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 5 Issue 3 August - October 2017

Research Paper

Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions

Mehmet Ozkaymak* , Abdulsamed Tabak, Ahmet Canan*, Mehmet Volkan Aksay**, Ozgur Inanc*, Ummu Gulsum Eruz****

* Professor, Department of Energy Systems Engineering, KarabÃ¼k University, KarabÃ¼k, Turkey.

-* Research Assistant, Department of Energy Systems Engineering, KarabÃ¼k University, KarabÃ¼k, Turkey.

**** Ph.D. Scholar, Department of Energy Systems Engineering, KarabÃ¼k University, KarabÃ¼k, Turkey.

Özkaymak, M., Tabak, A., Canan, A., Aksay, M. V., Inanç, Ö., and Eruz, Ü. G. (2017). Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions. i-manager’s Journal on Power Systems Engineering, 5(3), 1-9. https://doi.org/10.26634/jps.5.3.13666

Abstract

Within the scope of this study, the efficiency of Monocrystalline, polycrystalline, and thin film photovoltaic panels have been experimentally investigated in Karabük conditions. In the frame of the research, the efficiencies of the panels have been compared by using the amount of incoming radiation, current, voltage, and the amount of power generated by the panel. When the datasheets of panels are viewed, efficiency comparison has seen as monocrystalline, polycrystalline, and thin film, respectively. According to the experiments made, while the efficiency of monocrystalline panel at 250 Watt varies between 12-16%; the efficiency of polycrystalline panel increase nearly 21% based on the amount of radiation; whereas it has been seen that, the efficiency of thin film couldn't exceed nearly 5%. During the measurements, if the air temperature is considered between 30-35 ^oC, the efficiency of thin film estimated will be low. In Turkey conditions, when the unit price of these panels is considered, using polycrystalline panel for the investments seems much more attractive. As the efficiencies of thin film are low, it has been the last one in comparison. When it is considered that, the investors especially hesitate between polycrystalline and monocrystalline panels, experimental results give a reasonable answer.

References

[1]. Basoglu, M. E., Kazdaloglu A., Bilgin, M. Z., Erfidan, T., & Çakir, B. (2014). Performance Evaluation of Different Solar Panel Technologies for Kocaeli Province. Bursa: Elektrik-Elektronik-Bilgisayarve Biyomedikal Mühendisligi Sempozyumu, 27-29 Kasim.

[2]. Cohen, M.L., & Chelikowsky, J.R. (1988). Electronic Structure and Optical Properties of Semiconductors. Vol.75. Gewerbestrasse: Springer Series in Solid-State Sciences.

[3]. Directorate General of Renewable Energy. (2016 May 22). Ministry of Energy and Natural Resources. Retrieved from http://www.eie.gov.tr/yenilenebilir/g_enj_tekno.aspx

[4]. Erkul, A. (2010). Monokristal, PolikristalveAmorf- Silisyum Gunes Panelleri VerimliligininIncelenmesive Aydinlatma Sistemi Uygulamasi. GaziUniversitesi Fen Bilimleri Enstitusu YuksekLisans Tezi.

[5]. Luque, A., & Hegedus, S. (Eds.). (2011). Handbook of photovoltaic Science and Engineering. John Wiley & Sons.

[6]. Möller, H. J., Funke, C., Rinio, M., & Scholz, S. (2005). Multicrystalline silicon for solar cells. Thin Solid Films, 487(1), 179-187.

[7].Oguz, Y., Karaka n, A., Uslu , B. ( 2015 ). Afyonkarahisar’ da Kurulu Olan Monokristal, PolikristalveInce Film Günes Panellerinin VerimliligininIncelenmesi, TesisatMühendisligi, Sayi 149.

[8]. Prasad, D. K., & Snow, M. (2005). Designing with Solar Power, A Source Book for Building Integrated Photovoltaics (BIPV). Australia: Images Publishing.

[9]. Roberts, S., & Guariento, N. (2009). Building Integrated Photovoltaics: A Handbook. Berlin Germany: Birkhauser Press.

[10]. Tabak, A., & Endiz, M. S. (2016). The Comparative Analyzes of Solar Energy Production Potential between Van and Antalya Using PVSOL Simulation Tool. i-manager's Journal on Instrumentation & Control Engineering, 4(3), 1- 6.

