Risk based security assessment (RBSA) for power system security deals with the impact and probability of uncertainty to occur in the power system. In this study, the risk of voltage collapse is measured by considering...
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ISBN:
(纸本)9781479979929
Risk based security assessment (RBSA) for power system security deals with the impact and probability of uncertainty to occur in the power system. In this study, the risk of voltage collapse is measured by considering the L-index as the impact of voltage collapse while Poisson probability density function is used to model the probability of transmission line outage. The prediction of voltage collapse risk index in real time requires precise, reliable and short processing time. To facilitate this analysis, Artificial Intelligent using generalize regression neural network (GRNN) technique is proposed where the spread value is determined using Cuckoo Search (CS) optimization method. To validate the effectiveness of the proposed method, the performance of GRNN with optimized spread value obtained using CS is compared with heuristic approach.
In this paper the proper switching of the bidirectional switches found by calculating the duty cycles using Venturini's method 131 in conjunction by employing neuralnetwork presented. So the need for standard sof...
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ISBN:
(纸本)9781467350037;9781467350044
In this paper the proper switching of the bidirectional switches found by calculating the duty cycles using Venturini's method 131 in conjunction by employing neuralnetwork presented. So the need for standard soft switching calculation could be eliminated And the time needed to find modulation matrix using Venturini's method eliminate. A new modulation matrix using generalizes regressionneuralnetwork is calculated for the constant output frequency and voltage magnitude. Simulation results using MATLAB Simulink for inductive and resistive loads are produced and presented. Good agreement between the standard simulations results with the new modulation technique shows the validity of this method
Background: Diverse modeling approaches viz. neuralnetworks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown ...
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Background: Diverse modeling approaches viz. neuralnetworks and multiple regression have been followed to date for disease prediction in plant populations. However, due to their inability to predict value of unknown data points and longer training times, there is need for exploiting new prediction softwares for better understanding of plant-pathogen-environment relationships. Further, there is no online tool available which can help the plant researchers or farmers in timely application of control measures. This paper introduces a new prediction approach based on support vector machines for developing weather-based prediction models of plant diseases. Results: Six significant weather variables were selected as predictor variables. Two series of models (cross-location and cross-year) were developed and validated using a five-fold cross validation procedure. For cross-year models, the conventional multiple regression (REG) approach achieved an average correlation coefficient (r) of 0.50, which increased to 0.60 and percent mean absolute error (% MAE) decreased from 65.42 to 52.24 when back-propagation neuralnetwork (BPNN) was used. With generalized regressionneuralnetwork (GRNN), the r increased to 0.70 and % MAE also improved to 46.30, which further increased to r = 0.77 and % MAE = 36.66 when support vector machine (SVM) based method was used. Similarly, cross-location validation achieved r = 0.48, 0.56 and 0.66 using REG, BPNN and GRNN respectively, with their corresponding % MAE as 77.54, 66.11 and 58.26. The SVM-based method outperformed all the three approaches by further increasing r to 0.74 with improvement in % MAE to 44.12. Overall, this SVM-based prediction approach will open new vistas in the area of forecasting plant diseases of various crops. Conclusion: Our case study demonstrated that SVM is better than existing machine learning techniques and conventional REG approaches in forecasting plant diseases. In this direction, we have also developed a SVM
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