Complex Neural Classifiers for Power Quality Data Mining

S.Vidhya,V. Kamaraj 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.4

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

Analysis of Stability and Dynamic Behaviour of Ultra Lift Luo Converter

Raji, J.,Kamaraj, V. The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.5

Ultra Lift Luo Converter (ULC) gained considerable research interest in recent years. The stability analysis of voltage mode and peak current mode controlled ULC in continuous conduction mode is analyzed in this paper. The Eigen value theory is used for the stability analysis of voltage mode controlled ULC. Then to characterize the dynamics of inner current loop, the expressions of closed loop transfer function and loop gain are determined. An algorithm has been developed to analyze the stability of the peak current mode controlled ULC. The theoretical results are correlated with the simulation results obtained using PSIM 9.1(SMARTCTRL 1.0) software. Finally it is proposed to fabricate a prototype and validate the performance by suitable experimental setup.

Analysis of Stability and Dynamic Behaviour of Ultra Lift Luo Converter

J. Raji,V. Kamaraj 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.5

Ultra Lift Luo Converter (ULC) gained considerable research interest in recent years. The stability analysis of voltage mode and peak current mode controlled ULC in continuous conduction mode is analyzed in this paper. The Eigen value theory is used for the stability analysis of voltage mode controlled ULC. Then to characterize the dynamics of inner current loop, the expressions of closed loop transfer function and loop gain are determined. An algorithm has been developed to analyze the stability of the peak current mode controlled ULC. The theoretical results are correlated with the simulation results obtained using PSIM 9.1(SMARTCTRL 1.0) software. Finally it is proposed to fabricate a prototype and validate the performance by suitable experimental setup.

Investigation of ultra-lift Luo-converter with peak, average and hysteresis current-mode control

Raji, J.,Kamaraj, V. The Korean Institute of Power Electronics 2021 JOURNAL OF POWER ELECTRONICS Vol.21 No.6

The Renewable energy system, electric vehicle, and telecommunication applications require relatively stable power converters with a high gain and enhanced noise immunity. A study of three different types of current-mode controllers for high-gain ultra-lift Luo-converter (ULC) is discussed in this paper. The stability of a constant frequency peak current-mode controller (PCM), an average current-mode controller (ACM) and a variable frequency hysteresis current-mode controller (HCM) are analyzed based on small-signal characteristics. Using mathematical modeling of the controllers, the closed-loop transfer functions such as control voltage to output voltage, current loop gain, inductor current to control voltage, and audio susceptibility are obtained. These transfer functions along with MATLAB simulation results for PCM, ACM and HCM controllers are compared. Then, the component losses of the ULC converter using PCM, ACM and HCM controllers are calculated and an efficiency comparison of different control techniques is done. Investigations of the voltage and current stresses of the switch and diodes are also carried out. Finally, a prototype is fabricated to validate the performance of the converter.

Complex Neural Classifiers for Power Quality Data Mining

Vidhya, S.,Kamaraj, V. The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.4

This work investigates the performance of fully complex- valued radial basis function network(FC-RBF) and complex extreme learning machine (CELM) based neural approaches for classification of power quality disturbances. This work engages the use of S-Transform to extract the features relating to single and combined power quality disturbances. The performance of the classifiers are compared with their real valued counterparts namely extreme learning machine(ELM) and support vector machine(SVM) in terms of convergence and classification ability. The results signify the suitability of complex valued classifiers for power quality disturbance classification.

Sensitivity Analysis of Geometrical Parameters of a Switched Reluctance Motor with Modified Pole Shapes

M. Balaji,S. Ramkumar,V. Kamaraj 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.1

A major problem in Switched Reluctance Motor (SRM) is torque ripple, which causes undesirable acoustic noise and vibration. This work focuses on reducing the undesirable torque ripple in SRM by modifying stator and rotor geometry. This paper presents a comparative study on torque ripple minimization in SRM with modified pole shapes such as stator pole taper, stator pole face with non-uniform air gap and pole shoe attached to rotor pole. Further this paper presents a detailed sensitivity analysis of the effect of different geometrical parameters that alter the pole face shapes on the performance of SRM. The analysis is performed using finite-element method considering average torque and torque ripple as performance parameters. Based on the analysis, a design combining stator pole taper with non-uniform air gap is proposed to improve the torque characteristics of SRM. The dynamic characteristics of the proposed design are simulated and the results show satisfactory reduction in torque ripple.

