ข้อมูลบุคลากร

[1] J. Pahasa, K. Kraikitrat and C. Rakpenthai (2026). Coordinated control of HVDC and air conditioners using distributed MPC for regulating frequency and indoor temperature deviations, Electric Power Systems Research, vol. 258, pp. 1-14, September 2026 (Article number 113046). [2] K.P.Y. Myint, A. Deanseekeaw, J. Pahasa, P. Boonraksa, T. Boonraksa and B. Marungsri, (2026). Multi-objective energy management optimization on grid-integrated microgrid using multi-agent deep reinforcement learning for enhanced system stability in HRES and BESS, IEEE Access, vol. 14, pp. 32905-32926, 27 Feb. 2026. [3] A. Jewsubpong, N. Pothi, C. Rakpenthai, and J. Pahasa (2025). Impact of DC-link voltage ripple on induction motor drives utilizing cascaded H-bridge multilevel inverter, ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 23 (3), pp. 1 - 8, 21 Oct. 2025. [4] K. Meksagul, C. Rakpenthai, S. Uatrongjit, and J. Pahasa (2025). Fast and robust temperature-dependent three-phase state estimation using hierarchical constrained optimization, IEEE Open Access Journal of Power and Energy, vol. 12, pp. 541-551, 1 August 2025. [5] J. Pahasa, N. Rugthaicharoencheep, B. Marungsri and C. Rakpenthai (2025). Cooperative control of hydrogen electrolyzers and HVDC links for frequency regulation sharing in asynchronous power system, IEEE Access, vol. 13, pp. 131446-131460, 23 July 2025. [6] D. Wongta, S. Uatrongjit, J. Pahasa, S. Vachirasricirikul and C. Rakpenthai (2025). Transformer parameters and transmission line temperatures estimation in three-phase power networks, ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 23 (1), pp. 1 - 10, 28 Feb. 2025. [7] D. Wongta, J. Pahasa, C. Rakpenthai, and S. Uatrongjit (2024). Temperature-dependent state estimation based on constrained alternating optimization for three-phase power networks considering transformer parameters, International Journal of Electrical Power & Energy Systems, vol. 162 (110292), pp. 1 - 11, Nov. 2024. [8] S. Muangchuen, J. Pahasa and C. Rakpenthai (2024). Enhanced resilient model predictive control electrolyzers for frequency regulations under severe denial-of-service attacks, IEEE Access, vol. 12, pp. 65352 - 65361, 8 May. 2024. [9] S. Muangchuen, J. Pahasa and I. Ngamroo (2023). Improved resilient model predictive control for enhanced microgrid virtual inertia emulation by virtual energy storage system under DoS attacks, IEEE Access, vol. 11, pp. 96817 - 96830, 6 Sept. 2023. [10] J. Pahasa, P. Potejana, and I. Ngamroo (2022). “MPC-based virtual energy storage system using PV and air conditioner to emulate virtual inertia and frequency regulation of the low-inertia microgrid,” IEEE Access, vol. 10, pp. 133708-133719, 22 Dec. 2022. [11] Potejana, J. Pahasa, and I. Ngamroo (2022). “Fabrication of metallic nano-sing structures by soft stamping with the thermal uplifting method,” Crystals, vol. 12, no. 5 (668), pp. 1-11, 6 May. 2022. [12] J. Pahasa, P. Potejana, and I. Ngamroo (2021). “ Multi-objective decentralized model predictive control for inverter air conditioner control of indoor temperature and frequency stabilization in microgrid,” Energies, vol. 14, no. 21 (6969), pp. 1-28, Oct. 2021. [13] J. Pahasa and I. Ngamroo (2021). “Two-stage optimization based on SOC control of SMES installed in hybrid wind/PV system for stabilizing voltage and power fluctuations,” IEEE Transactions on Applied Superconductivity, vol. 31, no. 8, pp. 1-5, Nov. 2021. (Article no. 5401205) [14] J. Pahasa and I. Ngamroo (2018). Coordinated PHEV, PV, and ESS for microgrid frequency regulation using centralized model predictive control considering variation of PHEV number, IEEE Access, vol. 6, pp. 69151-69161, 2018. [15] J. Pahasa and I. Ngamroo (2017). Simultaneous control of frequency fluctuation and battery SOC in a smart grid using LFC and EV controllers based on optimal MIMO-MPC, Journal of Electrical Engineering & Technology, vol. 12(2), pp.601-611, 2017. [16] J. Pahasa and I. Ngamroo (2016). Coordinated control of wind turbine blade pitch angle and PHEVs using MPCs for load frequency control of microgrid, IEEE Systems Journal, vol.10, no.1, pp.97-105, 2016. [17] J. Pahasa and I. Ngamroo (2015). PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC, IEEE Transactions on Smart Grid, vol.6, no.3, pp.526-533, 2015. [18] J. Pahasa and I. Ngamroo (2014). Adaptive power system stabiliser design using optimal support vector machines based on harmony search algorithm, Electric Power Components and Systems, vol.42, no.5, pp. 439-452, 2014. [19] J. Pahasa, K. Hongesombut and I. Ngamroo (2012). Adaptive thyristor controlled series capacitor using particle swarm optimization and support vector regression, International Review on Modelling and Simulations, vol.5, no.2, pp.714-721, April 2012. [20] J. Pahasa and I. Ngamroo (2012). Optimal least squares support vector machines for SMES controller design using wide area phasor measurements. European Transactions on Electrical Power, vol. 22, October 2012, pp. 571-588. [21] J. Pahasa and I. Ngamroo (2012). PSO based Kernel principal component analysis and multi-class support vector machine for power quality problem classification. International Journal of Innovative Computing, Information and Control, vol.8, no.3(A), pp. 1523-1540, March 2012. [22] J. Pahasa and I. Ngamroo (2011). A heuristic training-based least squares support vector machines for power system stabilization by SMES. Expert Systems with Applications, vol. 38, no. 11, October 2011, pp. 13987-13993. [23] J. Pahasa and I. Ngamroo (2011). Least square support vector machine for power system stabilizer design using wide area phasor measurements. International Journal of Innovative Computing, Information and Control, vol.7, no.7B, August 2011, pp. 4487-4501.

