Intelligent methods are used more and more nowadays, in various fields, whose main purpose is to find better solutions to problems that are considered difficult. Identifying the parameters of transformer winding is a crucial problem. In this study, the authors shall use the 'particle swarm optimisation' (PSO) method to identify the parameters of highfrequency transformer winding, for diagnostic purposes. They validate the approach by comparing their results with the experimental ones. Two cases study of windings with air core were considered. Not only the effectiveness and accuracy of the proposed method is verified, the PSO method is compared with another methods referred to as transfer function and genetic algorithm. The results are in total agreement and show the reliability and the robustness of the PSO method. 2 Objective and principal of the study The primary goal in this study is to accurately identify the transformer winding parameters, starting from FRA measurements.

No abstract

Purpose It is not a secret that the identification of the high-frequency ladder network model (LNM) parameters for the transformer winding is a crucial task. This paper aims to present the application of one of the latest swarm intelligence algorithms, namely, gray wolf optimizer (GWO) for the identification of the high-frequency LNM parameters for the transformer winding. Design/methodology/approach The physical realizability of a unique ladder network is ensured and it is based on the frequency response analysis and some terminal measurements of a transformer winding. Findings The test results on a real transformer winding indicated that the identified model, which is improved and detailed, is superior in terms of representing the physical behavior of the transformer winding in high frequency. The efficiency and the superior capabilities of the proposed GWO method are demonstrated by comparing the later with recent algorithms, such as particle swarm optimization-simulated annealing and crow search. Results show that the proposed GWO is better in terms of optimal solution and fast convergence. Practical implications The identified LNM model is mutually coupled and able to reflect the physical behavior of the transformer winding in high frequency; therefore, it is more reliable for the diagnosis and analysis. Originality/value Contribution has been offered for the identification and the diagnosis of the transformer winding, using robust algorithms for future research.

<span id="docs-internal-guid-a50ef6a8-7fff-b6d7-8e58-d434be6097d4"><span>This paper proposes a novel approach based on the NSCE (elitist non dominated sorting cross entropy), for the optimization of the location and the size of a flexible AC transmission system device (FACTS) namely: unified power flow controller (UPFC) to achieve the optimal reactive power flow (ORPF). In the present work, the main objective is to minimize the real power losses, the cost investment of several UPFC and the deviation voltages using intelligent algorithms. The proposed study is multiobjective, in which, the power generator buses, the control voltages, the ratio tap changer of transformers and the reactive power injections from installed UPFC are considered as control variables. The proposed NSCE algorithm is validated on IEEE 30-bus test system. A comparison with elitist non dominated sorting genetic algorithm (NSGA-II) and a regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) is done and completed with hybridization of them.</span></span>

No abstract