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Rotor angle stability, which involves electromechanical oscillation damping and control, is very important in maintaining the stability of modern power grid systems. Renewable energy sources like wind energy are undergoing massive integration into modern power grid systems to meet energy demands and decarbonize power grid systems of carbon emissions from fossil fuel generators. To enable increased integration of wind renewable energy sources, precise models are needed for research and analytical purposes. Wind renewable energy is generated through a wind energy conversion system (WECS); one such conversion system is the doubly fed induction generator (DFIG) system. In this study, a precise model of a DFIG-WECS was modeled and integrated into the IEEE's two-area Kundur power test system, which represents the available power grid system, and is also a multimachine power system using the Matlab/Simulink 2023 software. A damping controller known as the power system stabilizer (PSS), whose optimal parameters were obtained using artificial eco-system optimization (AEO), was also incorporated into the integrated power grid system to control and damp electromechanical oscillations. The results showed that the PSS damping controller effectively damped electromechanical oscillations in the integrated power grid system. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]