i-manager Publications

i-manager's Journal on Power Systems Engineering (JPS)

Current Issue Vol. 11 Issue 3

Voltage Stability and Power Loss Minimization with STATCOM using Particle Swarm Optimization and Hybrid Firefly Particle Swarm Optimization Algorithms
Load Frequency Control of Multi Area Power System Network with Soft Computing Algorithm Based Controller and Coordinated Strategy of TCSC and Battery Energy Storage
Performance Analysis of Distribution System under Different Loading Conditions using Test Bench
Simulation of Solar PV with Different Control System of Boost Converter for Composite Climate
Enhancement of System Performance using PeSche Scheduling Algorithm on Multiprocessors

Most Cited

Volume 6 Issue 4 November - January 2019

Article

Different Energy Conservation Techniques for Various Operations in Opencast Mine

Virendra Kumar* , Arnab Bhuiya , Aditi Chatterjee, S. K. Chaulya, Gautam Banerjee

, _* CSIR-Central Institute of Mining & Fuel Research, Dhanbad, Jharkhand, India.

M. Tech. Scholar of Mine Machinery, IIT-ISMS, Dhanbad, Jharkhand, India

Kumar, V., Bhuiya, A., Chaaterji, A., Chaulya, S. K., and Banerjee, G. (2019). Different Energy Conservation Techniques for Various Operations In Opencast Mine. i-manager’s Journal on Power Systems Engineering, 6(4), 1-10. https://doi.org/10.26634/jps.6.4.15845

Abstract

Conservation of natural resources by increasing the energy efficiency is the primary objective. With increment in the power requirement, there is substantial pressure on innovation and designers are required to take into account the upgrade energy efficient electrical systems. In the current circumstance, the need for energy is substantial, and control of resources is constrained; considering the mining business the foundation of development in any nation requires a great efforts on the sustenance of electric power. The principle objective of this study is the energy conservation in mines by supplanting conventional system with the current innovative systems that would greatly reduce utilization of electricity. In this study, the different electrical equipment that are utilized in the opencast coal mines are recorded with their details and operations. The recommendations are given as per the improvement of PF (Power Factor) of different machines using different customary techniques in the opencast mines where electrical vitality is being squandered. The protection process can be actualized in various mining perspectives and devices like brightening, haulers, transports, penetrating and impacting equipment's, cutting apparatuses and crushers, dragline, dumper, scoop, scrubber and other electrical equipment.

References

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[10]. Linkov, S. A., Olizarenko, V. V., Radionov, A. A., & Sarapulov, O. A. (2015). Energy-efficient power supply system for mines. Procedia Engineering, 129, 63-68. https://doi.org/10.1016/j.proeng.2015.12.009

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[12]. Sahoo, L. K., Bandyopadhyay, S., & Banerjee, R. (2013). Benchmarking energy efficiency of opencast mining in India. In 4^th International Conference on Advances in Energy Research (pp. 1684-1692).

[13]. Shuzhao, C., Qingxiang, C., Cangyan, X., & Haijun, W. (2011, February). Energy consumption comparison and energy saving measures of open pit development. In 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (pp. 712-715). IEEE. https://doi.org/10.1109/CDCIEM.2011.64

[14]. Sisay, A., Rao, A., & Gera, A. M. (2017). Power factor improvement in electric distribution system by using shunt capacitor case study on samara university ethiopia. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 6(8), 6264- 6270 http://www.ijareeie.com/upload/2017/august/49_ asefa%20paper.pdf

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Research Paper

Mitigation of Torsional Oscillations via Interval Type-2 Fuzzy Logic Based Chopper Rectifier Controlled Braking Resistor

Mohamed Fayez Ahmed* , M. Mandor, M. El-Hadidy*, F. Bendary****

* Cairo Electricity Production Company (CEPC), Cairo, Egypt.

, Electrical Engineering Department, Faculty of Engineering Shoubra, Benha University, Cairo, Egypt.

* Egyptian Electricity Holding Company (EEHC), Cairo, Egypt.

