Control Hardware in the Loop and IoT Integration: A Testbed for Residential Photovoltaic System Evaluation

Sámano-Ortega, Víctor; Mendez-Guzman, Hugo; Padilla-Medina, José A.; Aguilera-Alvarez, Juan; Nolasco, Juan José Martínez

doi:10.1109/access.2022.3188686

Cited by 8 publications

(4 citation statements)

References 41 publications

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“…Samano-Ortega et al [ 46 ] developed a platform for the validation of photovoltaics (PVs) system controllers using IoT and HIL concept. The platform englobes five main parts: (i) a control emulator based on HIL, producing the behavior of PVs’ arrays, a converter, and Alternating-Current (AC) loads, (ii) Cloud database, (iii) smart sensors for load monitoring, (iv) residential PVs (RPVs) connected to the Internet, and (v) a mobile application for tracking and monitoring.…”

Section: Related Workmentioning

confidence: 99%

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Kharbouch

Berouine²,

Elkhoukhi³

et al. 2022

Sensors

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In this work, a Hardware-In-the-Loop (HIL) framework is introduced for the implementation and the assessment of predictive control approaches in smart buildings. The framework combines recent Internet of Things (IoT) and big data platforms together with machine-learning algorithms and MATLAB-based Model Predictive Control (MPC) programs in order to enable HIL simulations. As a case study, the MPC algorithm was deployed for control of a standalone ventilation system (VS). The objective is to maintain the indoor Carbon Dioxide (CO2) concentration at the standard comfort range while enhancing energy efficiency in the building. The proposed framework has been tested and deployed in a real-case scenario of the EEBLab test site. The MPC controller has been implemented on MATLAB/Simulink and deployed in a Raspberry Pi (RPi) hardware. Contextual data are collected using the deployed IoT/big data platform and injected into the MPC and LSTM machine learning models. Occupants’ numbers were first forecasted and then sent to the MPC to predict the optimal ventilation flow rates. The performance of the MPC control over the HIL framework has been assessed and compared to an ON/OFF strategy. Results show the usefulness of the proposed approach and its effectiveness in reducing energy consumption by approximately 16%, while maintaining good indoor air quality.

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Section: Related Workmentioning

confidence: 99%

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Kharbouch

Berouine²,

Elkhoukhi³

et al. 2022

Sensors

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“…Digital video watermarking mechanism is one of the mechanisms that can be used in monitoring solar panels. With video watermarking techniques, GPS coordinates, date, time and temperature can be monitored in real time [12]- [14]. Another mechanism for monitoring can also be done by using a Wireless Sensor Network (WSN).…”

Section: Introductionmentioning

confidence: 99%

Realtime And Centralized Solar Panel Online Monitoring System Design Using Thingspeak

Triwijaya,

Aulia Feriando,

Feriza

et al. 2023

jrtt

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Indonesia is a country with a tropical climate and has a high intensity of solar irradiation. Seeing this situation, Indonesia is the right region to implement the installation of new and renewable energy such as PLTS. In the use of solar panels, the magnitude of the output power is determined by several environmental conditions, such as the intensity of sunlight, temperature, and the direction in which sunlight comes. To prevent damage and deterioration in the performance of solar panels, research is needed for a more accurate and efficient use of renewable energy. The study was a solar panel system with a Wemos D1 microcontroller that monitored online using Thing speak with the help of INA 219 and LDR sensors. Online monitoring system by recording data on current, voltage, and light intensity in real time and centralized. The system will store and record measurement data every 15 seconds in the form of JSON, XML, CSV file extensions. Error on the voltage sensor of 1.7% for and 3.2% for the current sensor. average panel voltage value of 12.77 V, average panel current value of 0.43 A and 2410 lux for light intensity at interval testing from 07:00 a.m.to 16:00p.m.

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“…The platform was developed in an NI myRIO 1900 (NATIONAL INSTRUMENTS CORP., Austin, USA) and exhibits relative errors below 2%. Finally, in [39], a testbed to validate a residential photovoltaic system is presented. The testbed includes, among other components, an HIL platform developed in an NI myRIO that emulates a system composed of a photovoltaic array, a boost converter, and a monophasic inverter.…”

Section: Introductionmentioning

confidence: 99%

Development of a Hardware-in-the-Loop Platform for the Validation of a Small-Scale Wind System Control Strategy

Martínez-Nolasco,

Sámano-Ortega,

Botello-Álvarez

et al. 2023

Energies

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The use of renewable energies contributes to the goal of mitigating climate change by 2030. One of the fastest-growing renewable energy sources in recent years is wind power. Large wind generation systems have drawbacks that can be minimized using small wind systems and DC microgrids (DC-µGs). A wind system requires a control system to function correctly in different regions of its operating range. However, real-time analysis of a physical wind system may not be feasible. An alternative to counteract this disadvantage is using real-time hardware in the loop (HIL) simulation. This article describes the implementation of an HIL platform in an NI myRIO 1900 to evaluate the performance of control algorithms in a small wind system (SWS) that serves as a distributed generator for a DC-µG. In the case of an SWS, its implementation implies nonlinear behaviors and, therefore, nonlinear equations, and this paper shows a way to do it by distributing the computational work, using a high-level description language, and achieving good accuracy and latency with a student-oriented development kit. The platform reproduces, with an integration time of 10 µs, the response of the SWS composed of a 3.5 kW turbine with a fixed blade pitch angle and no gear transmission, a permanent magnet synchronous generator (PMSG), and a three-phase full-bridge AC/DC electronic power converter. The platform accuracy was validated by comparing its results against a software simulation. The compared variables were the PMSG currents in dq directions, the turbine’s angular speed, and the DC bus’s voltage. These comparisons showed mean absolute errors of 0.04 A, 1.9 A, 0.7 rad/s, and 9.5 V, respectively. The platform proved useful for validating the control algorithm, exhibiting the expected results in comparison with a lab-scale prototype using the same well-known control strategy. Using a well-known control strategy provides a solid reference to validate the platform.

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Control Hardware in the Loop and IoT Integration: A Testbed for Residential Photovoltaic System Evaluation

Cited by 8 publications

References 41 publications

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

Realtime And Centralized Solar Panel Online Monitoring System Design Using Thingspeak

Development of a Hardware-in-the-Loop Platform for the Validation of a Small-Scale Wind System Control Strategy

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