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Summary: Power quality has become one of the most vital challenges in the planning of smart distribution grids due to popularity and myriad applications of power electronic devices. This paper introduces a new method based on network reconfiguration to efficiently deliver the energy demand of standalone Diesel/PV/Battery microgrids with improved power quality. The feeder reconfiguration is performed to decrease power loss, decrease the total harmonic distortion (THD), and improve voltage sag indices. Mathematical models of Diesel/PV/Battery standalone system are integrated with harmonic power flow algorithms to implement the concept. Reconfiguration algebraic equations along with nonlinearities introduced by Diesel/PV/Battery standalone system are solved by means of Non‐Dominated Sorting Differential Evolution Algorithm (NDSDEA). The proposed methodology is carried out on a 33‐bus standalone microgrid. The results have shown that reconfiguration not only improves the overall performance of the standalone energy system but also is effective to improve the quality of power delivered to the significant consumers. The method has both economic and technical benefits for such islanded networks. [ABSTRACT FROM AUTHOR]

Accurate modeling of three-phase asymmetric (three- and five-limb) transformers is still a challenge. In this paper, a method for the calculation of the major hysteresis loop for three-phase asymmetric transformers is proposed. All necessary information can be obtained from measurements taken at the transformer terminals without breaking the connections and using only standard tests. For illustration and validation, the method is applied to Wye–wye and Wye–delta connections for three-limb and five-limb transformers, but the method is applicable to all transformer connections. The obtained hysteresis loops are implemented in three-phase dual-reversible models. Simulation results are validated with laboratory experiments. The excellent agreement between simulations and measurements gives great confidence that the method is practical for engineering work. [ABSTRACT FROM AUTHOR]

Thermal behaviour is a prime factor in the accurate performance assessment of power transformers as well as in the prediction of their life expectancy. This study presents a computer modelling tool based on an electro‐thermal equivalent circuit of transformers that is able to predict the hot‐spot temperature and average surface temperatures for all internal layers of distribution‐class toroidal transformers. Temperature is the limiting factor that prevents running transformers for hours or days in overload conditions. The modelling tool presented in this study is capable to identify the safe maximum overload current and duration that a transformer can handle without introducing damage or loss of life. The model is helpful to predict the short‐term (few hours) and long‐term (few days) overload capabilities of transformers. The electro‐thermal model can also be used as a tool to optimise the design and evaluate the performance of transformers. This study is specifically focused on the implementation of the proposed method on dry‐type distribution‐grade toroidal transformers. The model is built using circuit components (lumped R and C) obtained from the thermal–electrical analogy. The model is validated with numerous finite‐element method simulations and laboratory tests with transformers of various power ratings. [ABSTRACT FROM AUTHOR]

In this paper we present a combinatorial scheme based on hidden Markov models (HMM) and wavelet transform (WT) to discriminate between magnetizing inrush currents and internal faults in power transformers. HMMs are powerful tools for transient classification which compute the maximum likelihood probability between training and testing data signals for identification. The WT is employed to extract certain features which reduce the computation burden of HMMs and enhance detection accuracy. The newly extracted feature efficiently discriminates between faults by different trends. The k-means clustering technique is applied to reduce the training procedure time investment. Since the discrimination method is based on the probabilistic characteristics of the signals without application of any deterministic index, more reliable and accurate classification is achieved. This method is independent of the selection thresholds. Based on the proposed algorithm a high-speed relay response (a quarter of a cycle) can be achieved. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer using PSCAD/EMTDC software. [ABSTRACT FROM AUTHOR]

Distribution network reconfiguration (DNR) is a crucial issue especially in the operating scheme of distribution systems. In distribution networks tie switches will be changed in order to facilitate an appropriate configuration satisfying minimum losses. Many researches have been focused on developing evolutionary search algorithms to enhance the convergence rate associated with better performance. In order to achieve a global optimum in less computational effort in network reconfiguration, a novel Real Genetic Algorithm (RGA) is proposed in this paper. To validate the proposed method, it is compared with Differential Evolution Algorithm (DEA) solving the same problem. Both RGA and DEA are applied to the 69-bus and 83-bus distribution test systems. Simulation results regarding computational time validate a significant performance of RGA in comparison with DEA. Case studies and results analysis show the effectiveness of the proposed methodology for DNR problem. [ABSTRACT FROM AUTHOR]

The performance of the T and the \pi equivalent models used to represent transformers are tested under ferroresonance. Comparisons between simulations and laboratory experiments show the superiority of the \pi equivalent circuit. [ABSTRACT FROM AUTHOR]