Backstepping Control of a Buck-Boost Converter in an Experimental PV-System

Vázquez, Jesús R.; Martín, Aránzazu D.

doi:10.6113/jpe.2015.15.6.1584

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…For that, an MPPT algorithm is required. In this case, it includes a regression plane [26] in which all the MPPs are obtained for different values of temperature and irradiance (providing the theoretical reference voltage V ref,t ) and a modified P&O [26], which provides an incremental value of the reference voltage, ∆V ref,p , where the MPP is located. Both the regression plane and the modified P&O are used to calculate the reference voltage to be reached by the adaptive Lyapunov controller, V PV,ref , to obtain the MPP.…”

Section: Mpptmentioning

confidence: 99%

Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System

et al. 2021

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The design, monitoring, and control of photovoltaic (PV) systems are complex tasks that are often handled together, and they are made even more difficult by introducing features such as real-time, sensor-based operation, wireless communication, and multiple sensor nodes. This paper proposes an integrated approach to handle these tasks, in order to achieve a system efficient in tracking the maximum power and injecting the energy from the PV modules to the grid in the correct way. Control is performed by means of an adaptive Lyapunov maximum power point tracking (MPPT) algorithm for the DC/DC converters and a proportional integral (PI) control for the inverters, which are applied to the system using low latency wireless technology. The system solution exploits a low-cost wireless multi-sensor architecture installed in each DC/DC converter and in each inverter and equipped with voltage, current, irradiance, and temperature sensors. A host node provides effective control, management, and coordination of two relatively independent wireless sensor systems. Experimental validation shows that the controllers ensure maximum power transfer to the grid, injecting low harmonic distortion current, thus guaranteeing the robustness and stability of the system. The results verified that the MPPT efficiency is over 99%, even under perturbations and using wireless communication. Moreover, the converters’ efficiency remains high, i.e., for the DC/DC converter a mean value of 95.5% and for the inverter 93.3%.

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Section: Mpptmentioning

confidence: 99%

Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System

et al. 2021

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“…where k c is chosen to be positive. The sign of the sliding surface is defined as shown in Equation (18).…”

Section: Sliding Controlmentioning

confidence: 99%

“…These algorithms calculate the duty cycle of the DC/DC converter to make the modules voltage follow the MPPV previously calculated. Among these algorithms, the back-stepping, the sliding mode control (SMC), or others are considered, [16][17][18][19]. The use of one of these algorithms ensures an appropriate performance in the working range, operating in all the bandwidths of the system and guaranteeing global stability.…”

Section: Introductionmentioning

confidence: 99%

Wireless Sliding MPPT Control of Photovoltaic Systems in Distributed Generation Systems

et al. 2019

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The aim of a photovoltaic (PV) system’s control is the extraction of the maximum power even if the irradiance, the temperature, or the parameters vary. To do that, a maximum power point tracking (MPPT) algorithm is required. In this work, a sliding control is designed to regulate the PV modules’ output voltage and make the panel work at the maximum power voltage. This control is selected to improve the robustness, the transient dynamic response, and the time response of the system under changeable environmental conditions, adjusting the duty cycle of the DC/DC converter. The DC/DC converter connected to the PV module output is a buck-boost converter. This configuration presents the advantage of providing voltages lower or higher than supplied by the photovoltaic modules to provide the required voltage to the load (including the voltages ceded by telecommunication loads, amongst others). In addition, a remote sliding control is developed to make the global supervision of the PV system in distributed generation grids. The designed algorithm is tested in an experimental platform, both locally and remotely connected to the base station, to prove the effectiveness of the sliding control. Thus, the communication effect in the control is also analyzed.

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“…Moreover, LQR performance is prone to be degraded by the modeling errors as well. Other mentionable modern control schemes have been rigorously discussed in previous studies …”

Section: Introductionmentioning

confidence: 99%

Performance optimization of LQR‐based PID controller for DC‐DC buck converter via iterative‐learning‐tuning of state‐weighting matrix

Saleem

Rizwan

2019

Int J Numerical Modelling

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This paper presents a numerically optimized linear-quadratic-regulator-based tuning mechanism for a ubiquitous proportional-integral-derivative controller to improve the output-voltage regulation capability of a direct-current (DC)-DC buck converter. The linear-quadratic-regulator minimizes the quadratic cost of variations in the control signal and error-dynamics of output-voltage to provide a trivial set of optimized proportional-integral-derivative controller gains in the form of the state-feedback gain vector. In order to further improve the controller's time-domain performance and its disturbance-rejection capability against load-transients and input-fluctuations, an iterative-learningtuning mechanism is adopted to optimize the state-weighting matrix of the linear-quadratic cost function. The proposed optimization mechanism iteratively converges in the direction of the steepest gradient-descent of another performance index that directly captures the transient response characteristics, and thus, optimally selects the weighting matrix to achieve the desired natural frequency and damping ratio of the closed-loop system. Credible hardware-inthe-loop experiments are conducted on a low-power DC-DC buck converter circuit to validate the aforementioned propositions.A lot of research has been done to devise robust and optimal voltage controllers for effectively regulating the v o of the buck converter, despite the exogenous disturbances in load impedance and input voltage. A detailed comparative performance analysis of the conventional and contemporary control techniques has been presented in previous tudies. 4-6 These controllers are divided in two categories, namely, model-free controllers and model-based controllers. The model-free controllers do not depend on the mathematical model of the converter. Hence, they are relatively simpler to construct. 7 Although they can significantly improve the transient and steady-state response of the system, they lack optimality in minimizing the deviations in state-trajectories or energy consumption. The proportional-integralderivative (PID) controllers, and their variants, are the most widely used model-free controllers in the industry. 8,9 However, evaluating a trivial set of controller gains to obtain optimal control effort is a cumbersome process. Several autotuning techniques for PID gains have been proposed in the literature to enhance the robustness of the controller design. [10][11][12][13][14] The fuzzy-logic controllers infer the control decision based on a heuristically synthesized set of logical rules. 15,16 However, these rules are empirically defined and hence cannot completely compensate all the nonlinearities associated with the system's dynamics. Other widely used control schemes include the sliding-mode controller, 17,18 fractional-order PID controller, 19 and adaptive controller. [20][21][22] The model-based controllers depend on the mathematical model of the system and thus use the complete knowledge of system dynamics to provide optimal control effort. 23 Se...

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Backstepping Control of a Buck-Boost Converter in an Experimental PV-System

Cited by 8 publications

References 12 publications

Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System

Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System

Wireless Sliding MPPT Control of Photovoltaic Systems in Distributed Generation Systems

Performance optimization of LQR‐based PID controller for DC‐DC buck converter via iterative‐learning‐tuning of state‐weighting matrix

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