Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2021, Cilt: 5 Sayı: 3, 221 - 230, 30.09.2021
https://doi.org/10.30521/jes.932581

Öz

Kaynakça

  • [1] Rahim, NA., Chaniago, K., Selvaraj, J. Single-phase seven-level grid-connected inverter for photovoltaic system, IEEE transactions on industrial electronics 2010; 58: 2435-2443.
  • [2] Rashid, MH. Power Electronics-Circuits, Devices and Applications. Burlington, USA: Butterworth-Heinemann, 2010.
  • [3] Bhattacharjee, T, Jamil,M, Jana, A. Design of SPWM based three phase inverter model. In: ICSESP 2018 Technologies for Smart-City Energy Security and Power; 28-30 March 2018: IEEE, pp. 1-6, doi: 10.1109/ICSESP.2018.8376696.
  • [4] Ismail, B, Taib, S, Saad, ARM, Isa, M, Hadzer, CM. Development of a Single Phase SPWM Microcontroller-Based Inverter. In: PECon 2006 IEEE International Power and Energy Conference; 28-29 November 2006: IEEE, pp. 437-440.
  • [5] Liou, WR, Villaruza, HM, Yeh, ML, Roblin, P. A digitally controlled low-EMI SPWM generation method for inverter applications. IEEE Transactions on industrial informatics 2013; 10: 73-83.
  • [6] Maheshri, S, Khampariya, P. Simulation of single phase SPWM (Unipolar) inverter. International journal of innovative research in advanced Engineering 2014; 1(9):12-18.
  • [7] Karthikeyan, B, Sundararaju, K, Palanisamy, R. ANN-Based MPPT Controller for PEM Fuel Cell Energized Interleaved Resonant PWM High Step Up DC-DC Converter with SVPWM Inverter Fed Induction Motor Drive. Iranian Journal of Science Technology, Transactions of Electrical Engineering 2021; 1-17.
  • [8] Xue, Y, Chang, L. Closed-loop SPWM control for grid-connected buck-boost inverters. In: IEEE 2004 35th Annual Power Electronics Specialists Conference; 20-25 June 2004: IEEE, pp. 3366-3371.
  • [9] Zhang, K, Kang, Y, Xiong, J, Chen, J. Direct repetitive control of SPWM inverter for UPS purpose. IEEE Transactions on Power Electronics 2003; 18: 784-792.
  • [10] Solanki, G., Gurjar, C., Lokhande, M. Modelling and Performance Evaluation of Square Wave And Spwm Based Inverter Fed AC Drive. International Journal for Research in applied Science EngineeringTechnology 2014; 2: 244-248.
  • [11] Maswood, AI. A switching loss study in SPWM IGBT inverter. In: PECon 2008 2nd International Power and Energy Conference;1-3 December 2008: IEEE, pp. 609-613.
  • [12] Sabanci, K, Balci, S, Aslan, MF. Estimation of the switching losses in DC-DC boost converters by various machine learning methods. Journal of Energy Systems 2020; 4: 1-11.
  • [13] Raju, NI, Islam, MS, Uddin, AA. Sinusoidal PWM signal generation technique for three phase voltage source inverter with analog circuit & simulation of PWM inverter for standalone load & micro-grid system. International journal of renewable energy research 2013; 3: 647-658.
  • [14] Lakka, M, Koutroulis, E, Dollas, A. Development of an FPGA-based SPWM generator for high switching frequency DC/AC inverters. IEEE Transactions on power electronics 2013; 29: 356-365.
  • [15] Aslan, MF, Sabanci, K, Durdu, A. Different wheat species classifier application of ANN and ELM. Journal of Multidisciplinary Engineering Science Technology 2017; 4: 8194-8198.
  • [16] Darvishan, A., Bakhshi, H., Madadkhani, M., Mir, M., Bemani, A. Application of MLP-ANN as a novel predictive method for prediction of the higher heating value of biomass in terms of ultimate analysis. Energy Sources, Part A: Recovery, Utilization, Environmental Effects 2018; 40: 2960-2966.
  • [17] Gupta, DK. A review on wireless sensor networks. Network Complex Systems 2013; 3: 18-23.
  • [18] Kayabasi, A. An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains. International Journal of Intelligent Systems Applications in Engineering 2018; 6: 85-91.
  • [19] Sabanci, K, Kayabasi, A, Toktas, A. Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food Agriculture 2017; 97: 2588-2593.
  • [20] Koklu, M, Ozkan, IA. Multiclass classification of dry beans using computer vision and machine learning techniques. Computers Electronics in Agriculture 2020; 174: 105507.
  • [21] Sabanci, K, Koklu, M. The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals. International Journal of Intelligent Systems Applications in Engineering 2015; 3: 127-130.
  • [22] Zhang, S, Cheng, D, Deng, Z, Zong, M, Deng, X. A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters 2018; 109: 44-54.
  • [23] Deka, PC. Support vector machine applications in the field of hydrology: a review. Applied soft computing 2014; 19: 372-386.
  • [24] Durgesh, KS, Lekha, B. Data classification using support vector machine. Journal of theoretical applied information technology 2010; 12: 1-7.
  • [25] Sabanci, K. Artificial intelligence based power consumption estimation of two-phase brushless DC motor according to FEA parametric simulation. Measurement 2020; 155: 107553.

