Control Of Boost Converter Research Articles

Design and PIL Implementation of Fuzzy Logic-based MPPT Control for Symmetrical Multilevel Boost Converter

This paper aims to introduce a fuzzy logic adaptive MPPT controller to control a symmetrical multilevel converter in a standalone PV system. The system is evaluated under fixed and variable solar radiation with 15 V as the input voltage 60 V as the required output voltage and 50 kHz as the switching frequency. Results prove that by using the controller, the system successfully tracks the MPPT point for constant and variable radiation without oscillation around the maximum power point. In addition, the overshoot and time response is reduced while the voltage ripples are eliminated. The proposed controller is verified through practical implementation in Arduino mega board to test the accuracy of results. The practical finding via a processor in the loop test validates the simulation results.

Extreme learning machine based on BDE feature selection to detect fault scenarios in grid-connected PV systems under MPPT mode

This paper considers real-time data-driven adaptive fault detection (FD) in grid-connected PV (GPV) systems under maximum power point tracking (MPPT) modes during large variations. Faults under MPPT modes remain undetected for longer periods, introducing new protection challenges and threats to the system. An intelligent FD algorithm is developed through real-time multi-sensor measurements and virtual Micro Phasor Measurement Unit (Micro-PMU) estimations. The high-dimensional and high-frequency multivariate features vary over time, and computational efficiency becomes crucial to realizing online adaptive FD. The goal of this study is to present an artificial intelligence (AI) technique for detecting seven faults: inverter fault, feedback sensor fault, grid anomaly, nonhomogeneous partial shading, open circuit in PV array, MPPT controller fault, and boost converter controller fault. In this work, it was found that the application of Extreme Learning Machine (ELM) plays an important role in fault detection and localization. Nine (9) statistical features and eight (8) wavelet packet parameters are extracted from the data based on multiple default values. These features were used as an input vector to train and test the ELM and determine whether the system is operating under normal conditions or is faulty. The BDE feature selection algorithm is adopted to optimize the seven-fault classification procedure to reduce the number of features. The results showed that the Extreme Learning Machine (ELM), based on statistical parameters followed by BDE, can detect faults with high accuracy (98.3%) compared to a case without optimization.

Modelling and Analysis of Adaptive ON-Time Controlled Boost Converter Using Verilog-A

ABSTRACT This paper presents a mixed modelling of adaptive ON-time controlled boost converter using Verilog-A. It is a mixture of Mathematical modelling, Behavioural modelling, and discrete components. While mathematical modelling has been used to model individual components, for circuits such as comparator and pulse generator behavioural modelling is used. Power MOSFETs in the power stage are the only discrete components used from the library. Each model was coded using Verilog-A in Cadence Virtuoso and its symbolic notation obtained. The symbols were then connected to obtain the complete schematic of the boost converter. The behavioural modelling of the boost converter is targeted for an output voltage of 1.2 V for an input voltage ranging from 0.5 to 0.7 V and switching frequency of 800 KHz. The modelling also captures the results for 40% variation in input and load, for which the output voltage is found to vary by about 5–6%. This work is an attempt at completely modelling a complex analog circuit. It also shows the successful integration of modelled components with discrete components. To validate the results obtained, the performance of the modelled converter is compared with that of circuit simulation, with both simulations being conducted at identical operating conditions. The results obtained with modelling simulation match that obtained with the circuit simulation.

Proximal policy optimization-based reinforcement learning approach for DC-DC boost converter control: A comparative evaluation against traditional control techniques

This article proposes a proximal policy optimization (PPO)-based reinforcement learning (RL) approach for DC-DC boost converter control that is compared with traditional control methods. The performance of the PPO algorithm is evaluated using MATLAB Simulink co-simulation, and the results demonstrate that the most efficient approach for achieving short settling time and stability is to combine the PPO algorithm with a reinforcement learning-based control method. The simulation results show that the control method based on RL with the PPO algorithm provides step response characteristics that outperform traditional control approaches, thereby enhancing DC-DC boost converter control. This research also highlights the inherent capability of the reinforcement learning method to enhance the performance of boost converter control.