Fayez, M., Mandor, M., El-Hadidy, M., and Bendary, F. M. (2019). Mitigation of Torsional Oscillations via Interval Type-2 Fuzzy Logic Based Chopper Rectifier Controlled Braking Resistor. i-manager’s Journal on Power Systems Engineering, 6(4), 11-21. https://doi.org/10.26634/jps.6.4.16079

Abstract

Autoreclosrure is an indispensable protection scheme firstly designed for enhancing the power system stability especially in countries with high isokeraunic levels. Its unsuccessful operation would much more likely cause dire consequences on the thermal power plants in the vicinity. Therefore, almost all major utility-scale turbine-generator suppliers have been expressing concern about performing autoreclosing activities near the generator bus. A resistor brake interfaced to the grid through power electronic devices is a special FACTS controller. A more recent resistor model, namely Chopper Rectifier Controlled Braking Resistor controlled via Interval Type-2 fuzzy logic controller is proposed in this work for mitigation of torsional oscillations resulting from unsuccessful reclosure. Local control input signal synthesized from the generator shaft speed is employed in this work for the proposed controller. For testing the effectiveness of the proposed scheme, non-linear time-domain simulation study is performed on the Western System Coordinated Council, 3-machine 9-bus system via MATLAB/Simulink-based modelling and simulation platform. Comparative simulation studies of the test system after being subjected to unsuccessful reclosure of three-phase to ground fault condition should demonstrate the effectiveness of proposed scheme. From the time domain simulation results, the torsional torque profiles for both machines reach an excellent level due to the implementation of the proposed scheme.

References

[1]. Ahmed, M. F., Ebrahim, M. A., & Mansour, W. (2018). Torsional oscillations mitigation for interconnected power system via novel fuzzy control based braking resistor model. In 47^th International Council on Large Electric Systems Session, 1-9.

[2]. Anderson, P. M. (1999). Power System Protection. New York. IEEE Press.

[3]. Antão, R. (2017). Type-2 Fuzzy Logic: Uncertain Systems Modeling and Control. Singapore: Springer.

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[5]. Castillo, O., & Melin, P. (2008). Type-2 Fuzzy Logic: Theory and Applications (1^st Ed.). Berlin: Springer Verlag.

[6]. Eldamaty, A. (2005). Damping interarea and torsional oscillations using FACTS devices (Doctoral Dissertation), University of Saskatchewan, Canada.

[7]. EPRI Technical Report 1011679 (2005). Steam turbine-generator torsional vibration interaction with the electrical network: Tutorial. Electric Power Research Institute. Retrieved from https://www.epri.com/#/pages/ product/1011679/?lang=en-US

[8]. Halim, A., Bakar, A., & Imai, S. (2002, October). Auto-reclose performance on 275 kV and 132 kV transmission line in Malaysia. In IEEE/PES Transmission and Distribution Conference and Exhibition (Vol. 1, pp. 603-608). IEEE. https://doi.org/10.1109/TDC.2002.1178485

[9]. Helmy, S., El-Wakeel, A. S., Rahman, M. A., & Badr, M. A. (2012). Mitigating Subsynchronous resonance torques using dynamic braking resistor. Journal of Energy and Power Engineering, 6(5), 833-839.

[10]. Hingorani, N. G., & Gyugyi, L. (2000). Understanding FACTS: Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. New York: IEEE press.

[11]. IEC 60034-3 (2007). Rotating electrical machines – Part 3: Specific requirements for synchronous generators driven by steam turbines or combustion gas turbines. Retrieved from https://standards.globalspec.com/std/ 1034210/iec-60034-3

[12]. IEEE Standard C50.13 (2005). IEEE Standard for Cylindrical-Rotor 50 and 60 Hz Synchronous Generators Rated 10 MVA and Above. Retrieved from https://standards.ieee.org/standard/C50_13-2005.html

[13]. Kamel, S., Ziyad, B., Naguib, H. M., Mouloud, A., & Mohamed, R. (2015, December). An indirect adaptive type-2 fuzzy sliding mode PSS design to damp power system oscillations. In 2015 7^th International Conference on Modelling, Identification and Control (ICMIC) (pp. 1- 6). IEEE. https://doi.org/10.1109/ICMIC.2015.7409472

[14]. Klempner, G., & Kerszenbaum, I. (2008). Handbook of Large Turbo-Generator Operation and Maintenance (2^nd Ed.). New York: John Wiley.

[15]. Panda, M. K., Pillai, G. N., & Kumar, V. (2012, December). Power system stabilizer design: Interval typend 2^nd fuzzy logic controller approach. In 2012 2 International Conference on Power, Control and Embedded Systems (pp. 1-10). IEEE. https://doi.org/10.1109/ICPCES.2012. 6508097

[16]. Raju, S. K., & Pillai, G. N. (2015). Design and implementation of type-2 fuzzy logic controller for DFIGbased wind energy systems in distribution networks. IEEE Transactions on Sustainable Energy, 7(1), 345- 353.https://doi.org/10.1109/TSTE.2015.2496170

[17]. Saluja, R., & Ali, M. H. (2013, February). Novel braking resistor models for transient stability enhancement in power grid system. In 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT) (pp. 1-6). IEEE. https://doi.org/10.1109/ISGT.2013.6497838

[18]. Saluja, R., Ghosh, S., & Ali, M. H. (2013, April). Transient stability enhancement of multi-machine power system by novel braking resistor models. In 2013 Proceedings of IEEE Southeastcon (pp. 1-6). IEEE.