Data Interpolation and Design Optimisation of Brushless DC Motor Using Generalized Regression Neural Network

Umadevi, N.,Balaji, M.,Kamaraj, V.,Padmanaban, L. Ananda The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.1

This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.

Data Interpolation and Design Optimisation of Brushless DC Motor Using Generalized Regression Neural Network

N. Umadevi,M. Balaji,V. Kamaraj,L. Ananda Padmanaban 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.1

This paper proposes a generalized regression neural network (GRNN) based algorithm for data interpolation and design optimization of brushless dc (BLDC) motor. The procedure makes use of magnet length, stator slot opening and air gap length as design variables. Cogging torque and average torque are treated as performance indices. The optimal design necessitates mitigating the cogging torque and maximizing the average torque by varying design variables. The data set for interpolation and ensuing design optimisation using GRNN is obtained by modeling a standard BLDC motor using finite element analysis (FEA) tool MagNet 7.1.1. The performance indices of the standard motor obtained using FEA are validated with an experimental model and an analytical method. The optimal design is authenticated using particle swarm optimization (PSO) algorithm and the performance indices of the optimal design obtained using GRNN is validated using FEA. The results indicate the suitability of GRNN as an interpolation and design optimization tool for a BLDC motor.

Fault Detection and Identification Strategy Based on Luenberger Observer for Bidirectional Interleaved Switched—Capacitor DC–DC Converter Interfaced Microgrids

Nalina B. S.,Kamaraj V.,Babu M. Ramesh 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.4

Faults in power electronic converters interfaced to renewable energy systems are caused due to malfunction of system components, short circuits in converters, commutation failures. These faults aff ect the performance of the system. Therefore, to improve the performance of the system, these faults have to be detected and identifi ed. In this paper, fault detection and identifi cation strategy are designed, analyzed both mathematically and experimentally based on the design of Luenberger observer. Two diff erent types of fi lters are incorporated in the system which includes Fault Detection (FD) fi lter and Fault Identifi cation (FI) fi lters. The design structures of both the fi lters are based on the design of Luenberger observer. Initially, the fault detection fi lters are activated to detect the availability of fault. Once the fault is identifi ed, a set of fault identifi cation fi lters are initialized to identify the type of fault. Each type of fault is determined by each fi lter in the set of fault identifi cation fi lters. In this paper inductor fault, capacitor fault and switch fault are analyzed. The proposed strategy is verifi ed on an Interleaved Bidirectional Switched Capacitor DC–DC converter interfaced to a solar powered Microgrid using theoretical and simulation results. Finally, a prototype is fabricated to validate the performance of the system.

Sensitivity Analysis of Geometrical Parameters of a Switched Reluctance Motor with Modified Pole Shapes

Balaji, M.,Ramkumar, S.,Kamaraj, V. The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.1

A major problem in Switched Reluctance Motor (SRM) is torque ripple, which causes undesirable acoustic noise and vibration. This work focuses on reducing the undesirable torque ripple in SRM by modifying stator and rotor geometry. This paper presents a comparative study on torque ripple minimization in SRM with modified pole shapes such as stator pole taper, stator pole face with non-uniform air gap and pole shoe attached to rotor pole. Further this paper presents a detailed sensitivity analysis of the effect of different geometrical parameters that alter the pole face shapes on the performance of SRM. The analysis is performed using finite-element method considering average torque and torque ripple as performance parameters. Based on the analysis, a design combining stator pole taper with non-uniform air gap is proposed to improve the torque characteristics of SRM. The dynamic characteristics of the proposed design are simulated and the results show satisfactory reduction in torque ripple.