Performance evaluation of a MPPT controller with model predictive control for a photovoltaic system

Pradhan, Roshan; Panda, Aurobinda

doi:10.1080/00207217.2020.1727027

Cited by 9 publications

(6 citation statements)

References 40 publications

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“…Furthermore, the performance of the proposed IT2-TSK-FLC-FA has been compared with IT2-TSK-FLC in all studies. The efficiency of each case is calculated by the following equation 30 :…”

Section: Simulation Resultsmentioning

confidence: 99%

Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Keçecioğlu

2022

Int J Numerical Modelling

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The efficiency is a very important parameter for photovoltaic systems. Positive output elementary super lift Luo converter classified as high gain DC‐DC converter are used in photovoltaic systems because of their advantages such as high efficiency, reduced ripples, and low switching loss. The drawback of the photovoltaic system based on this converter is its sensitivity to small changes on the control signal. Both maximum power point tracking and control signal stability require a powerful control system as this is a challenging task for the photovoltaic system. Interval type‐2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties better than type‐1 fuzzy logic systems. In this work, interval type‐2 Takagi–Sugeno–Kang fuzzy logic controller was first designed as a controller. Then, the controller, which was proposed for this paper, was optimized with the Firefly Algorithm to improve the control performance. Performance of controllers is evaluated via comparative simulation studies under the most demanding environmental conditions. It has been observed that the proposed controller reduces the settling time by more than 50% and increases the efficiency up to 10% according to the simulation situations.

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“…Furthermore, the performance of the proposed IT2-TSK-FLC-FA has been compared with IT2-TSK-FLC in all studies. The efficiency of each case is calculated by the following equation 30 :…”

Section: Simulation Resultsmentioning

confidence: 99%

Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Keçecioğlu

2022

Int J Numerical Modelling

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“…When applying each suboptimal condition, it was waited at least 5 min to take the measurements of the required physical variables, ensuring that the measurement corresponds to the steady-state of the failure. In order to establish this parameter, it was considered that in case of a variation in the PV array conditions (e.g., a failure), the inverter takes time to reach the stability of the new operation point [20], although this time can be less than 1 s, it was decided to leave a longer time.…”

Section: Description For Electrical Measurementsmentioning

confidence: 99%

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

et al. 2022

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This paper compares multiple techniques to detect suboptimal conditions in the PV system. Detection of suboptimal conditions in the PV system is required to achieve optimal photovoltaic (PV) systems. Therefore, maintenance managers need to choose the most suitable techniques objectively. However, there is a lack of objective information comparing the effectiveness of the methods. This article calculates and compares the effectiveness of Infrared thermography (IRT), visual inspection (VI), and electrical analysis (EA) in detecting soiling, partial shadows, and electrical faults experimentally. The results showed that the VI was the best at detecting soiling and partial shading with 100% of effectiveness. IRT and EA had an effectiveness of 78% and 73%, respectively, detecting the three types of conditions under study. It was not possible to achieve maximum detection using only one of the techniques, but that VI must be combined with IR or EA. This research represents a significant contribution by achieving an objective comparison between techniques for detecting suboptimal conditions, being very useful to guide PV system maintainers and designers of fault detection techniques.

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“…An objective function needs to be chosen that captures the control objectives over the finite prediction horizon. in this study one prediction horizon has been applied, which reduces the number of computations to the number of possible switching states of the converter [39]. The variables used for determining the cost function represented as difference between desired current of PV and predicted values .…”

Section: Kalman Filtermentioning

confidence: 99%

“…Some of them have proposed control methods to increase the efficiency of converters such as a PID controller, fuzzy controller, sliding mode controller (SMC) and model predictive controller (MPC) [19][20][21][22][23]. The model predictive controller (MPC) scheme is presented in [24] in order to track the maximum power PV point; the results show that MPC technique gives better performance than conventional methods. Furthermore, hybrid MPC control methods increase the sensitivity [25].…”

Section: Introductionmentioning

confidence: 99%

New improved hybrid MPPT based on neural network-model predictive control-kalman filter for photovoltaic system

Kacimi

Grouni

Idir

et al. 2020

IJEECS

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In this paper, new hybrid Maximum Power Point Tracking (MPPT) strategy for Photovoltaic Systems has been proposed. The proposed technique for MPPT control based on a novel combination of an Artificial Neural Network (ANN) with an improved Model Predictive Control using Kalman Filter (NN-MPC-KF). In this paper the Kalman Filter is used to estimate the converter state vector for minimizes the cost function then predict the future value to track the Maximum Power Point (MPP) with fast changing weather parameters. The proposed control technique can track the MPP in fast changing irradiance conditions and a small overshoot. Finally, the system is simulated in the MATLAB/Simulink environment. The simulation results verify the appropriate performance of the proposed method. As a result, the proposed algorithm can achieve higher maximum power point tracking efficiency, faster dynamic response, and lower oscillations. In addition, the comparative simulation results of the proposed algorithm with the other maximum power point tracking algorithms show the superiority of the proposed algorithm.

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Performance evaluation of a MPPT controller with model predictive control for a photovoltaic system

Cited by 9 publications

References 40 publications

Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Design of type‐2 fuzzy logic controller optimized with firefly algorithm for maximum power point tracking of photovoltaic system based on super lift Luo converter

Quantitative Comparison of Infrared Thermography, Visual Inspection, and Electrical Analysis Techniques on Photovoltaic Modules: A Case Study

New improved hybrid MPPT based on neural network-model predictive control-kalman filter for photovoltaic system

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