[19]. Saoudi, K., Bouchama, Z., Ayad, M., Benziane, M., & Harmas, M. N. (2016, November). Design of a robust PSS using an indirect adaptive type-2 fuzzy sliding mode for a multi-machine power system. In 2016 8^th International Conference on Modelling, Identification and Control (ICMIC) (pp. 713-718). IEEE. https://doi.org/10.1109/ ICMIC.2016.7804204

[20]. Sharma, A., Nagar, L. K., Patidar, N. P., Kolhe, M. L., Nandanwar, S. R., Puranik, V. N., & Singh, V. K. (2017, February). Minimizing uncertainties with improved power system stability using wide area fuzzy-2 logic based damping controller. In 2017 3^rdInternational Conference on Computational Intelligence & Communication Technology (CICT) (pp. 1-5). IEEE. https://doi.org/10.1109/ CIACT.2017.7977284

[21]. Taskin, A., & Kumbasar, T. (2015, December). An open source Matlab/Simulink toolbox for interval type-2 fuzzy logic systems. In 2015 IEEE Symposium Series on Computational Intelligence (pp. 1561-1568). IEEE. https://doi.org/10.1109/SSCI.2015.220

[22]. Tripathy, M., & Mishra, S. (2011). Interval type-2- based thyristor controlled series capacitor to improve power system stability. IET Generation, Transmission & Distribution, 5(2), 209-222. https://doi.org/10.1049/ietgtd. 2010.0035

[23]. Yagami, M., & Tamura, J. (2009, September). Power system stabilization by fault current limiter and thyristor controlled braking resistor. In 2009 IEEE Energy Conversion Congress and Exposition (pp. 548-553). IEEE. https://doi.org/10.1109/ECCE.2009.5316567

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Research Paper

Peak Power Tracking Technique for a Small-Scale Photovoltaic System

Amine Daoud*

* Department of Electronics, Faculty of Electrical Engineering, University of Sciences and Technology of Oran, Oran, Algeria.

Daoud, A. (2019). Peak Power Tracking Technique for a Small-Scale Photovoltaic System. i-manager’s Journal on Power Systems Engineering, 6(4), 22-36. https://doi.org/10.26634/jps.6.4.16047

Abstract

A photovoltaic (PV) generator can directly transform the sun's rays into usable electric power. The power-voltage characteristic of PV generator is highly nonlinear and its optimal power point varies with sunlight intensity and temperature. Thus, to increase the efficiency of a PV system, it is important to track the optimal power point instantly. This paper presents a simple variable step-size maximum power point tracking (MPPT) technique for a small-scale PV system. The latter is composed of a PV array, a DC/DC power converter, and a DC motor-pump. Furthermore, using this technique, only the output voltage of the switching converter needs to be sensed in order to track the optimal power point. Compared with classical Perturbation & Observation (P&O) technique, the proposed MPPT technique can largely improve the MPPT efficiency and the total volume of water pumped a day. Also for comparison purpose, the artificial neural network (ANN) based MPPT technique is addressed in this manuscript. Moreover, the MPPT techniques considered in this study are applied to a solar-powered water pumping system under different climate conditions. Finally, mathematical modeling and computer simulations of such small-scale PV system are performed using the MATLAB environment.

References

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[2]. Belkaid, A., Colak, I., & Isik, O. (2016). Photovoltaic maximum power point tracking under fast varying of solar radiation. Applied Energy, 179, 523-530. https://doi.org/10.1016/j.apenergy.2016.07.034

[3]. Chen, P. C., Chen, P. Y., Liu, Y. H., Chen, J. H., & Luo, Y. F. (2015). A comparative study on maximum power point tracking techniques for photovoltaic generation systems operating under fast changing environments. Solar Energy, 119, 261-276. https://doi.org/10.1016/j.solener. 2015.07.006

[4]. Daud, A. K., & Mahmoud, M. M. (2005). Solar powered induction motor-driven water pump operating on a desert well, simulation and field tests. Renewable Energy, 30(5), 701-714. https://doi.org/10.1016/j.renene.2004.02.016

