Optimal pulse shifting in timed antenna array for the reduction of sidelobe and sideband using improvedharmonysearch algorithm (IHSA) dealt in this paper. The essence of 'Time-modulation' lies in the fact th...
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Optimal pulse shifting in timed antenna array for the reduction of sidelobe and sideband using improvedharmonysearch algorithm (IHSA) dealt in this paper. The essence of 'Time-modulation' lies in the fact that 'Time' can be used as an additional control parameter in antenna array synthesis. The proposed approach demonstrates the controlling nature of periodic time sequences through pulse shifting. The undesired sideband radiations (SRs) generated in time modulated linear array (TMLA) is controlled by minimizing the sideband levels (SBLs) with an optimal pulse shifting scheme applied to the outer elements of the array. Evolutionary algorithm-based design is considered to optimize the time sequences and the excitation coefficients of the array along with the inter-element spacing between the array elements for sidelobe level (SLL) reduction at the fundamental frequency. Pulse shifting with optimized switch-ON instants and switch-ON time intervals of outer elements is responsible for the minimization of SBL only, as the fundamental pattern does not depend on pulse shifting. Thus, a combined approach is developed with optimized excitation coefficients and controlled pulse switching to reduce the SLL and SBL of TMLA simultaneously. 16 and 30 isotropic elements of TMLA structures are considered with a music-inspired IHSA to get the optimal solution. IHSA based numerical results are compared with the results obtained from other applied algorithms such as harmonysearch algorithm (HSA), particle swarm optimization (PSO), and real-coded genetic algorithm (RGA) with the proposed pulse shifting scheme. The obtained numerical results are also compared with previously published literature results to show the superior performance achieved by the proposed approach.
Accurate prediction of power load plays a crucial role in the power industry and provides economic operation decisions for the power operation department. Due to the unpredictability and periodicity of power load, an ...
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Accurate prediction of power load plays a crucial role in the power industry and provides economic operation decisions for the power operation department. Due to the unpredictability and periodicity of power load, an improved method to deal with complex nonlinear relation was adopted, and a short-term load forecasting model combining FEW (fuzzy exponential weighting) and IHS (improved harmonic search) algorithms was proposed. Firstly, the domain space was defined, the harmony memory base was initialized, and the fuzzy logic relation was identified. Then the optimal interval length was calculated using the training sample data, and local and global optimum were updated by optimization criteria and judging criteria. Finally, the optimized parameters obtained by an IHS algorithm were applied to the FEW model and the load data of the Huludao region (2013) in Northeast China in May. The accuracy of the proposed model was verified using an evaluation criterion as the fitness function. The results of error analysis show that the model can effectively predict short-term power load data and has high stability and accuracy, which provides a reference for application of short-term prediction in other industrial fields.
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