Implementation of a cost-effective fuzzy MPPT controller on the Arduino board

Algarin, Carlos Arturo Robles; Fuentes, Roberto Liñán; Castro, Alfonso Peter

doi:10.21307/ijssis-2018-002

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…In this method, the voltage at the terminal of the PV cell is regulated to keep it closer to the MPP corresponding voltage (Vmpp) of the PV cell at the moment. To determine the maximum power point, controllers such as P&O, incremental conductance, fuzzy logic, and artificial neural networks can be used [ 31 , 32 , 33 , 34 , 35 ]. Based on the controller’s output signal, MPP is achieved by regulating the connection between the voltage up-converter and PV cell terminals [ 36 , 37 ].…”

Section: Requirements and Challengesmentioning

confidence: 99%

Light-Based IoT: Developing a Full-Duplex Energy Autonomous IoT Node Using Printed Electronics Technology

Perera

Katz

Häkkinen

et al. 2021

Sensors

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The light-based Internet of things (LIoT) concept defines nodes that exploit light to (a) power up their operation by harvesting light energy and (b) provide full-duplex wireless connectivity. In this paper, we explore the LIoT concept by designing, implementing, and evaluating the communication and energy harvesting performance of a LIoT node. The use of components based on printed electronics (PE) technology is adopted in the implementation, supporting the vision of future fully printed LIoT nodes. In fact, we envision that as PE technology develops, energy-autonomous LIoT nodes will be entirely printed, resulting in cost-efficient, flexible and highly sustainable connectivity solutions that can be attached to the surface of virtually any object. However, the use of PE technology poses additional challenges to the task, as the performance of these components is typically considerably poorer than that of conventional components. In the study, printed photovoltaic cells, printed OLEDs (organic light-emitting diodes) as well as printed displays are used in the node implementation. The dual-mode operation of the proposed LIoT node is demonstrated, and its communication performance in downlink and uplink directions is evaluated. In addition, the energy harvesting system’s behaviour is studied and evaluated under different illumination scenarios and based on the results, a novel self-operating limitation aware algorithm for LIoT nodes is proposed.

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Section: Requirements and Challengesmentioning

confidence: 99%

Light-Based IoT: Developing a Full-Duplex Energy Autonomous IoT Node Using Printed Electronics Technology

Perera

Katz

Häkkinen

et al. 2021

Sensors

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“…where E rr is the number of erro; DP is the ratio of change of power; DV is the change of voltage; DE rr is the rate of change of error; and P PV and V PV are the output active power and voltage of PV panels, respectively. A fuzzy controller can be implemented on any low to medium powerful microcontroller including Arduino Mega and Microchip to manipulate the output duty cycle D of the DC-DC converter depending on T and E e , which searches the MPP of the solar power system [24]. The solar power is dependent on the dynamic of solar irradiance [25].…”

Section: A Flcmentioning

confidence: 99%

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

Yap

Sarimuthu

Lim

2020

Journal of Modern Power Systems and Clean Energy

175

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In the last decade, artificial intelligence (AI) techniques have been extensively used for maximum power point tracking (MPPT) in the solar power system. This is because conventional MPPT techniques are incapable of tracking the global maximum power point (GMPP) under partial shading condition (PSC). The output curve of the power versus voltage for a solar panel has only one GMPP and multiple local maximum power points (MPPs). The integration of AI in MPPT is crucial to guarantee the tracking of GMPP while increasing the overall efficiency and performance of MPPT. The selection of AI-based MPPT techniques is complicated because each technique has its own merits and demerits. In general, all of the AI-based MPPT techniques exhibit fast convergence speed, less steady-state oscillation and high efficiency, compared with the conventional MPPT techniques. However, the AI-based MPPT techniques are computationally intensive and costly to realize. Overall, the hybrid MPPT is favorable in terms of the balance between performance and complexity, and it combines the advantages of conventional and AI-based MPPT techniques. In this paper, a detailed comparison of classification and performance between 6 major AI-based MPPT techniques have been made based on the review and MATLAB/Simulink simulation results. The merits, open issues and technical implementations of AI-based MPPT techniques are evaluated. We intend to provide new insights into the choice of optimal AI-based MPPT techniques. Index Terms-Maximum power point tracking (MPPT), artificial intelligence (AI), fuzzy logic control (FLC), artificial neural network (ANN), genetic algorithm (GA), swarm intelligence (SI), machine learning (ML).

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“…The presence of oscillations has contributed to the researchers focusing their efforts on designing MPPT controllers [10] with better performance. In this way, investigations have been carried out with genetic algorithms [11], fuzzy logic [12][13][14][15][16][17], golden section [18,19], simulated annealing [20], artificial bee colony [21], adaptive control [22], glowworm swarm optimization [23], ant colony optimization [24], artificial neural networks [25][26][27][28][29][30], and numerical methods [31].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm

Viloria-Porto¹,

Robles-Algarín²,

Restrepo-Leal³

2018

Energies

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The Real-Time Recurrent Learning Gradient (RTRL) algorithm is characterized by being an online learning method for training dynamic recurrent neural networks, which makes it ideal for working with non-linear control systems. For this reason, this paper presents the design of a novel Maximum Power Point Tracking (MPPT) controller with an artificial neural network type Adaptive Linear Neuron (ADALINE), with Finite Impulse Response (FIR) architecture, trained with the RTRL algorithm. With this same network architecture, the Least Mean Square (LMS) algorithm was developed to evaluate the results obtained with the RTRL controller and then make comparisons with the Perturb and Observe (P&O) algorithm. This control method receives as input signals the current and voltage of a photovoltaic module under sudden changes in operating conditions. Additionally, the efficiency of the controllers was appraised with a fuzzy controller and a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) controller, which were developed in previous investigations. It was concluded that the RTRL controller with adaptive training has better results, a faster response, and fewer bifurcations due to sudden changes in the input signals, being the ideal control method for systems that require a real-time response.

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Implementation of a cost-effective fuzzy MPPT controller on the Arduino board

Cited by 12 publications

References 38 publications

Light-Based IoT: Developing a Full-Duplex Energy Autonomous IoT Node Using Printed Electronics Technology

Light-Based IoT: Developing a Full-Duplex Energy Autonomous IoT Node Using Printed Electronics Technology

Artificial Intelligence Based MPPT Techniques for Solar Power System: A review

A Novel Approach for an MPPT Controller Based on the ADALINE Network Trained with the RTRL Algorithm

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