Here, it is presented a novel Cuckoo Search (CS) algorithm called Cuckoo‐GRN (Cuckoo Search with Genetically Replaced Nests), which combines the benefits of genetic algorithm (GA) into the CS algorithm. The proposed method handles the abandoned nests from CS more efficiently by genetically replacing them, significantly improving the performance of the algorithm by establishing optimal balance between diversification and intensification. The algorithm is used for the optimal location and size of distributed generation units in a distribution system, in order to minimise active power losses while improving system voltage stability and voltage profile. The allocation of single and multiple distribution generation units is considered. The proposed algorithm is extensively tested in mathematical benchmark functions as well as in the 33‐bus and 119‐bus distribution systems. Simulation results show that Cuckoo‐GRN can lead to a substantial performance improvement over the original CS algorithm and others techniques currently known in literature, regarding not only the convergence but also the solution accuracy.

In this study, a methodology for automatic recognition of multiple simultaneous types of partial discharges (PDs) in hydro-generator stator windings was proposed. All the seven PD sources typical in rotating machines were considered, and up to three simultaneous sources could be identified. The functionality of identifying samples with no valid PDs was also incorporated using a new technique. The data set was composed of phase-resolved partial discharge (PRPD) patterns obtained from on-line measurements of hydro-generators. From an input PRPD, noise and interference were removed with an improved version of an image-based denoising algorithm previously proposed by the authors. Then, a novel image-based algorithm that separates partially superposed PD clouds was proposed, by decomposing the input pattern into two sub-PRPDs containing discharges of different natures. From the sub-PRPDs, one extracts features quantifying the PD distribution over amplitudes and the contour of PD clouds. Those features are fed as inputs to several artificial neural networks (ANNs), each of which solves a part of the classification problem and acts as a block of a larger system. Once trained, ANNs work collaboratively to identify an unknown sample. Good results were obtained, with overall accuracies ranging from 88% to 94.8% for all the considered PD sources.

Rectangular graphene loop antenna for terahertz (THz) band is proposed and analyzed. Symmetry properties of the antenna in terms of currents and the radiated fields are investigated. By means of numerical simulations, the antenna is analyzed in free space and on a semi-infinite substrate. It is shown that the maximum total efficiency and minimum input reflection coefficient occur around the second resonance frequency of the antenna. Depending on the relative permittivity of the semi-infinite substrate considered in this article (from 1 to 3.8), the maximum total efficiency of the proposed antenna ranges from 50.1% to 57.4% and its fractional bandwidth (BW) is between 85.2% and 96.6%. Fractional BW of loop antenna can be approximately twice that of graphene dipole antenna and maximum total efficiency can be about 14% higher. BW augmentation is explained using developed equivalent circuit models for dipole and loop antennas. It is also shown that under specific conditions, the radiation pattern of the proposed device can be approximated by an equivalent scheme of two coupled small electric dipole antennas. Finally, we demonstrate that the proposed antenna tolerates moderate variations of graphene parameters.

A novel graphene antenna composed of a graphene dipole and four auxiliary graphene sheets oriented at ${90}^{\circ }$ to each other is proposed and analyzed. The sheets play the role of reflectors. A detailed group-theoretical analysis of symmetry properties of the discussed antennas has been completed. Through electric field control of the chemical potentials of the graphene elements, the antenna can provide a quasi-omnidirectional diagram, a one- or two-directional beam regime, dynamic control of the beam width and, due to the vertical orientation of the dipole with respect to the base substrate, a ${360}^{\circ }$ beam steering in the azimuth plane. An additional graphene layer on the base permits control of the radiation pattern in the $\theta$-direction. Radiation patterns in different working states of the antenna are considered using symmetry arguments. We discuss the antenna parameters such as input reflection coefficient, total efficiency, front-to-back ratio, and gain. An equivalent circuit of the antenna is suggested. The proposed antenna operates at frequencies between 1.75 THz and 2.03 THz. Depending on the active regime defined by the chemical potentials set on the antenna graphene elements, the maximum gain varies from 0.86 to 1.63.

In this paper, a novel image denoising algorithm and novel input features are proposed. The algorithm is applied to phase-resolved partial discharge (PRPD) diagrams with a single dominant partial discharge (PD) source, preparing them for automatic artificial-intelligence-based classification. It was designed to mitigate several sources of distortions often observed in PRPDs obtained from fully operational hydroelectric generators. The capabilities of the denoising algorithm are the automatic removal of sparse noise and the suppression of non-dominant discharges, including those due to crosstalk. The input features are functions of PD distributions along amplitude and phase, which are calculated in a novel way to mitigate random effects inherent to PD measurements. The impact of the proposed contributions was statistically evaluated and compared to classification performance obtained using formerly published approaches. Higher recognition rates and reduced variances were obtained using the proposed methods, statistically outperforming autonomous classification techniques seen in earlier works. The values of the algorithm’s internal parameters are also validated by comparing the recognition performance obtained with different parameter combinations. All typical PD sources described in hydro-generators PD standards are considered and can be automatically detected.