Sensorless current prediction in single phase inverter circuits with machine learning algorithms

Yıl 2021, Cilt: 5 Sayı: 3, 221 - 230, 30.09.2021
https://doi.org/10.30521/jes.932581

Öz

Inverter circuits are widely used in power electronics applications such as electric motor control, induction heating or different Alternating Current (AC) loads. The control signal applied to the switching elements can affect the quality of the sinusoidal signal that occurs at the output of the inverter circuit by the means of voltage and current values. The inverter circuit topologies are generally designed as closed loop. However, these cause complexity of the circuit topology, increase the production costs and give difficulties in designing the control signals. In the present work, simulations of a single-phase inverter circuit with the Sinusoidal Pulse Width Modulation (SPWM) control signal are performed. Thus, the effect of the SPWM signals on the output of the inverter has been observed by changing the Modulation rate (M), the carrier signal frequency (f_c) and the reference signal frequency (f_r). Through the data obtained, the output current of a phase inverter without a sensor is estimated by Machine Learning Algorithms (MLA) such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM) and K-Nearest Neighbors (KNN).

Kaynakça

  • [1] Rahim, NA., Chaniago, K., Selvaraj, J. Single-phase seven-level grid-connected inverter for photovoltaic system, IEEE transactions on industrial electronics 2010; 58: 2435-2443.
  • [2] Rashid, MH. Power Electronics-Circuits, Devices and Applications. Burlington, USA: Butterworth-Heinemann, 2010.
  • [3] Bhattacharjee, T, Jamil,M, Jana, A. Design of SPWM based three phase inverter model. In: ICSESP 2018 Technologies for Smart-City Energy Security and Power; 28-30 March 2018: IEEE, pp. 1-6, doi: 10.1109/ICSESP.2018.8376696.
  • [4] Ismail, B, Taib, S, Saad, ARM, Isa, M, Hadzer, CM. Development of a Single Phase SPWM Microcontroller-Based Inverter. In: PECon 2006 IEEE International Power and Energy Conference; 28-29 November 2006: IEEE, pp. 437-440.
  • [5] Liou, WR, Villaruza, HM, Yeh, ML, Roblin, P. A digitally controlled low-EMI SPWM generation method for inverter applications. IEEE Transactions on industrial informatics 2013; 10: 73-83.
  • [6] Maheshri, S, Khampariya, P. Simulation of single phase SPWM (Unipolar) inverter. International journal of innovative research in advanced Engineering 2014; 1(9):12-18.
  • [7] Karthikeyan, B, Sundararaju, K, Palanisamy, R. ANN-Based MPPT Controller for PEM Fuel Cell Energized Interleaved Resonant PWM High Step Up DC-DC Converter with SVPWM Inverter Fed Induction Motor Drive. Iranian Journal of Science Technology, Transactions of Electrical Engineering 2021; 1-17.
  • [8] Xue, Y, Chang, L. Closed-loop SPWM control for grid-connected buck-boost inverters. In: IEEE 2004 35th Annual Power Electronics Specialists Conference; 20-25 June 2004: IEEE, pp. 3366-3371.
  • [9] Zhang, K, Kang, Y, Xiong, J, Chen, J. Direct repetitive control of SPWM inverter for UPS purpose. IEEE Transactions on Power Electronics 2003; 18: 784-792.
  • [10] Solanki, G., Gurjar, C., Lokhande, M. Modelling and Performance Evaluation of Square Wave And Spwm Based Inverter Fed AC Drive. International Journal for Research in applied Science EngineeringTechnology 2014; 2: 244-248.
  • [11] Maswood, AI. A switching loss study in SPWM IGBT inverter. In: PECon 2008 2nd International Power and Energy Conference;1-3 December 2008: IEEE, pp. 609-613.