A Comparative Investigation of Hybrid MPPT Methods for Enhancing Solar Power Generation in Renewable Energy Systems

Photovoltaic (PV) systems are among the types of renewable energy that are frequently employed. Since the characteristics of the solar cell depend on the amount of insolation and temperature, it is necessary to use MPPT “Maximum Power Point Tracking” to move the operating voltage close to the maximum power point under changing weather conditions. This article aims to design a photovoltaic energy system based on boost converter control to obtain maximum power using a hybrid algorithm based on artificial neurons (ANN). Additionally included is a proportional-integral (PI) controller, which improves the performance of the ANN-MPPT controller; this method is quick and precise for tracking the maximum power point (MPP) in the face of variations in temperature and solar radiation. The efficiency of the tracking algorithm was calculated and compared to one of the traditional methods, the incremental conductance (INC) method, in addition to comparing it to other hybrid methods and by simulating the system using MATLAB/Simulation and analyzing the results. This study unequivocally proves the superiority of the hybrid ANN+PI strategy; the efficiency reached 99.91%. This approach excels at tracking maximum power accuracy by leveraging the adaptive learning capabilities of neural networks, ensuring maximum power even in the face of changing environmental conditions.

A Unified Controller Design Method for Multiple Control Structure with Perspective to Parametric Independent Control of Boost Converter

A Unified Controller Design Method for Multiple Control Structure with Perspective to Parametric Independent Control of Boost Converter

Simulation of AC-DC Buck Boost Converter Control Based on Fuzzy Logic

Simulation of AC-DC Buck Boost Converter Control Based on Fuzzy Logic

Experimental Verification of Two Degree-of-Freedom Control Using Nonlinear Noncausal Feedforward Control for Boost Converters

Experimental Verification of Two Degree-of-Freedom Control Using Nonlinear Noncausal Feedforward Control for Boost Converters

Dynamic evolution control for the DC/DC boost converter design and implementation

This paper introduces the design and hardware implementation of the dynamic evolution control for a DC/DC boost converter. The development of the controller aims to effectively regulate the output voltage of the DC/DC boost converter with a high degree of precision. A comprehensive examination of the duty cycle formula for the boost converter is conducted using a non-linear analysis approach. This study introduces a methodology for the synthesis of a converter controller utilizing the principles of dynamic evolution control theory. The converter's output consistently aligns with the target voltage by following the dynamic evolution path determined by the designed controller. The performance evaluation of the proposed boost converter controller utilizing dynamic evolution control is being validated through the utilization of MATLAB/Simulink simulation software. The effectiveness of the proposed controller is further validated through the presentation of hardware results. The performance of the controller was evaluated by varying the values of parameters k and m.

Fast Terminal Sliding Mode Control of DC–DC Boost Converters With Enhanced Disturbance Rejection

Fast Terminal Sliding Mode Control of DC–DC Boost Converters With Enhanced Disturbance Rejection

Continuous Practical Terminal Sliding Mode Controller for Boost Converters: Design and Experimental Evaluation

Continuous Practical Terminal Sliding Mode Controller for Boost Converters: Design and Experimental Evaluation

Output Voltage Precise Tracking Control for Boost Converters Based on Noncausal and Nonlinear Feedforward Control

Output Voltage Precise Tracking Control for Boost Converters Based on Noncausal and Nonlinear Feedforward Control

Stability Improvement for One Cycle Controlled Boost Converters Based on Energy Balance Principle

Stability Improvement for One Cycle Controlled Boost Converters Based on Energy Balance Principle

A Comparison of Type-1 and Type-2 Fuzzy Logic Controller for Full Bridge Boost Converter on DC Microgrid System

A Comparison of Type-1 and Type-2 Fuzzy Logic Controller for Full Bridge Boost Converter on DC Microgrid System

Performance analysis of PID controller and fuzzy logic controller for DC-DC boost converter.

Demand of Power electronics converter is increased due to the rapid increase of green re-sources. The distributed nature of uncontrolled renewable energy resources are connected with the grid via power electronics converter. DC-DC converters are high-frequency current conversion circuits that use transformers, inductors, and capacitors to smooth switching noise into regulated DC voltages. In this study, a comparative performance analysis of Proportional integral Derivative (PID) and fuzzy logic controller (FLC) for DC-DC Boost Converter is presented. PID-based controller for dynamic boosting of DC-DC boost converter was replaced by Fuzzy Inference system (FIS) to study the system behavior. Five different membership functions were performed to observe the response of the system for DC -DC boost converter with FIS controller. The performances of FIS-based controller and PID-based controller for DC-DC boost converter were compared in detail. Also, the proposed techniques for the switching mode of DC-DC converter have been applied. The performance analysis of both PID and fuzzy controller was simulated using MAT LAB. The results of FIS based approach are better than PID based controller in terms of transient responses.

