IC Pattern Based Power Factor Maximization Model for Improved Power Stabilization

The voltagefluctuation in electric circuits has been identified as key issue in different electric systems.As the usage of electricity growing in rapid way,there exist higherfluctuations in powerflow.To maintain theflow or stabi-lity of power in any electric circuit,there are many circuit models are discussed in literature.However,they suffer to maintain the output voltage and not capable of maintaining power stability.To improve the performance in power stabilization,an efficient IC pattern based power factor maximization model(ICPFMM)in this article.The model is focused on improving the power stability with the use of IC(Inductor and Conductor)towards identifying most efficient circuit for the current duty cycle according to the input voltage,voltage in capacitor and output voltage required.The model with boost converter diverts the incoming voltage through number of conductors and inductors.By triggering specific inductor,a specific capacitor gets charged and a particular circuit gets on.The model maintains num-ber of IC(Inductor and Conductor)patterns through which the powerflow occurs.According to that,the pattern available,the mofset controls the level of power to be regulated through any circuit.From the pattern,the model computes the Cir-cuits Switching Loss and Circuits Conduction Loss for various circuits.Accord-ing to the input voltage,the model estimates Circuit Power Stabilization Support(CPSS)according to the voltage available in any capacitor and input voltage.Using the value of CPSS,the model trigger optimal number of circuits to maintain voltage stability.In this approach,more than one circuit has been triggered to maintain output voltage and to get charged.The proposed model not only main-tains power stability but also reduces the wastage in voltage which is not utilized.The proposed model improves the performance in voltage stability with less switching loss.

Speech Separation Methodology for Hearing Aid

In the design of hearing aids(HA),the real-time speech-enhancement is done.The digital hearing aids should provide high signal-to-noise ratio,gain improvement and should eliminate feedback.In generic hearing aids the perfor-mance towards different frequencies varies and non uniform.Existing noise can-cellation and speech separation methods drops the voice magnitude under the noise environment.The performance of the HA for frequency response is non uni-form.Existing noise suppression methods reduce the required signal strength also.So,the performance of uniform sub band analysis is poor when hearing aid is con-cern.In this paper,a speech separation method using Non-negative Matrix Fac-torization(NMF)algorithm is proposed for wavelet decomposition.The Proposed non-uniformfilter-bank was validated by parameters like band power,Signal-to-noise ratio(SNR),Mean Square Error(MSE),Signal to Noise and Dis-tortion Ratio(SINAD),Spurious-free dynamic range(SFDR),error and time.The speech recordings before and after separation was evaluated for quality using objective speech quality measures International Telecommunication Union-Telecommunication standard ITU-T P.862.

Deep Learning-Based FOPID Controller for Cascaded DC-DC Converters

Smart grids and their technologies transform the traditional electric grids to assure safe,secure,cost-effective,and reliable power transmission.Non-linear phenomena in power systems,such as voltage collapse and oscillatory phenomena,can be investigated by chaos theory.Recently,renewable energy resources,such as wind turbines,and solar photovoltaic(PV)arrays,have been widely used for electric power generation.The design of the controller for the direct Current(DC)converter in a PV system is performed based on the linearized model at an appropriate operating point.However,these operating points are everchanging in a PV system,and the design of the controller is usually accomplished based on a low irradiance level.This study designs a fractional-order proportional-integrated-derivative(FOPID)controller using deep learning(DL)with quasi-oppositional Archimedes Optimization algorithm(FOPID-QOAOA)for cascaded DC-DC converters in micro-grid applications.The presented FOPIDQOAOA model is designed to enhance the overall efficiency of the cascaded DC-DC boost converter.In addition,the proposed model develops a FOPID controller using a stacked sparse autoencoder(SSAE)model to regulate the converter output voltage.To tune the hyper-parameters related to the SSAE model,the QOAOA is derived by the including of the quasi-oppositional based learning(QOBL)with traditional AOA.Moreover,an objective function with the including of the integral of time multiplied by squared error(ITSE)is considered in this study.For validating the efficiency of the FOPID-QOAOA method,a sequence of simulations was performed under distinct aspects.A comparative study on cascaded buck and boost converters is carried out to authenticate the effectiveness and performance of the designed techniques.