[5]. El Khazane, J., & Tissir, E. H. (2018). Achievement of MPPT by finite time convergence sliding mode control for photovoltaic pumping system. Solar Energy, 166, 13-20. https://doi.org/10.1016/j.solener.2018.03.026

[6]. Eltawil, M. A., & Zhao, Z. (2013). MPPT techniques for photovoltaic applications. Renewable and Sustainable Energy Reviews, 25, 793-813. https://doi.org/10.1016/j. rser.2013.05.022

[7]. Enany, M. A., Farahat, M. A., & Nasr, A. (2016). Modeling and evaluation of main maximum power point tracking algorithms for photovoltaics systems. Renewable and Sustainable Energy Reviews, 58, 1578-1586. https://doi.org/10.1016/j.rser.2015.12.356

[8]. Femia, N., Petrone, G., & Spagnuolo, G. (2012). Power Electronics and Control Techniques for Maximum Energy Harvesting in Photovoltaic Systems. Florida: CRC Press.

[9]. Huang, Y. P., & Hsu, S. Y. (2016). A performance evaluation model of a high concentration photovoltaic module with a fractional open circuit voltage-based maximum power point tracking algorithm. Computers & Electrical Engineering, 51, 331-342. https://doi.org/ 10.1016/j.compeleceng.2016.01.009

[10]. Jafar, M. (2000). A model for small-scale photovoltaic solar water pumping. Renewable Energy, 19(1-2), 85-90. https://doi.org/10.1016/S0960-1481 (99)00020-8

[11]. Jiang, Y., Qahouq, J. A. A., & Haskew, T. A. (2012). Adaptive step size with adaptive-perturbation-frequency digital MPPT controller for a single-sensor photovoltaic solar system. IEEE Transactions on Power Electronics, 28(7), 3195-3205. https://doi.org/10.1109/TPEL.2012. 2220158

[12]. Karanjkar, D. S., Chatterji, S., Shimi, S. L., & Kumar, A. (2014, March). Real time simulation and analysis of maximum power point tracking (MPPT) techniques for solar photo-voltaic system. In 2014 Recent Advances in Engineering and Computational Sciences (RAECS) (pp. 1- 6). IEEE. https://doi.org/10.1109/RAECS.2014.6799656

[13]. Lyden, S., & Haque, M. E. (2015). Maximum Power Point Tracking techniques for photovoltaic systems: A comprehensive review and comparative analysis. Renewable and Sustainable Energy Reviews, 52, 1504- 1518. https://doi.org/10.1016/j.rser.2015.07.172

[14]. Matam, M., Barry, V. R., & Govind, A. R. (2018). Optimized Reconfigurable PV array based Photovoltaic water-pumping system. Solar Energy, 170, 1063-1073. https://doi.org/10.1016/j.solener.2018.05.046

[15]. Messalti, S., Harrag, A., & Loukriz, A. (2017). A new variable step size neural networks MPPT controller: Review, simulation and hardware implementation. Renewable and Sustainable Energy Reviews, 68, 221-233.

[16]. Muhsen, D. H., Khatib, T., & Nagi, F. (2017). A review of photovoltaic water pumping system designing methods, control strategies and field performance. Renewable and Sustainable Energy Review, 68(1), 70-86. https://doi.org/ 10.1016/j.rser.2016.09.131

[17]. Rashid, M. H. (Ed.) (2015). Alternative Energy in Power Electronics. Florida: Butterworth-Heinemann-Elsevier. https://doi.org/10.1016/C2012-0-07333-4

[18]. Rashid, M. H. (Ed.) (2018). Power Electronics 3^rd Handbook ( Ed.). Florida: Butterworth-Heinemann- Elsevier.

[19]. Rai, A. K., Kaushika, N. D., Singh, B., & Agarwal, N. (2011). Simulation model of ANN based maximum power point tracking controller for solar PV system. Solar Energy Materials and Solar Cells, 95(2), 773-778. https://doi.org/ 10.1016/j.solmat.2010.10.022

[20]. Yahyaoui, I., Chaabene, M., & Tadeo, F. (2016). Evaluation of Maximum Power Point Tracking algorithm for off-grid photovoltaic pumping. Sustainable Cities and Society, 25, 65-73. https://doi.org/10.1016/j.scs.2015.11. 005

[21]. Zhao, J., Zhou, X., Ma, Y., & Liu, W. (2015). A novel maximum power point tracking strategy based on optimal voltage control for photovoltaic systems under variable environmental conditions. Solar Energy, 122, 640-649. https://doi.org/10.1016/j.solener.2015.09.040

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Research Paper

Distribution Network Reconfiguration using GA & BPSO

P. V. Prasad* , M. Balasubba Reddy**

*_** Department of Electrical and Electronics Engineering, Chaitanya Bharathi Institute of Technology(A), Hyderabad, India.