[11]. Thomas, R., & Fordham, M. (2001). Photovoltaics and Architecture. London and Newyork: Spon Press.

[12]. Upadhyaya, A.,Yelundur V., & Rohatg. A. (2006). High Efficiency Mono-Crystalline Solar Cells with Simple Manufacturable Technology. Gerorgia Institute of Technology.

Full Article (HTML)

Pdf

Research Paper

Sine Cosine Algorithm for Loss Reduction in Distribution System with Unified Power Quality Conditioner

M. Laxmidevi Ramanaiah* , M. Damodar Reddy**

* Research Scholar, Department of Electrical and Electronics Engineering, Sri Venkateswara. University, Tirupati, Andhra Pradesh, India.

** Professor, Department of Electrical and Electronics Engineering, Sri Venkatesswara University, Tirupati, Andhra Pradesh, India.

Ramanaiah, M. L., and Reddy, M. D. (2017). Sine Cosine Algorithm for Loss Reduction in Distribution System with Unified Power Quality Conditioner. i-manager’s Journal on Power Systems Engineering, 5(3), 10-16. https://doi.org/10.26634/jps.5.3.13667

Abstract

This paper presents a novel concept of Unified Power Quality Conditioner (UPQC). In steady state conditions, the series compensator is in idle mode and the shunt compensator is involved in reactive power compensation. This paper addresses the reactive power compensation capability of both the compensators in steady state conditions. The problem of UPQC placement is formulated as a single objective problem. UPQC allocation in the distribution system is addressed by Sine Cosine Algorithm (SCA) with the objective of minimizing the losses and improving the voltage profile. Suitable Load Flow method is applied to incorporate the complex voltage and reactive power injected by the series and shunt compensator, respectively. The proposed algorithm is tested on standard distribution systems.

References

[1]. Baran, M. E., & Wu, F. F. (1989a). Network Reconfiguration in Distribution Systems for Loss Reduction and Load Balancing. IEEE Transactions on Power Delivery, 4(2), 1401-1407.

[2]. Baran, M. E., & Wu, F. F. (1989b). Optimal Capacitor Placement on Radial Distribution Systems. IEEE Transactions on Power Delivery, 4(1), 725-734.

[3]. Boutebel, M., & Mokrani, K. (2014). Optimal Steady State Operation of Unified Power Quality Conditionner for Power Losses Reduction and Voltage Regulation Improvement. Journal of Electrical Engineering, 14(1), 114- 121.

[4]. Das, D., Kothari, D. P., & Kalam, A. (1995). Simple and efficient method for Load Flow Solution of Radial Distribution Networks. International Journal of Electrical Power & Energy Systems, 17(5), 335-346.

[5]. Fujita, H., & Akagi, H. (1998). The unified power quality conditioner: The integration of series-and shunt-active filters. IEEE Transactions on Power Electronics, 13(2), 315- 322.

[6]. Ganguly, S. (2014). Unified power quality conditioner allocation for reactive power compensation of radial Distribution Networks. IET Generation, Transmission & Distribution, 8(8), 1418-1429.

[7]. Gupta, A. R., & Kumar, A. (2017). Performance Analysis of Radial Distribution Systems with UPQC and D-STATCOM. Journal of The Institution of Engineers (India): Series B, 98(4), 415-422.

[8]. Hosseini, M., Shayanfar, H. A., & Fotuhi-Firuzabad, M. (2009). Modeling of unified power quality conditioner (UPQC) in Distribution Systems Load Flow. Energy Conversion and Management, 50(6), 1578-1585.

[9]. Mirjalili, S. (2016). SCA: A Sine Cosine Algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.

[10]. Reddy, M. D., & Reddy, V. C. (2008). A two-stage methodology of optimal capacitor placement for the reconfigured network. Indian Journal of Engineering and Material Sciences, 17, 105-112.

[11]. Taher, S. A., & Afsari, S. A. (2012). Optimal Location and Sizing of UPQC in Distribution Networks using Differential Evolution Algorithm. Mathematical Problems in Engineering, 2012.

[12]. Teng, J. H. (2003). A Direct Approach for Distribution System Load Flow Solutions. IEEE Transactions on Power Delivery, 18(3), 882-887.

