Volume-8 , Issue-6 , Dec 2020, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  ESP32-Based Workbench for Digital Control Systems of Duty-Cycle Modulation Buck Choppers

  Paul Owoundi Etouké, Léandre Nneme Nneme, Jean Mbihi

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.6 , pp.62-67, Dec-2020

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  Abstract
  This research work presents an automated workbench for digital control systems of DCM (Duty-cycle modulation) Buck choppers. The prototyping control system used as practical case study consists of: a power Buck chopper associated with a simple DCM driver, an UTD2052CL digital storage oscilloscope, a PS150 power supply with ±15 V outputs, a Laptop computer with installed Arduino IDE/C++ 1.8.13, analog signal conditioning circuits, an ESP32 system-on-chip device, and a standard testing board. The overall ESP32-based workbench has been implemented, built and well tested. Then, experimental conditions and results for all automated operating modes are presented and discussed.

  Key-Words / Index Term
  Digital control system, DCM Buck choppers, analog signal conditioning circuits, ESP32 system-on-chip, Arduino C++ application, automated operating modes

  References
  [1] J. Mbihi, F. Ndjali Beng, M. Kom, M. Mbouenda., "Modelling and simulation of a class of duty-cycle modulation for industrial electronics," Iranian Journal of Electrical and Computer Engineering, Vol.4, Issue.2, pp. 121-128, 2005.
  [2] L. Nneme Nneme, B. Moffo Lonla, G. B. Sonfack. J. Mbihi, "Review of multipurpose duty-cycle modulation technology in electrical engineering and industrial electronics," Journal of Electrical Engineering, Electronics, Control and Computer Science, Vol.4, Issue.12, pp.9-18, 2018.
  [3] J. Mbihi, F. Njali Beng, M. Kom, M. Mbouenda., "A novel analog-to-digital conversion technique using nonlinear duty-cycle modulation," International Journal of Electronics and Computer Science Engineering, Vol.1, No. 3, pp 818-825, 2012.
  [4] J. Mbihi, L. Nneme Nneme, "A multichannel analog-to-digital conversion technique using parallel duty-cycle modulation," International Journal of Electronics and
  Computer Science Engineering, Vol.1, No.3, pp.826-833, 2012.
  [5] G. B. Sonfack J. Mbihi, "FPGA-based Analog-to-Digital Conversion Via Optimal Duty-Cycle Modulation," Journal of Electrical and Electronic Engineering, Vol.8, No.2, pp. 29-36, 2018.
  [6] B. Lonla Moffo, J. MbihI, "A novel digital duty-cycle modulation scheme for FPGA-based digital-to-analog conversion," IEEE Transactions on Circuits and Systems II, No.62, Issue.6, pp.543-547, 2015.
  [7] B. Lonla Moffo, J. Mbihi, Nneme Nneme, "FPGA-Based Multichannel Digital Duty-Cycle Modulation and Application to simultaneous generation of analog signals," Journal of Electronic Design Technology, Vol.8, Issue.1, pp.23-35, 2017.
  [8] L. Nneme Nneme, J. Mbihi, "Modeling and simulation of a new duty-cycle modulation scheme for signal transmission system," American Journal of Electrical and Electronic Engineering, Vol.2, No.3, pp.82-87, 2014.
  [9] L. Tatou Nguefack, F. Paune, G. Wamba Kenfack, J. Mbihi, "A novel optical fiber transmission system using duty-cycle modulation and application to ECG signals: Analog design and simulation," Journal of Electrical Engineering, Electronics, Control and Computer Science. Vol.6, Issue.21, pp.39-48, 2020.999
  [10] Y P. Sounsoumo Dangwe, J. Mbihi, Haman-Djalo, J. Effa, "Virtual Digital Control Scheme for a duty-cycle modulation Boost converter," Journal of Computer Science and Control Systems, Vo.10, Vo.2, pp.22-27, 2017.
  [11] Y P. Sounsoumo Dangwe, Haman-Djalo, J. Mbihi, J. Effa, "Modélisation et simulation virtuelle d’un nouveau schéma de réglage de hacheurs Boost à commande rapprochée par modulation en rapport cyclique," Afrique Science, pp.176-185, Vol.13, No.1, 2017.
  [12] A. Obono Biyobo, L. Nneme Nneme, J., Mbihi, "A novel sine duty-cycle modulation control scheme for photovoltaic single-phase power inverters," WSEAS Transactions on Circuits and Systems, Vol.17, pp.105-113, 2018.
  [13] A. Obono Biyobo, L. Nneme Nneme, J. Mbihi, F. Pauné, B. Lonla Moffo, "Etude expérimentale d’un nouveau modèle d’onduleur solaire monophasé à modulation en rapport cyclique," Afrique Science, Vol.16, No.2, pp.118-131, 2020.
  [14] L. Nneme Nneme, and Mbihi, J., "Virtual simulation and comparison of sine pulse width and sine duty-cycle modulation drivers for single phase power converters," Journal of electrical Engineering, Electronics, Control and Computer Science, Vol.6, Issue.21, pp.31-38, 2020.
  [15] J. Mbihi, L. NnemE Nneme,, "A novel Control Scheme for Buck Power Converters using Duty-Cycle Modulation," International Journal of Power Electronics, Vol.5, No.3/4, pp.185-199, 2013.
  [16] P. Owoundi Etouke, L. Nneme Nneme, J. Mbihi, "An optimal control scheme for a class of duty-cycle modulation Buck choppers: Analog design and virtual simulation," Journal of Electrical Engineering, Electronics, Control and Computer Science, No.6, Vol.19, pp.13-20, 2020.
  [17] J., Mbihi, “Analog automation and digital Feedback control techniques“, Ed. John Wiley and Sons Inc, 238 pages, New Jersey (USA), 2018.
  [18] P. Owoundi Etouke, J. Mbihi, L. Nneme Nneme, "Synthesis of a Digital PID/LQR Control System for Duty-Cycle Modulation Buck Converters,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, Vol.6, Issue.6, pp.186-189, 2020.
  [19] M. Girish Aishwarya, M. Prabhakar, "Automated Water Quality Monitoring IOT System for Small Scale Aquaculture Farms," International Journal of Computer Sciences and engineering, Vol.10 Issue.10, pp.40-47, 2020.
  [19] L. M. Fernandez-Gomez, A. Camacho-Martinez, "Electroonic Skills wit Arduino and Raspberry in Undergraduate Research Course," International Journal of Computer Sciences and engineering, Vol.8, Issue.7, pp.8-10, 2020.