Prasad, P.V., and Reddy, M. B. S. (2019). Distribution Network Reconfiguration using GA & BPSO. i-manager’s Journal on Power Systems Engineering, 6(4), 37-44. https://doi.org/10.26634/jps.6.4.15802

Abstract

As increase in the high demand utilization of electrical energy over few decades the power loss issue is persevered. In order to minimize the power losses, various methods are followed in distribution system such as capacitor placement, distribution generator placement and proper conductor selection methods. In all these methods, lot of money is to be invested to decrease the losses. The network reconfiguration method is one, where investment for loss reduction is minimum. By changing the position of sectionalizing and the tie switches the distribution system reconfiguration is done to minimize the power losses. In this paper, Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) techniques are used for distribution system reconfiguration. The performance of the two algorithms is tested with two different test systems i.e. 33 and 69 node radial distribution systems. The outcomes illustrate that after reconfiguration the power loss is minimized and voltage profile is improved. Finally the results of the two algorithms are compared and found that BPSO has given better results compared to Genetic Algorithm.

References

[1]. Abdelaziz, A. Y., Mohammed, F. M., Mekhamer, S. F., & Badr, M. A. L. (2009). A modified particle swarm optimization technique for distribution systems reconfiguration. The Online Journal on Electronics and Electrical Engineering (OJEEE), 1(2), 121-129.

[2]. Amanulla, B., Chakrabarti, S., & Singh, S. N. (2012). Reconfiguration of power distribution systems considering reliability and power loss. IEEE Transactions on Power Delivery, 27(2), 918-926. https://doi.org/10.1109/TPWRD. 2011.2179950

[3]. Franken, N., & Engelbrecht, A. P. (2005). Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma. IEEE Transactions on evolutionary computation, 9(6), 562-579. https://doi.org/ 10.1109/TEVC.2005.856202

[4]. Hu, Y., Hua, N., Wang, C., Gong, J., & Li, X. (2010, August). Research on distribution network reconfiguration. In 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, Vol. 1, (pp. 176-180). IEEE. https://doi.org/10.1109/CMCE. 2010.5610464

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[8]. Rao, P. R., & Sivanagaraju, S. (2010). Radial distribution network reconfiguration for loss reduction and load balancing using plant growth simulation algorithm. International Journal on Electrical Engineering and Informatics, 2(4), 266-277.

[9]. Ravibabu, P., Ramya, M. V. S., Sandeep, R., Karthik, M. V., & Harsha, S. (2010, April). Implementation of improved genetic algorithm in distribution system with feeder reconfiguration to minimize real power losses. In 2010 2^nd International Conference on Computer Engineering and Technology, Vol. 4, (pp. V4-320). IEEE. https://doi.org/10. 1109/ICCET.2010.5485563

[10]. Sadri, J., & Suen, C. Y. (2006, July). A genetic binary particle swarm optimization model. In 2006 IEEE International Conference on Evolutionary Computation (pp. 656-663). IEEE. https://doi.org/10.1109/CEC.2006.16 88373

[11]. Shamsudin, N. H., Omar, N. F., Sulaima, M. F., Jaafar, H. I., & Kadir, A. F. A. (2014). The distribution network reconfiguration improved performance of genetic algorithm considering power losses and voltage profile. International Journal of Engineering and Technology, 6(2), 1247-1258.

[12]. Taşgetiren, M. F., & Liang, Y. C. (2003). A binary particle swarm optimization algorithm for lot sizing problem. Journal of Economic and Social Research, 5(2), 1-20.

[13]. Vitorino, R. M., Neves, L. P., & Jorge, H. M. (2009, June). Network reconfiguration to improve reliability and efficiency in distribution systems. In 2009 IEEE Bucharest PowerTech (pp. 1-7). IEEE. https://doi.org/110.1109/ PTC.2009.5281806

[14]. Zhang, C., & Hu, H. (2005, October). Using PSO algorithm to evolve an optimum input subset for a SVM in time series forecasting. In 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, (pp. 3793-3796). IEEE. https://doi.org/10.1109/ICSMC.2005. 1571737

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Research Paper

Maximum Power Point Tracking in Solar PV systems using Artificial Neural Networks

Jyotsana Pandey*

* Department of Electrical Engineering, Raipur Institute of Technology, Raipur, Chhattisgarh, India.