Full Article (HTML)

Pdf

Research Paper

Optimizing Load Dispatch using Flower Pollination Algorithm Technique

Saurabh Yadav* , S. K. Gupta**

* PG Scholar, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.

** Professor, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.

Yadav, S., and Gupta, S. K. (2017). Optimizing Load Dispatch using Flower Pollination Algorithm Technique. i-manager’s Journal on Power Systems Engineering, 5(3), 17-23. https://doi.org/10.26634/jps.5.3.13668

Abstract

For the operational planning of power system, Economic Load Dispatch (ELD) problem is to be optimized. This paper presents an effective and reliable Flower Pollination Algorithm (FPA) technique for the economic load dispatch problem. Flower Pollination Algorithm is applied to determine the optimal schedule of power generation in a thermal power system. The results are calculated using the ELD of standard 3-generator and 6-generator systems with all the equality and inequality constraints. It is found that the results obtained by using the FPA technique are better than the conventional technique and the Particle Swarm Optimization (PSO) in terms of fuel cost and computation time.

References

[1]. Ahmad, A., Singh, N., & Varshney, T. (2011). A New Approach for Solving Economic Load Dispatch Problem. MIT International Journal of Electrical and Instrumentation Engineering, 1(2), 93-98.

[2]. Bakirtzis, A., Petridis, V., & Kazarlis, S. (1994). Genetic Algorithm Solution to the Economic Dispatch Problem. IEE Proc. Generation, Transmission, and Distribution, 141(4), 377-382.

[3]. Chang, S., Chen, C., FongI, & Luh, P. B. (1990). Hydroelectric generation scheduling with an effective differential programming. IEEE Transactions PWRS, 5(3), 737-743.

[4]. Danaraj, R. M. S., & Gajendran F. (2005). Quadratic Programming Solution to Emission and Economic Dispatch Problems. Journal of the Institution of Engineers (India), 86(p), 129-132.

[5]. Gaing, Z. L. (2003). Particle Swarm Optimization to Solving the Economic Dispatch considering the Generator Constraints. IEEE Transactions on Power Systems, 18(3), 1187-1195.

[6]. Gupta, S. K., Malik, M., & Gupta, D. (2017). Application of Accelerated PSO and ANN for optimal Scheduling of Hydrothermal system. Journal of Power Electronics & Power Systems, 6(3), 42-52.

[7]. Gupta, S. K., & Chawala, P. (2015). Economic Load Dispatch in Thermal Power Plant considering Additional Constraints using Curve Fitting and ANN. Review of Energy Technologies and Policy Research, 2(1), 16-28.

[8]. Hardiansyah, Junaidi, Yohannes, M. S. (2012). Solving Economic Load Dispatch Problem using Particle Swarm Optimization Technique. I. J. Intelligent Systems and Applications, 12, 12-18.

[9]. Kennedy, J., & Eberhart, R. C. (1995). Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks (ICNN'95), (Vol.4, pp.1942-1948).

[10]. LeeK, Y., Sode-Yome, A., & Park, J. H. (1998). Adaptive Hopfield Neural Network for Economic Load Dispatch. IEEE Trans. on Power Systems, 13(2), 519-526.

[11]. Liang, Z. X., & Glover, J. D. (1992). A Zoom Feature for a Dynamic Programming Solution to Economic Dispatch Including Transmission Losses. IEEE Transactions on Power Systems, 7(2), 544-550.

[12]. Lim, S. Y., Montakhab, M., & Nouri, H. (2009). Economic Dispatch of Power System using Particle Swarm Optimization with Constriction Factor. International Journal of Innovations in Energy Systems and Power, 4(2), 29-34.

[13]. Park, J. B., Lee, K. S., Shin, J. R., & Lee, K. Y. (2005). A Particle Swarm Optimization for Economic Dispatch with Non smooth Cost Functions. IEEE Transactions on Power Systems, 20(1), 34-42.

[14]. Pavlyukevich I. (2007). Levy flight, non-local search and simulated annealing. J. Comput. Phys., 226(1), 1830- 1844.

[15]. Saadat H. (2002). Power System Analysis. Tata McGraw Hill Publishing Company, New Delhi.

