This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, a...
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This paper deals with the problem regarding the optimal placement and sizing of distribution static compensators (D-STATCOMs) in radial and meshed distribution networks. These grids consider industrial, residential, and commercial loads within a daily operation scenario. The optimal reactive power flow compensation problem is formulated through a mixed-integer nonlinear programming (MINLP) model. The objective function is associated with the minimization of the expected energy losses costs for a year of operation by considering the investment costs of D-STATCOMs. To solve the MINLP model, the application of a master-slave optimization approach is proposed, which combines the salpswarmalgorithm (SSA) in the master stage and the matricial backward/forward power flow method in the slave stage. The master stage is entrusted with defining the optimal nodal location and sizes of the D-STATCOMs, while the slave stage deals with the power flow solution to determine the expected annual energy losses costs for each combination of nodes and sizes for the D-STATCOMs as provided by the SSA. To validate the effectiveness of the proposed master-slave optimizer, the IEEE 33-bus grid was selected as a test feeder. Numerical comparisons were made against the exact solution of the MINLP model with different solvers in the general algebraic modeling system (GAMS) software. All the simulations of the master-slave approach were implemented in the MATLAB programming environment (version 2021b). Numerical results showed that the SSA can provide multiple possible solutions for the studied problem, with small variations in the final objective function, which makes the proposed approach an efficient tool for decision-making in distribution companies.
Recent trend indicates that the assessment of low complexity is crucial for an effectual design of digital filter. Earlier the existing algorithms solely focused on reducing the pass band ripples and the stop band rip...
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Recent trend indicates that the assessment of low complexity is crucial for an effectual design of digital filter. Earlier the existing algorithms solely focused on reducing the pass band ripples and the stop band ripples in spite of the requirement of low implementation complexity in finite impulse response (FIR) filter design. In this regard, the optimization-based FIR filter using multiobjective improved salp swarm optimization algorithm have been designed. The proposed method not only minimizes the ripples of pass band and stop band but also minimizes the transition band, number of sparsity, which is used to implement a FIR filter that abides the specified frequency. The nondominated solution of filter design has a set of an optimum solution as pareto front that maintains the trade-off between the various specifications. The designed FIR filter has been compared with other reported method to validate the applicability of the proposed method. The evaluation and analysis have been performed regrading minimum pass band ripples, minimum stop band ripple, minimum transition band, and implementation complexity. To prove the practical applicability of the proposed method, the designed FIR filters have been implemented in Xilinx ISE14.7 (Virtex-VII) environment. The comparison result shows that the proposed method performs better than all other reported method in achieving minimum ripples and implementation complexity.
Data analysis in medicine is becoming more and more frequent to clarify diagnoses, refine research methods, and plan appropriate equipment supplies according to the importance of the pathologies that appear. Artificia...
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Data analysis in medicine is becoming more and more frequent to clarify diagnoses, refine research methods, and plan appropriate equipment supplies according to the importance of the pathologies that appear. Artificial intelligence offers software solutions that are required to analyze the present data for optimal prediction of results. A system model is capable of several data processing algorithms for the classification of heart disease. This research work is particularly interested in the category of data. The classification allows us to obtain a prediction model from training data and test data. These data are screened by a classification algorithm that produces a new model capable of detailed data, possibly having the same classes of data through the combination of mathematical tools and computer methods. To analyze the present data to predict optimal results, we need to use the optimization technique. This research work aims to design a framework for heart disease prediction by using major risk factors based on different classifier algorithms such as Naïve Bayes (NB), Bayesian Optimized Support Vector Machine (BO-SVM), K-Nearest Neighbors (KNN), and salpswarm Optimized Neural Network (SSA-NN). This research is carried out for the effective diagnosis of heart disease using the heart disease dataset available on the UCI Machine Repository. The highest performance was obtained using BO-SVM (accuracy = 93.3%, precision = 100%, sensitivity = 80%) followed by SSA-NN with (accuracy = 86.7%, precision = 100%, sensitivity = 60%) respectively. The results reveal that the proposed novel optimized algorithm can provide an effective healthcare monitoring system for the early prediction of heart disease.
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