Buck-Boost Converter Small Signal Model: Dynamic Analysis under System Uncertainties

An accurate mathematical model of DC-DC converters is an imperative for high performance in all domains of electronic systems operations. In this work, a small signal circuit model for DC-DC buck-boost converter operated in continuous conduction mode (CCM) is developed. The proposed modeling is initialized from dynamic equations illustrating the converter. The nonlinear behaviour of the pulse-width modulation and switching process are addressed via the application of waveform averaging and small-signal modeling techniques. A complete MATLAB/Simulink model is designed to check the robustness of the proposed converter under different input voltage and switched load variations. Simulation results present the superiority of proposed model in terms of transient and steady-state performance, such as small overshoot and short settling time. Furthermore, the proposed model can be useful to achieve input and output impedances, inductor current variations, and converter transfer functions to develop a robust closed-loop controller design that can meet stability and performance conditions of the DC-DC buck-boost converter

Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid

In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower-top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response

Advances and perspectives on solid oxide fuel cells : From nanotechnology to power electronics devices

Solid oxide fuel cells (SOFCs) hold an important place in energy conversion and storage systems due to their fuel flexibility, high efficiency, and environmental sustainability. The scorching temperature (≥800 °C) to operate SOFCs results in shorter life span due to rapid deterioration of accompanying components. Nanomaterials have attained considerable attention in recent years due to their great technological importance in fuel cell technology. Nanoengineering of the architectures of known materials and adopting composite approach can effectively enhance the active sites for electrode reactions. The use of nanotechnology will make SOFCs environment friendly and sustainable through green manufacturing processes of nanotechnology. Overviews of the contributions of nanotechnology and power electronics technologies to SOFCs, the transition of SOFCs from macro- to nanotechnology, the significance of nanomaterials in SOFCs, dynamic modeling, the function of optimization techniques, and the requirement for power electronics converters in SOFCs are all provided in this piece of work. The applications of SOFCs in different sectors, prominent institutes/labs and companies involved in SOFCs’ research, future challenges, and perspectives are also highlighted.© 2023 The Authors. Energy Technology published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed

Special Issue “Applications of Advanced Control and Optimization Paradigms in Renewable Energy Systems”

The increasing environmental damage caused by adversarial factors, a growing need for energy, the continued reliance on fossil fuels, which comes with rising costs, and the global push for net-zero emissions targets have drawn significant focus on the global promotion of renewable energy sources [...

Load Sharing and Arrangement through an Effective Utilization of SOFC/Super-capacitor/Battery in a Hybrid Power System

Solid oxide fuel cell (SOFC) provides several benefits such as high efficiency, modularity, quiet operation and cogeneration alternatives. Nevertheless, the main weakness in SOFC-based power plant has the slow dynamic response during transient situations in peak demand since this problem can be addressed by using complementary system such as a super-capacitor (SC). This paper provides an optimal load sharing and arrangement strategy (LSAS) for a hybrid power system which combines a SOFC stack, a SC module and a battery bank that support local grid. According to LSAS, the SOFC is the primary power source. The SC module is utilized as a backup and complement device to take care of the load following problems of SOFC during transient. The battery bank is added as a high energy density and/or backup device to stabilize the DC bus voltage, while an electrolyzer (ELYZ) is used as a dump load during surplus power. The LSAS operates in two layers. The external layer accomplished the overall power management system. Depending upon load demand, this layer generates references to the internal layer. The internal layer controls the individual subsystems, i.e., SOFC, ELYZ, SC and battery according to the references coming from the external layer. A complete MATLAB/Simulink model has been established to check the performance of the proposed system for the real load conditions. Simulation results show the effectiveness of the proposed system in terms of power transfer, load tracking and grid stability

Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System

In the current smart grid scenario, the evolution of a proficient and robust maximum power point tracking (MPPT) algorithm for a PV subsystem has become imperative due to the fluctuating meteorological conditions. In this paper, an adaptive feedback linearization-based NeuroFuzzy MPPT (AFBLNF-MPPT) algorithm for a photovoltaic (PV) subsystem in a grid-integrated hybrid renewable energy system (HRES) is proposed. The performance of the stated (AFBLNF-MPPT) control strategy is approved through a comprehensive grid-tied HRES test-bed established in MATLAB/Simulink. It outperforms the incremental conductance (IC) based adaptive indirect NeuroFuzzy (IC-AIndir-NF) control scheme, IC-based adaptive direct NeuroFuzzy (IC-ADir-NF) control system, IC-based adaptive proportional-integral-derivative (IC-AdapPID) control scheme, and conventional IC algorithm for a PV subsystem in both transient as well as steady-state modes for varying temperature and irradiance profiles. The comparative analyses were carried out on the basis of performance indexes and efficiency of MPPT