[16]. Sudhakaran, M., Ajay, P., Raj, D. V., & Palanivelu, T. G. (2007). Application of Particle Swarm Optimization for Economic Load Dispatch Problems. The 14^th International Conference on Intelligent System Applications to Power Systems. pp.668-674, Kaohsiung, Taiwan.

[17]. Vanitha, M., & Tanushkodi, K. (2011). Solution to Economic Dispatch Problem By Differential Evolution Algorithm considering Linear Equality and Inequality Constraints. International Journal of Research and Reviews in Electrical and Computer Engineering, 1(1), 21-26.

[18]. Walters, D., Sheble, C., & Gerald, B. (1993). Genetic Algorithm solution of Economic Dispatch with Valve Point- Loading. IEEE Transactions on Power System, 8.

[19]. Wood, A. J., & Wollenberg, B. F. (1984). Power Generation, Operation, and Control. John Wiley and Sons, New York.

[20]. Yang, X. (2012). Flower Pollination Algorithm for Global Optimization. In Springer, Heidelberg. Durand- Lose, J., Jonoska, N. (Eds.) UCNC 2012. LNCS, 7445, 240- 249.

Full Article (HTML)

Pdf

Research Paper

A Modified BFOA Approach for Optimal Location and Sizing of Facts for Enhancing Power System Security

T. N. V. L. N. Kumar* , R. V. S. Satyanarayana**

*Professor and Head, Department of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, India.

** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.

Kumar, T. N. V. L. N., and Satyanarayana, R. V. S. (2017). A Modified BFOA Approach for Optimal Location and Sizing of Facts for Enhancing Power System Security. i-manager’s Journal on Power Systems Engineering, 5(3), 24-33. https://doi.org/10.26634/jps.5.3.13669

Abstract

Power system planners have come to rely on FACTS devices to overcome several operational limitations in terms of thermal stability, voltage stability, and other inherent limitations offered by the transmission lines. One important aspect that has always intrigued the power system planners and researches alike is in regard to the location and sizing of FACTS devices. This work aims to address this intriguing question by identifying the optimal location and size of the Static Var Compensator (SVC). The optimal location and size is proposed to be identified by optimizing the multi-objective function, formulated by different factors that define the system security, namely Voltage Deviation, System Overload, and Real Power Losses. The multi-objective optimization function has been optimized using a Modified Bacterial Foraging Optimization Algorithm (MBFOA). The results are presented and analysed for an IEEE 30 bus test system.

References

[1]. Adebayo, I. G., Bhaskhar, M. A., Yusuff, A. A., & Jimoh, A. A. (2016). Optimal location identification of FACTS devices through genetic algorithm and the network structural characteristics techniques. In Renewable Energy Research and Applications (ICRERA), 2016 IEEE International Conference on (pp. 778-782). IEEE.

[2]. Archana, N., & Vidhyapriya, R. (2016). Location of multi-type facts devices under contingency: An intelligent appproach using modified ABC. In Advances in Electrical, Electronic and Systems Engineering (ICAEES), International Conference on (pp. 477-483). IEEE.

[3]. Dixit, S., Agnihotri, G., Srivastava, L., & Singh, A. (2014). An Overview of Placement of TCSC for Enhancement of Power System Stability. In Computational Intelligence and Communication Networks (CICN), 2014 International Conference on (pp. 1184-1187). IEEE.

[4]. Dorigo, M., & Gambardella, L. M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.

[5]. Eissa, M. M., Abdel-Hameed, T. S., & Gabbar, H. (2013). A novel approach for optimum allocation of Flexible AC Transmission Systems using Harmony Search technique. In Smart Energy Grid Engineering (SEGE), 2013 IEEE International Conference on (pp. 1-6). IEEE.

[6]. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. An Introductory Analysis with Application to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press.

[7]. Kennedy, J., & Eberhart, R. (1995). Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks IV (Vol. 1000, pp.1942- 1948). IEEE.

[8]. Kulkarni, P. P., & Ghawghawe, N. D. (2015). Optimal placement and parameter setting of TCSC in power transmission system to increase the power transfer capability. In Energy Systems and Applications, 2015 International Conference on (pp. 735-739). IEEE.

[9]. Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52-67.

