calculated. As it's impossible to measure the dip level at every node of a feeder, the voltage dips occurring at nonmonitored nodes should be estimated from the values recorded at monitored nodes with the consideration of measurement accuracy. This paper proposes a voltage dip state estimation method based on Bayesian inference. The performance of the proposed method is assessed through case study applied in a typical distribution network. Monte Carlo simulation is used to obtain the statistic results. The proposed method shows the flexibility of different measurement quantities and the adequacy for the analysis of distribution networks.