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Multiple‐input multiple‐output (MIMO) radars may outperform other radar systems such as phased array radars, in terms of higher resolution, better detection probability in the presence of interferences, better parameter identifiability and more flexibility in beampattern design. Waveform covariance matrix design, because of its role in the beampattern synthesis process, is one of the most important problems in MIMO radar systems. In this study, the authors have proposed a closed‐form solution based on Fourier series coefficients to design a covariance matrix. The resulting covariance matrix fulfils the practical constraints, i.e. positive semi‐definiteness and the uniform elemental power constraint. It also provides performance similar to that of iterative methods, while requires lower computation time and provides better mean square error with respect to other existing closed‐form methods. Eigenvalue decomposition is also utilised to convert the possible resulted pseudo‐covariance matrices (pseudo‐CM), which are not guaranteed to be positive semidefinite, into a covariance matrix. Simulation results show the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The windings of the power transformers are prone to deformation or displacement as a result of huge electromagnetic forces. Regular monitoring of transformers for detecting minor winding movements is crucial for preventing them from developing into major faults and eventually, causing an outage. In this study, a novel procedure is proposed to determine the location and severity of the disk space variation, a frequently occurring mechanical damage in transformers, by developing a two‐level method. At the first level, an accurate model of the winding is determined from sweep frequency response analysis measured data corresponding to the healthy status. Analysis and interpretation of the obtained results by the proposed fault detector algorithm lead to locating the position and severity of the fault, in the next level. The process of determining the winding model from the frequency response analysis data is achieved by a multistage method utilising an enhanced version of magnetic optimisation algorithm, which comprises of utilising Fourier series coefficients and phase functions, and fast calculation of coupled network matrices. Experimental results of two 64 kV transformer windings verify the effectiveness of the proposed method in detecting the winding faults. [ABSTRACT FROM AUTHOR]