Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

Ma, Suliang; Chen, Mingxuan; Wu, Jianwen; Huo, Wenlei; Huang, Lian

doi:10.3390/en9121005

Cited by 22 publications

(12 citation statements)

References 42 publications

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“…In recent years, various scholars have proposed smart maximum power tracking techniques for photovoltaic module arrays, including the fuzzy control (FC) [8,9], genetic algorithm (GA) [10], neural network (NN) [11], artificial neural network (ANN) [12,13], and ant colony algorithm (ACA) [14,15] methods. In particular, the FC, NN, and ANN methods involve complex control processes and computation; hence, they are difficult to execute.…”

Section: Introductionmentioning

confidence: 99%

Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

et al. 2018

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This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power-voltage (P-V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.

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Section: Introductionmentioning

confidence: 99%

Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

et al. 2018

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“…The functioning of solar photovoltaic arrays obviously requires having high quality real-time measurements (monitoring) and a data storing system for several essential parameters; see also, for example, [2][3][4][5]. A wide variety of the control and monitoring systems for photovoltaic arrays was discussed in [6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

New Monitoring System for Photovoltaic Power Plants’ Management

Beránek¹,

Olšan

Libra

et al. 2018

Energies

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An innovative solar monitoring system has been developed. The system aimed at measuring the main parameters and characteristics of solar plants; collecting, diagnosing and processing data. The system communicates with the inverters, electrometers, metrological equipment and additional components of the photovoltaic arrays. The developed and constructed long working system is built on special data collecting technologies. At the generating plants, a special data logger BBbox is installed. The new monitoring system has been used to follow 65 solar plants in the Czech Republic and elsewhere for 175 MWp. As an example, we have selected 13 PV plants in this paper that are at least seven years old. The monitoring system contributes to quality management of plants, and it also provides data for scientific purposes. Production of electricity in the built PV plants reflects the expected values according to internationally used software PVGIS (version 5) during the previous seven years of operation. A comparison of important system parameters clearly shows the new solutions and benefits of the new Solarmon-2.0 monitoring system. Secured communications will increase data protection. A higher frequency of data saving allows higher accuracy of the mathematical models.

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“…On the one hand, PV maximum power point tracking (MPPT) systems, based on dc/dc power converters, are responsible for high tracking efficiency. Indeed, MPPT systems are operated through a closed-loop structure such that an improved tracking efficiency is obtained in comparison with open-loop structures, [1][2][3][4][5][6]. On the other hand, switching devices are the most fragile components in PEC [7], which are mainly affected by thermal stress conditions [8].…”

Section: Introductionmentioning

confidence: 99%

Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems

et al. 2018

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This paper proposes a fault identification system for short and open-circuit switch faults (SOCSF) for a dc/dc converter acting as a Maximum Power Point Tracker (MPPT) in Photovoltaic (PV) systems. A closed-loop operation is assumed for the boost dc/dc converter. A linearizing control plus a Proportional-Derivative (PD) controller is suggested for PV voltage regulation at the maximum power point (MPP). In this study, the SOCSF are modeled by using an additive fault representation and the fault identification (FI) system is synthesized departing from a Luenberger observer. Hence, an FI signal is obtained, which is insensitive to irradiance and load current changes, but affected by the SOCSF. For FI purposes, only the sensors used in the control system are needed. Finally, an experimental evaluation is presented by using a solar array simulator dc power supply and a boost dc/dc converter of 175 W in order to validate the ideas this study exposes.

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Augmented Nonlinear Controller for Maximum Power-Point Tracking with Artificial Neural Network in Grid-Connected Photovoltaic Systems

Cited by 22 publications

References 42 publications

Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

New Monitoring System for Photovoltaic Power Plants’ Management

Open- and Short-Circuit Fault Identification for a Boost dc/dc Converter in PV MPPT Systems

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