[10]. Power Systems Test Case Archive. Retrieved from https://www2.ee.washington.edu/research/pstca/

[11]. Sekhar, A. H., & Devi, A. L. (2016). Voltage profile improvement and power system losses reduction with multi TCSC placement in transmission system by using firing angle control model with Heuristic Algorithms. In Signal Processing, Communication, Power and Embedded System (SCOPES), 2016 International Conference on (pp. 295-301). IEEE.

[12]. Selvarasu, R., Kalavathi, S.M., Rajan, A., & Christober, C. (2013). SVC placement for voltage constrained loss minimization using Self-adaptive Firefly Algorithm. Archives of Electrical Engineering, 62(4), 649- 661.

[13]. Shchetinin, D., & Hug, G. (2014). Optimal TCSC allocation in a power system for risk minimization. In North American Power Symposium (NAPS), 2014 (pp. 1-6). IEEE.

[14]. Sheth, A., Kotwal, C. D., & Pujara, S. (2015). Optimal placement of TCSC for improvement of static voltage stability. In Engineering (NUiCONE), 2015 5 Nirma University International Conference on (pp. 1-6). IEEE.

[15]. Srivastava, L., Dixit, S., & Agnihotri, G. (2014). Optimal location and size of TCSC for voltage stability enhancement using PSO-TVAC. In Power and Energy Systems Conference: Towards Sustainable Energy, 2014 (pp. 1-6). IEEE.

[16]. Storn, R., & Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11(4), 341-359.

[17]. Telang, A. S., & Bedekar, P. P. (2016). Systematic approach for optimal placement and sizing of STATCOM to assess the voltage stability. In Circuit, Power and Computing Technologies (ICCPCT), 2016 International Conference on (pp. 1-6). IEEE.

[18]. Zeinhom, A. N. (2016). Optimal sizing and allocation of Unified Power Flow Controller (UPFC) for enhancement of Saudi Arabian interconnected grid using Genetic Algorithm (GA). In Smart Grid (SASG), 2016 Saudi Arabia (pp. 1-6). IEEE.

[19]. Zimmermann, R., & Murillo-Sanchez, C. (2015). MATPOWER 5.1, User's Manual. Technical Report, Power Systems Engineering Research Center. Retrieved from http://www.pserc.cornell.edu//matpower/manual.pdf

Full Article (HTML)

Pdf

Research Paper

Analysis of Optimal Size and Location of Distributed Generation for Various Inertia Weights using IPSO Algorithm

Lavanya Nalla* , N. Prema Kumar**

* PG Scholar, Department of Electrical Engineering, Andhra University, Visakhapatnam, India.

** Professor, Department of Electrical Engineering, Andhra University, Visakhapatnam, India.

Nalla, L., and Navuri, P. K. (2017). Analysis of Optimal Size and Location of Distributed Generation for Various Inertia Weights using IPSO Algorithm. i-manager’s Journal on Power Systems Engineering, 5(3), 34-40. https://doi.org/10.26634/jps.5.3.13670

Abstract

This paper presents multi objective optimization in Radial Distribution System (RDS). Optimal size and location of Distributed Generation (DG) plays an important role in reduction of real power loss and voltage improvement in RDS. The optimal location of DG will give system reliability and stability, and which leads to improved performance of the system. The proposed PSO method is introduced by various inertia weights strategies and carried out through inertia weighted Particle Swarm Optimization (IPSO) by considering technical aspects. The technical indices real power index, reactive power index, and voltage profile index are discussed over here. These indices are analysed with various inertia weight strategies and how they impact the DG size and DG location. This approach is tested on standard IEEE 33 bus system.

References

[1]. Abu-Mouti, F. S., & El-Hawary, M. E. (2009, October). Modified Artificial Bee Colony Algorithm for Optimal Distributed Generation Sizing and Allocation in Distribution systems. In Electrical Power & Energy Conference (EPEC), 2009 IEEE (pp. 1-9). IEEE.

[2]. Ahmad, N. A., Musirin, I., & Sulaiman, S. I. (2014, March). Exponential based PSO performed on DG installation for loss minimization considering THD. In Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8^th International (pp. 607-612). IEEE.