  • [12] Sabanci, K, Balci, S, Aslan, MF. Estimation of the switching losses in DC-DC boost converters by various machine learning methods. Journal of Energy Systems 2020; 4: 1-11.
  • [13] Raju, NI, Islam, MS, Uddin, AA. Sinusoidal PWM signal generation technique for three phase voltage source inverter with analog circuit & simulation of PWM inverter for standalone load & micro-grid system. International journal of renewable energy research 2013; 3: 647-658.
  • [14] Lakka, M, Koutroulis, E, Dollas, A. Development of an FPGA-based SPWM generator for high switching frequency DC/AC inverters. IEEE Transactions on power electronics 2013; 29: 356-365.
  • [15] Aslan, MF, Sabanci, K, Durdu, A. Different wheat species classifier application of ANN and ELM. Journal of Multidisciplinary Engineering Science Technology 2017; 4: 8194-8198.
  • [16] Darvishan, A., Bakhshi, H., Madadkhani, M., Mir, M., Bemani, A. Application of MLP-ANN as a novel predictive method for prediction of the higher heating value of biomass in terms of ultimate analysis. Energy Sources, Part A: Recovery, Utilization, Environmental Effects 2018; 40: 2960-2966.
  • [17] Gupta, DK. A review on wireless sensor networks. Network Complex Systems 2013; 3: 18-23.
  • [18] Kayabasi, A. An Application of ANN Trained by ABC Algorithm for Classification of Wheat Grains. International Journal of Intelligent Systems Applications in Engineering 2018; 6: 85-91.
  • [19] Sabanci, K, Kayabasi, A, Toktas, A. Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food Agriculture 2017; 97: 2588-2593.
  • [20] Koklu, M, Ozkan, IA. Multiclass classification of dry beans using computer vision and machine learning techniques. Computers Electronics in Agriculture 2020; 174: 105507.
  • [21] Sabanci, K, Koklu, M. The Classification of Eye State by Using kNN and MLP Classification Models According to the EEG Signals. International Journal of Intelligent Systems Applications in Engineering 2015; 3: 127-130.
  • [22] Zhang, S, Cheng, D, Deng, Z, Zong, M, Deng, X. A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters 2018; 109: 44-54.
  • [23] Deka, PC. Support vector machine applications in the field of hydrology: a review. Applied soft computing 2014; 19: 372-386.
  • [24] Durgesh, KS, Lekha, B. Data classification using support vector machine. Journal of theoretical applied information technology 2010; 12: 1-7.
  • [25] Sabanci, K. Artificial intelligence based power consumption estimation of two-phase brushless DC motor according to FEA parametric simulation. Measurement 2020; 155: 107553.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Araştırma Makaleleri
Yazarlar

Hüseyin Türe 0000-0003-0561-9112

Selami Balcı 0000-0002-3922-4824

Kadir Sabancı 0000-0003-0238-9606

Muhammet Fatih Aslan 0000-0001-7549-0137

Yayımlanma Tarihi 30 Eylül 2021
Kabul Tarihi 4 Eylül 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 5 Sayı: 3

Kaynak Göster

Vancouver Türe H, Balcı S, Sabancı K, Aslan MF. Sensorless current prediction in single phase inverter circuits with machine learning algorithms. Journal of Energy Systems. 2021;5(3):221-30.

Journal of Energy Systems is the official journal of 

European Conference on Renewable Energy Systems (ECRES8756 and


Electrical and Computer Engineering Research Group (ECERG)  8753


Journal of Energy Systems is licensed under CC BY-NC 4.0