Pandey, J. (2019). Maximum Power Point Tracking in Solar PV systems using Artificial Neural Networks i-manager’s Journal on Power Systems Engineering, 6(4), 45-52. https://doi.org/10.26634/jps.6.4.16409

Abstract

Maximum power point tracking (MPPT) is critical in the design and use of solar PV cells. However, attaining MPPT is often challenging due to the random fluctuations in solar irradiation. Off late Artificial Neural Networks (ANN) are being used for maximum power point tracking of solar PV cells. In the proposed work, the Levenberg-Marqardt (LM) algorithm has been used to train a neural network with training features. Subsequently, the neural network is tested and an accuracy of 98.84% has been achieved. The high accuracy can be attributed to the structuring of the training data and the effectiveness of the Levenberg Marqardt back-propagation algorithm which is both fast and stable. The performance of the system has been evaluated in terms of the number of epochs for training, the mean absolute percentage error, accuracy and regression.

References

[1]. Algarín, R. C., Hernández, S. D., & Leal, D. R. (2018). A low-cost maximum power point tracking system based on neural network inverse model controller. Electronics, 7(1), 4. https://doi.org/10.3390/electronics 7010004

[2]. Armghan, H., Ahmad, I., Armghan, A., Khan, S., & Arsalan, M. (2018). Backstepping based non-linear control for maximum power point tracking in photovoltaic system. Solar Energy, 159, 134-141. https://doi.org/10. 1016/j.solener.2017.10.062

[3]. Asrari, A., Wu, T. X., & Ramos, B. (2016). A hybrid algorithm for short-term solar power prediction-Sunshine state case study. IEEE Transactions on Sustainable Energy, 8(2), 582-591. https://doi.org/10.1109/TSTE.2016. 2613962

[4]. Cammarano, A., Petrioli, C., & Spenza, D. (2012, October). Pro-Energy: A novel energy prediction model for solar and wind energy-harvesting wireless sensor networks. In 2012 IEEE 9^th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012) (pp. 75- 83). IEEE. https://doi.org/10.1109/MASS.2012.6502504

[5]. Deng, W., Liu, F., Jin, H., Li, B., & Li, D. (2014). Harnessing renewable energy in cloud datacenters: Opportunities and challenges. IEEE Network, 28(1), 48-55. https://doi.org/10.1109/MNET.2014.6724106

[6]. Huang, R., Huang, T., Gadh, R., & Li, N. (2012, November). Solar generation prediction using the ARMA model in a laboratory-level micro-grid. In 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm) (pp. 528-533). IEEE.

[7]. Kosmopoulos, P. G., Kazadzis, S., Lagouvardos, K., Kotroni, V., & Bais, A. (2015). Solar energy prediction and verification using operational model forecasts and ground-based solar measurements. Energy, 93, 1918- 1930. https://doi.org/10.1016/j.energy.2015.10.054

[8]. Ma, T., Yang, H., & Lu, L. (2014). Solar photovoltaic system modeling and per formance prediction. Renewable and Sustainable Energy Reviews, 36, 304- 315. https://doi.org/10.1016/j.rser.2014.04.057

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i-manager's Journal on Power Systems Engineering (JPS)

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Volume 6 Issue 4 November - January 2019

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*, ***_***** CSIR-Central Institute of Mining & Fuel Research, Dhanbad, Jharkhand, India. ** M. Tech. Scholar of Mine Machinery, IIT-ISMS, Dhanbad, Jharkhand, India

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Maximum Power Point Tracking in Solar PV systems using Artificial Neural Networks

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Virendra Kumar* , Arnab Bhuiya , Aditi Chatterjee, S. K. Chaulya, Gautam Banerjee

, _* CSIR-Central Institute of Mining & Fuel Research, Dhanbad, Jharkhand, India.

M. Tech. Scholar of Mine Machinery, IIT-ISMS, Dhanbad, Jharkhand, India

Mohamed Fayez Ahmed* , M. Mandor, M. El-Hadidy*, F. Bendary****

* Cairo Electricity Production Company (CEPC), Cairo, Egypt.

, Electrical Engineering Department, Faculty of Engineering Shoubra, Benha University, Cairo, Egypt.

* Egyptian Electricity Holding Company (EEHC), Cairo, Egypt.