[3]. Bansal, J. C., Singh, P. K., Saraswat, M., Verma, A., Jadon, S. S., & Abraham, A. (2011, October). Inertia weight strategies in Particle Swarm Optimization. In Nature and Biologically Inspired Computing (NaBIC), 2011 Third World Congress on (pp. 633-640). IEEE.

[4]. Diaz-Dorado, E., Cidrás, J., & Míguez, E. (2002). Application of Evolutionary Algorithms for the Planning of Urban Distribution Networks of Medium Voltage. IEEE Transactions on Power Systems, 17(3), 879-884.

[5]. Doostan, M., Navaratnan, S., Mohajeryami, S., & Cecchi, V. (2016, September). Concurrent placement of distributed generation resources and capacitor banks in distribution systems. In North American Power Symposium (NAPS), 2016 (pp. 1-6). IEEE.

[6]. Eberhart, R., & Kennedy, J. (1995, October). A New Optimizer using Particle Swarm Theory. In Micro Machine and Human Science, 1995. MHS'95., Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.

[7]. Feng, Y., Teng, G. F., Wang, A. X., & Yao, Y. M. (2007, September). Chaotic Inertia Weight in Particle Swarm Optimization. In Innovative Computing, Information and Control, 2007. ICICIC'07. Second International Conference on (pp. 475). IEEE.

[8]. Imran, A. M., & Kowsalya, M. (2014). Optimal size and siting of multiple distributed generators in Distribution System using Bacterial Foraging Optimization. Swarm and Evolutionary computation, 15, 58-65.

[9]. Jain, N., Singh, S. N., & Srivastava, S. C. (2010, December). Particle Swarm Optimization based method for optimal siting and sizing of multiple Distributed Generators. In Proceedings of 16^th National Power Systems Conference (pp. 669-674).

[10]. Kansal, S., Sai, B. B. R., Tyagi, B., & Kumar, V. (2011). Optimal Placement of Distributed Generation in Distribution Networks. International Journal of Engineering, Science and Technology, 3(3), 47-55.

[11]. Kenndy, J., & Eberhart, R. C. (1995, December). Particle Swarm Optimization. In Proceedings of IEEE International Conference on Neural Networks (Vol. 4, pp. 1942-1948). IEEE Press.

[12]. Lalitha, M. P., Reddy, V. V., & Usha, V. (2010). Optimal DG Placement for Minimum Real Power Loss In Radial Distribution Systems using PSO. Journal of Theoretical & Applied Information Technology, 13, 107-116.

[13]. Manafi, H., Ghadimi, N., Ojaroudi, M., & Farhadi, P. (2013). Optimal Placement of Distributed Generations in Radial Distribution Systems using various PSO and DE algorithms. Elektronika ir Elektrotechnika, 19(10), 53-57.

[14]. Mardaneh, M., & Gharehpetian, G. B. (2004, November). Siting and sizing of DG units using GA and OPF based technique. In TENCON 2004. 2004 IEEE Region 10 Conference (Vol. 100, pp. 331-334). IEEE.

[15]. Mishra, A., & Bhandakkar, A. (2014). Selection of Optimal Location and Size of Distributed Generation in Distribution System using Particle Swarm Optimization. International Journal of Engineering Research & Technology (IJERT), 3(1).

[16]. Mistry, K., & Roy, R. (2012, December). CRPSO based Optimal Placement of Multi-distributed Generation in Radial Distribution System. In Power and Energy (PECon), 2012 IEEE International Conference on (pp. 852-857). IEEE.

[17]. Murty, V. V. S. N., Teja, B. R., & Kumar, A. (2014, January). A contribution to load flow in radial distribution system and comparison of different load flow methods. In Power Signals Control and Computations (EPSCICON), 2014 International Conference on (pp. 1-6). IEEE.

[18]. Shi, Y., & Eberhart, R. (1998, May). A Modified Particle Swarm Optimizer. In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on (pp. 69-73). IEEE.

[19]. Singh, D., Singh, D., & Verma, K. S. (2009). Multi objective optimization for DG planning with load models. IEEE Transactions on Power Systems, 24(1), 427-436.

[20]. Xin, J., Chen, G., & Hai, Y. (2009, April). A Particle Swarm Optimizer with Multi-stage Linearly-decreasing Inertia Weight. In Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on (Vol. 1, pp. 505-508). IEEE.

