Abstract

Abstract
This letter is an enhancement to our previous paper that quantifies additional reinforcement costs (ARCs) for low-voltage assets under moderate degree of three-phase imbalance. The original formulas cause an overestimation of the ARCs under severe imbalance. This letter first quantifies the threshold of the severe degree of imbalance (DIB), below which the original formulas are applicable. Then, the ARC formulas are extended to account for the whole range of DIB. Case studies demonstrate that when the asset loading level is below 33.3% (50%) for a feeder (a transformer), the DIB never exceeds the threshold and the original ARC formulas are applicable; otherwise, the DIB can exceed the threshold and the extended formulas yield correct ARCs.

Abstract
In this paper, typical strengths, fault levels, and source impedances are thoroughly analyzed and calculated for the study of quality of supply in 230/400 V 50 Hz distribution systems. Considering all the disparity in distribution network design, this study is based on a comprehensive database containing typical arrangements and equipment in U.K./European systems, as well as on fully documented generic network models supplying four residential load subsectors in the U.K., i.e., from metropolitan to rural areas. Thus, this paper proposes an alternative method for determining reference values of network supply impedances and short-circuit fault levels at different points and locations of the medium-to-low voltage distribution system. The aim of this study is to provide a wider range of benchmark values than those stipulated in the IEC 60725 Standard, which only defines a single-reference threshold of public supply impedances for all types of distribution systems and residential customers. In order to assist network operators in the planning and design of their distribution systems, these values are further disaggregated and classified in this paper according to network/demand type.

Abstract
An improved and efficient parameter extraction method applied to GaN high electron mobility transistors (HEMTs) is presented. The aim is to extract reliable values for the generally distributed small-signal model to accurately describe the device at mm-wave range. A modified version of hybrid extraction technique combining particle-swarm-optimization and direct fitting has been implemented. S-parameters fitting has been used to validate the model under various bias conditions. The results indicate a very good agreement between model and simulation up to 60 GHz.

Abstract
This paper presents a comparison between different optimization techniques in the context of hybrid small-signal model (SSM) parameter extraction for GaN High Electron Mobility Transistors (HEMTs). The optimization techniques considered in this work are: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). The algorithms were tested for their robustness, convergence speed, and efficiency. PSO algorithm was shown to be the most suitable in terms of robustness and efficiency. However, all techniques were able to obtain credible model parameters, which confirms the reliability of the adopted procedure. The quality of extraction was evaluated by means of S-parameter fitting at different bias conditions.

Abstract
The residential load of existing consumers can be increased significantly due to large-scale purchase of household appliances with high-energy consumption; consequently, changing the expansion plans of electrical distribution networks. In this paper, a spatial-Temporal model is proposed to estimate the load growth of distribution transformers owed for this kind of electrical appliances. In order to determine the location of inhabitants interested in buying these appliances, the proposed approach includes the socioeconomic characteristics of the consumers in a spatial form. After that, the number of appliances added each year is computed using a logistic regression. The results are the residential load curves of distribution transformers, including the additional yearly demand of the new appliances. These curves provide valuable information regarding the distribution network expansion planning.