Developing Adaptive Neuro-Fuzzy Inference System for Controlling the Intelligent Universal Transformers in ADA

Abstract:

This approach is carry out for developing the Adaptive Neuro-Fuzzy Inference System (ANFIS) for controlling the forthcoming Intelligent Universal Transformer (IUT) in regard of voltages and current control in both input and output stages which is optimized by particle swarm optimization. Current or voltages errors and their time derivative have been considered as the inputs of Nero Fuzzy controller for elaborating the firing angles of converters in IUT basic construction. ANFIS constructed from a fuzzy inference system (FIS) in which the membership function parameters are tuned according to the back propagation algorithm or in conjunction to the least squares method. A neural network maps inputs through input membership functions and associated parameters, and output membership functions and associated parameters to outputs which interprets the input-output map. The associated parameters of membership functions change through the learning algorithm by a gradient vector modeling the input output data in case of given parameters. Optimization method will be investigated to adjust the parameters according to error reduction computed by sum of the squared variation from actual outputs to the desired ones. Advanced Distribution Automation (ADA) is the state of art introducing for tomorrows distribution automation with the new invention in management and control. ADA is equipping by the Intelligent Equipment Devices (IED) in which IUT is a key point introducing as an intelligent transformer subjecting for tomorrows distribution automation in the near future. The proposed ANFIS is a control scheme develop for controlling the IUT by bringing the major advantages like harmonic Filtering, voltage regulation, automatic sag correction, energy storage, 48V DC option, three phase outputs in term of one phase input, reliable divers power as 240V 400HZ for communication utilization and two other 240V 60 HZ outputs, dynamic system monitoring and robustness in major disturbances occurred in terms of input and load variation.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 3969-3973
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.3969
• Online since: January 2012
• Authors: Maryam Sadeghi, Majid Gholami
• Keywords: ADA, ANFIS, Back Propagation, DER, FIS, IED, IUT, Neuro-Fuzzy

References

[1] B. Allaoua, A. LAOUFI, B. Gasbaoui, A. Abderrahmani, Neuro-Fuzzy DC Motor Speed Control Using Particle Swarm Optimization, Leonardo Electronic Journal of Practices and Technologies, 15, July-December (2009).

[2] EPRI Product ID # 1009516, Feasibility Study for the Development of High-Voltage, Low-Current Power Semiconductor Devices, 2003 Strategic Science and Technology Project.

[3] F. Goodman, Intelligent Universal Transformer Technology Development, EPRI (2006).

[4] IEEE Power Engineering Society, Research Plan for Advanced Distribution Automation, General Meeting, (2005).

[5] M. Mc. Granaghan, F. Goodman, Technical and System Requirements for Advanced Distribution Automation, 18th International Conference on Electricity Distribution, CIRED, Turin, 6-9 June (2005).

DOI: 10.1049/cp:20051374

[6] P. Z. Grabowski, A. M. P. Kazmierkowski, B. K. Bose, A Simple Direct-Torque Neuro-Fuzzy Control of PWM-Inverter-Fed Induction Motor Drive, IEEE Transaction on Industrial Electronics, vol 47, No. 4, August (2000).

DOI: 10.1109/41.857966

[7] X. H. Yu, W. Li, Taufik, Design and implementation of a neural network controller for real-time adaptive voltage regulation, Department of Electrical Engineering, California Polytechnic Scare University. San Luis Obispo. CA 93407. USA.

DOI: 10.31979/mti.2023.2153

[8] D. Wang, C. Mao, J. Lu, S. Fan, F. Peng, Theory and application of distribution electronic power transformer, Electric Power Systems Research Journal, vol 77, 2007, pp.219-226.

DOI: 10.1016/j.epsr.2006.02.012

[9] A. Hefner, Silicon-Carbide Power Devices for High-voltage, high-Frequency Power Conversion, National Institute of Standards and Technology, Gaithersburg, (2007).

[10] D. Aggler, J. Biela, J. W. Kolar, Solid State Transformer based on Sic JFETs for Future Energy Distribution Systems, Power Electronic Systems Laboratory, Switzerland.

[11] A. Maitra, A. Sundaram, M. Gandhi, S. Bird, Sh. Doss, Intelligent Universal Transformer Design and Applications, CIRED 20th International Conference on Electricity Distribution, Prague, 8-11 June (2009).

DOI: 10.1049/cp.2009.1129

[12] J. S. Lai,A. Maitra,A. Mansoor,F. Goodman, Multilevel Intelligent Universal Transformer for medium voltage applications, IEEE Industry Application Conf., (2005).

DOI: 10.1109/ias.2005.1518705

[13] A. Maitra, A. Sundaram, Universal Intelligent Transformer Design and applications, Power Distribution Conference, (2009).

[14] S. R. Jalluri, B.V. Sanker Ram, A Neuro Fuzzy Controller For Induction Machines Drives, Journal of Theoretical and Applied Information Technology, 2005 - 2010 JATIT & LLS.

[15] E. Deniz, R. Coteli, B Dandil, S. Tuncer, F. Ata, M. T. Gencoglu, Neuro-Fuzzy Current Controller for Three-LevelCascade Inverter Based D-STATCOM, UPEC2010, 31st Aug, 3rd Sept (2010).

DOI: 10.4316/aece.2012.01013