[1] P. Kaewma, N. Pothi, K. Kraikitrat, C. Rakpenthai, A. L. Shuraiji, and J. Pahasa (2024). Robust flux-weakening control approach for three-phase induction motor drives, The 21st International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, May 27-30, 2024, Khonkaen, Thailand. [2] J. Pahasa, and I. Ngamroo (2020). “Adaptive output power smoothing of grid-connected hybrid wind-photovoltaic by SMES,” in Proc. IEEE Int. Conf. ASEMD, Tianjin, China, Oct. 2020, (ID20392). [3] J. Pahasa, and I. Ngamroo (2014). Model predictive control-based wind turbine blade pitch angle control for alleviation of frequency fluctuation in a smart grid. Proceedings of the 2014 International Electrical Engineering Congress (iEECON2014), 19-21 March 2014, Pattaya, Thailand, No.051, pp.304-307. [4] J. Pahasa, and I. Ngamroo (2013). Feature selection for adaptive power system stabilizer using optimal support vector machines. Proceedings of the 2013 International Electrical Engineering Congress (iEECON2013), 13-15 March 2013, Chiang Mai, Thailand, No.89, pp.304-307. [5] J. Pahasa, K. Hongesombut and I. Ngamroo (2012). PSO-based learning of support vector machines for adaptive TCSC. Proceedings of IASTED Technology and Management Conferences 2012, Power and Energy Systems, (AsiaPES 2012), 2-4 April 2012, Phuket, Thailand, no.768-092, pp.164-169. [6] P. Chantachiratham, K. Hongesombut and J. Pahasa (2011). Optimum fault current limiter placement using PSO method.The 5th PSU-UNS International Conference on Engineering and Technology (ICET-2011), 2-3 May, 2011, Merlin Beach Resort Hotel, Tritrang Beach, Phuket, Thailand, pp. 124-127. [7] J. Pahasa and I. Ngamroo (2010). Wide area SMES controller design using least-squares support vector machines. Proceedings of IASTED Technology and Management Conferences 2010, Power and Energy Systems, (AsiaPES 2010), 24-26 November 2010, Phuket, Thailand, no.701-160, pp.431-436. [8] J. Pahasa and I. Ngamroo (2010). GA-based support vector machines for adaptive power system damping controller of SMES. Proceedings of the 2010 Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology International Conference, (ECTI-CON 2010), 19-21 May 2010, Chiang Mai, Thailand, vol.1, pp.1011-1015. [9] J. Pahasa and I. Ngamroo (2010).Kernel principal component analysis for power quality problem classification.Proceedings of the 2010 Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology International Conference, (ECTI-CON 2010), 19-21 May 2010, Chiang Mai, Thailand, vol. 1, pp.675-679. [10] N. Theera-Umpon, S. Auephanwiriyakul, S. Suteepohnwiroj, J. Pahasa and K. Wantanajittikul (2008). River basin flood prediction using support vector machine.Proceeding of IEEE World Congress on Computational Intelligence (WCCI), pp. 47-52, Hong Kong, June 2008. [11] J. Pahasa and N. Theera-Umpon (2008). Cross-substation short term load forecasting using support vector machine. Proceeding of the 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), no.2, pp. 953-956, Krabi Thailand, May. 2008. [12] J. Pahasa and N. Theera-Umpon (2007). Short-term load forecasting using wavelet transform and support vector machine. Proceeding of the 8th International Power Engineering Conference (IPEC2007), pp. 47-52, Singapore, Dec. 2007.