Low-Voltage Self-Operating MPPT Controlled Boost Converter for Thermoelectric Power Generator

Low-Voltage Self-Operating MPPT Controlled Boost Converter for Thermoelectric Power Generator

Nonlinear Cascaded Control for a DC-DC Boost Converter

The Boost Converter is a type of DC-DC converter that operates using switching techniques and is designed to elevate the voltage level. This paper presents a cascaded control for a boost converter to ensure that the inductor current and output capacitor voltage remain in a safe operating zone. Ensuring safe operating conditions and stable closed-loop poles is crucial because it guarantees that both current and voltage remain within the designated operating range. This preventive measure prevents any damage to components like capacitors (C), inductors (L), and switches. Unstable operation, on the other hand, could lead to oscillations and an undesirable increase in the amplitude of current and voltage, posing a risk to all components involved. The research contribution involves an investigation of cascaded control, utilizing power and energy concepts due to their advantageous effects on system performance and design. By implementing nonlinear controllers based on a large-signal averaged model, the closed-loop poles remain independent of operating points, eliminating the need for small-signal linearization. Small-signal linearization makes the controlled system dependent on the operating point. Two controllers are introduced based on power and energy concept, which is easy to understand. The potential practical application of the proposed cascaded control approach is in high-power applications. Tracking the energy stored in the output capacitor is first investigated to validate the proposed control scheme by varying the output voltage reference from 32 V to 50 V. Then, the regulation of the energy voltage is explored by varying the load resistance for the output voltage at 50 V. Both are done using a switched model using MATLAB/Simulink software. Simulation results are given to demonstrate the effectiveness of the proposed method. The key metrics used to assess the effectiveness of the proposed control scheme are the undershoot voltage and robustness. The results show that the studied system's tracking, regulating operations and robustness properties are as expected. The proposed method faces a challenge with the number of sensors required. To address this, observers can be utilized to reduce sensor usage while maintaining measurement accuracy. The proposed method can be applied to other power electronic systems.

Control of a lithium-ion battery interfacing input-voltage-controlled boost converter with virtual impedance compensation technique

Lithium-ion batteries are becoming increasingly popular for energy storage in various hybrid energy systems, hybrid ac/dc, micro-grid, e-mobility applications. However, due to the wide battery impedance range, the performance of lithium-ion battery interfacing dc-dc converter is affected, results in complicated task for design of this regulation. As the virtual impedance concept is increasingly used for the control of power electronic systems, this letter introduces virtual impedance into the Lithium-ion Battery interfacing boost converter controller, to reduce the impact of variable inner impedance. The proposed virtual-impedance based control structure, is derived with small-signal model of boost converter, and explained in detail in this letter. Finally, the performance of the strategy is verified on a lithium-ion battery pack equipped dc micro-grid.

Output Voltage Precise Tracking Control for Boost Converters based on Noncausal and Nonlinear Feedforward Control

Boost converters are key components of DC power conversion used for electric mobility and renewable energy applications. In addition to constant voltage control of the output, variable voltage control has been attracting attention in recent years for high-efficiency drive of loads. However, the dynamic characteristics of boost converters exhibit nonlinear and nonminimum phase characteristics. Therefore, the inverse model for feedforward control is unstable, making high-precision voltage trajectory tracking control challenging. This study aims to present a noncausal and nonlinear feedforward controller to compensate for the nonlinear and nonminimum phase characteristics of the boost converter and to achieve perfect tracking control with respect to the output voltage trajectory. This study also establishes a method for identifying circuit parameters and deriving the time length of noncausal control input for practical implementation. The effectiveness of this control method is demonstrated by experiments using a boost converter.

Fuzzy Control of a High-Performance Boost Converter in Discontinuous Conduction Mode and its Application to a Photovoltaic Pumping System

In this work, a fuzzy voltage controller design of a 1 kW high-gain, high-efficiency direct current converter operating in discontinuous conduction mode is developed. In this condition, the design of a conventional controller is more challenging. This converter is part of an autonomous photovoltaic pumping system without batteries consisting of four photovoltaic modules, a variable frequency inverter and an induction motor coupled to a pump. The boost converter is responsible for the voltage elevation of photovoltaic modules to a 311V direct current bus, which must not have oscillations for a good operation of an algorithm to track the maximum power point of the modules. The fuzzy controller was implemented in a digital signal processor device to control the boost converter, producing a response with an overshoot of 5.78%, settling time of 3.8s and zero error in steady state.