  Citation
  Paul Owoundi Etouké, Léandre Nneme Nneme, Jean Mbihi, "ESP32-Based Workbench for Digital Control Systems of Duty-Cycle Modulation Buck Choppers," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.6, pp.62-67, 2020

• Open Access   Article

  Hydro Meteorological Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Hydro Meteorological Drought Condition Derived using Rainfall Data

  Rajesh Kumar Sharma

  Research Paper | Journal-Paper (IJSRCSE)

  Vol.8 , Issue.6 , pp.68-71, Dec-2020

  Abstract

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  Abstract
  This paper focuses on Hydro Metrological Drought Forecasting, using Artificial Neural Network (ANN) and predicts the values of Hydro Meteorological Drought condition derived using Rainfall data of Bhopal (M.P). We have used the Rainfall data as input data of ANN model for Hydro Meteorological Drought forecasting, and determine Standardized Precipitation Index (SPI). Artificial Neural networks operate on the principle of learning from a training set. There is a large variety of neural network models and learning procedures. Two classes of neural networks that are usually used for prediction applications are feed-forward networks and recurrent networks. They often train both of these networks using back-propagation algorithm.

  Key-Words / Index Term
  Data Source, Artificial Neural Network

  References
  [1]. Agnew, C. T.: Using the SPI to identify drought. Drought Network News, Vol.12, Issue.1, pp.6-11, 1999.
  [2]. Bankert, R. L.: Cloud classification of AVHRR Imagery in maritime regions using a probabilistic neural network, J. Appl. Meteorol., 33, pp.909–918, 1994.
  [3]. Marzban, C. and Stumpf, G. J.: A neural network for tornado prediction based on Doppler radar-derived attributes. J. Appl. Meteor., 35, pp.617–626, 1996.
  [4]. Mu¨ller, B., and Reinhardt, J.: Neural Networks: An Introduction, the Physics of Neural Networks Series, Springer-Verlag, 2, pp.266, 1991.
  [5]. McKee, T. B., Doesken, N. J. and Kleist J.: The relation of drought frequency and duration to time scales, Proceedings of the Eighth Conference on Applied Climatology, American Meteorological Society, Boston. pp.179–184, 1993.
  [6]. McKee, T. B., Doesken, N. J. and Kleist, J.: Drought monitoring with multiple time scales. Proceedings of the Ninth Conference on Applied Climatology; American Meteorological Society, Boston. pp.233–236, 1995.

  Citation
  Rajesh Kumar Sharma, "Hydro Meteorological Drought Forecasting, using Artificial Neural Network (ANN) and Predict Values of Hydro Meteorological Drought Condition Derived using Rainfall Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.8, Issue.6, pp.68-71, 2020