[21]. Yammani, C., Maheswarapu, S., & Matam, S. K. (2014, May). Optimal placement and sizing of multi Distributed generations with renewable bus available limits using Shuffled Bat algorithm. In Electrical and Computer Engineering (CCECE), 2014 IEEE 27^th Canadian Conference on (pp. 1-6). IEEE.

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

Current Issue Vol. 11 Issue 3

Most Read

Most Cited

Volume 5 Issue 3 August - October 2017

Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions

Mehmet Ozkaymak* , Abdulsamed Tabak**, Ahmet Canan***, Mehmet Volkan Aksay****, Ozgur Inanc*****, Ummu Gulsum Eruz******

Abstract

References

Full Article (HTML)

Pdf

Sine Cosine Algorithm for Loss Reduction in Distribution System with Unified Power Quality Conditioner

M. Laxmidevi Ramanaiah* , M. Damodar Reddy**

* Research Scholar, Department of Electrical and Electronics Engineering, Sri Venkateswara. University, Tirupati, Andhra Pradesh, India. ** Professor, Department of Electrical and Electronics Engineering, Sri Venkatesswara University, Tirupati, Andhra Pradesh, India.

Ramanaiah, M. L., and Reddy, M. D. (2017). Sine Cosine Algorithm for Loss Reduction in Distribution System with Unified Power Quality Conditioner. i-manager’s Journal on Power Systems Engineering, 5(3), 10-16. https://doi.org/10.26634/jps.5.3.13667

Abstract

References

Full Article (HTML)

Pdf

Optimizing Load Dispatch using Flower Pollination Algorithm Technique

Saurabh Yadav* , S. K. Gupta**

* PG Scholar, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India. ** Professor, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.

Yadav, S., and Gupta, S. K. (2017). Optimizing Load Dispatch using Flower Pollination Algorithm Technique. i-manager’s Journal on Power Systems Engineering, 5(3), 17-23. https://doi.org/10.26634/jps.5.3.13668

Abstract

References

Full Article (HTML)

Pdf

A Modified BFOA Approach for Optimal Location and Sizing of Facts for Enhancing Power System Security

T. N. V. L. N. Kumar* , R. V. S. Satyanarayana**

*Professor and Head, Department of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, India. ** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.

Kumar, T. N. V. L. N., and Satyanarayana, R. V. S. (2017). A Modified BFOA Approach for Optimal Location and Sizing of Facts for Enhancing Power System Security. i-manager’s Journal on Power Systems Engineering, 5(3), 24-33. https://doi.org/10.26634/jps.5.3.13669

Abstract

References

Full Article (HTML)

Pdf

Analysis of Optimal Size and Location of Distributed Generation for Various Inertia Weights using IPSO Algorithm

Lavanya Nalla* , N. Prema Kumar**

* PG Scholar, Department of Electrical Engineering, Andhra University, Visakhapatnam, India. ** Professor, Department of Electrical Engineering, Andhra University, Visakhapatnam, India.

Nalla, L., and Navuri, P. K. (2017). Analysis of Optimal Size and Location of Distributed Generation for Various Inertia Weights using IPSO Algorithm. i-manager’s Journal on Power Systems Engineering, 5(3), 34-40. https://doi.org/10.26634/jps.5.3.13670

Abstract

References

Full Article (HTML)

Pdf

Mehmet Ozkaymak* , Abdulsamed Tabak, Ahmet Canan*, Mehmet Volkan Aksay**, Ozgur Inanc*, Ummu Gulsum Eruz****

* Research Scholar, Department of Electrical and Electronics Engineering, Sri Venkateswara. University, Tirupati, Andhra Pradesh, India.

** Professor, Department of Electrical and Electronics Engineering, Sri Venkatesswara University, Tirupati, Andhra Pradesh, India.

* PG Scholar, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.

** Professor, Department of Electrical Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, India.

*Professor and Head, Department of Electrical and Electronics Engineering, Geethanjali Institute of Science and Technology, Nellore, India.

** Professor, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Tirupati, India.

* PG Scholar, Department of Electrical Engineering, Andhra University, Visakhapatnam, India.

** Professor, Department of Electrical Engineering, Andhra University, Visakhapatnam, India.