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In this research, an ensemble classifier using a voting method is proposed to categorize different Power Quality events (PQEs), switching, and fault transients in solar photovoltaic (PV) connected Microgrid (MG) network. For this analysis, an islanded MG network model is developed in the MATLAB-Simulink software framework to study the various power system disturbances (PQEs, switching transients, and Line to Ground (LG) fault). The voltage/current signals of various power disturbances are used for the extraction of features by applying a discrete wavelet transform technique. Then, the individual base classifiers (Bayesian Net, Naive Bayesian, and Logistic Regression) are trained by using the extracted features in the first stage. Subsequently, the voting method of Meta classification is applied to obtain the final predictions of class events in the second level, based on the predictions of base classifiers. The classification and statistical analysis are carried out under standard test conditions (STC) and real-time-varying solar irradiance of PV. The evaluation results of classification and statistical analysis clearly show that the proposed ensemble classifier offers improved classification accuracy with superior performance than individual base classifiers under STC and uncertain conditions of solar PV.

This paper presents the design and analysis of a single-phase power factor correction (PFC) scheme using a DC–DC Zeta converter operating in continuous conduction mode. The presented DC–DC converter exhibits non-linearity due to switching elements that degrades the performance of the converter. Therefore, this paper proposes a non-linear cascade control of particle swarm optimization-gravitational search algorithm (PSO-GSA) tuned proportional-integral (PI) and sliding mode controller (SMC) in outer and inner loop, respectively, to improve the performance of the converter. The robustness of the controller is analysed by applying the load and line variations to the converter. The simulation results showed that the steady state performance indices such as integral absolute error and integral square error, and transient performance indices like settling time and peak overshoot are significantly enhanced for the proposed cascade control scheme compared to the PSO and root locus method of tuning the outer PI with the inner SMC controller. The proposed control scheme gives better performance in terms of power factor improvement, percentage reduction in total harmonic distortion, and efficient tracking of output voltage for change in reference voltage compared to other techniques presented. Further, the performance of the proposed scheme is tested for battery charging application of the solar photovoltaic system. The demonstrated simulation results are validated by implementing the DC–DC Zeta converter in real-time hardware set-up.

In this work, the design and implementation of an optimized fractional-order proportional-integral (FOPI) controller for a DC–DC single-ended primary inductance converter (SEPIC) is considered to fulfill the application of power factor correction and voltage regulation along with improved robustness, efficiency, and performance characteristics of the converter. An Atom Search Optimization technique is proposed to optimize the parameters of the controller in the outer voltage and inner current control loop of the DC–DC SEPIC model. The model is developed using MATLAB –Simulink, and the simulation analysis is carried out at rated load, setpoint tracking, source voltage, and load variation. The effectiveness of the proposed atom search optimized FOPI controller is verified by comparing its performance with the particle swarm optimized FOPI controller. Furthermore, Matignon’s theorem-based closed-loop stability analysis is carried out for the tuned values of the FOPI controller with the SEPIC model. According to the simulation results, the proposed technique provides a more dominant dynamic response, an improved efficiency, and a power factor than the particle swarm-tuned controller of the SEPIC model. In addition, better voltage regulation is achieved in the case of source voltage and load variation. Furthermore, the closed-loop stability analysis reveals that the proposed Atom Search Optimization-tuned FOPI controller performance is more stable than the particle swarm optimization method. The hardware-in-the-loop implementation is carried out to validate the proposed controller in real-time.

Microgrid (MG) networks have evolved as reliable power source for providing secure, reliable, and low carbon emission of energy supply to the remote communities. Power quality disturbance (PQD) is a common issue affecting the performance of the MG network and hindering its usage in small scale. PQD tends to lessen the reliability, performance, and lifecycle of the various power devices in the network. Hence, in this study, a probabilistic based intelligence method has been proposed to detect and classify the PQDs more accurately in the MG network. MG system has been developed using built in features available in the Matlab/Simulink platform. Discrete Wavelet Transform (DWT) based signal processing technique has been applied to extract the features from the multiple PQD signals. The obtained features are used to train the computational intelligent based classifiers such as Multi-Layer Perceptron (MLP) neural network, Support Vector Machine (SVM), and Naive Bayes (NB). The results obtained indicate the proffered NB and SVM classifier could predict PQDs in the MG network with 100% classification accuracy while the MLP gives the classification accuracy of 66.7%. Further, the robustness of classifiers is evaluated using performance indices (PI) of Kappa statistic, mean absolute error and root mean square error. From the PI evaluation, it can be concluded that the probabilistic based NB approach gives the predominated result compared to SVM and MLP method.