i-manager Publications

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

Current Issue Vol. 12 Issue 1

Thermodynamic and Exergoeconomic Operation Optimization and Simulation of Steam Generation Solar Power Plant
Topology Transformation Approach for Optimal PMU Placement for Monitoring and Control of Power System
Performance Evaluation of Power System with HVDC Integration: Impact of SSSC and STATCOM on Power System Efficiency and Stability
Photovoltaic Systems: A Pollination-Based Optimization Approach for Critical Industrial Applications
Design of a Robust Controller for the Load Frequency Control of Interconnected Power System

Most Cited

Volume 2 Issue 4 November - January 2015

Research Paper

Mitigation of Unbalanced Voltage Sag/Swells Using Dynamic Voltage Restorer Based on Super Capacitor

K. Venkateswarlu* , B. Ashwin Kumar, N. Srinivas*, V. Mallikarjuna Rao****

* Associate Professor, Department of EEE, Sri Indu College of Engineering & Technology, Hyderabad, India.

Assistant Professor, Department of EEE, Sri Indu College of Engineering & Technology, Hyderabad, India.

* Assistant Professor, Department of EEE, Vardhaman College of Engineering, Hyderabad, India.

**** Sr. Designing Engineer of Electrical Switch Yards SSIPCPL, Hyderabad, India.

Koppolu, V., Kumar, B. A., Srinivas, N., and Rao, V. M. (2015). Mitigation of Unbalanced Voltage Sag/Swells Using Dynamic Voltage Restorer Based on Super Capacitor. i-manager’s Journal on Power Systems Engineering, 2(4), 1-6. https://doi.org/10.26634/jps.2.4.3110

Abstract

This paper presents the design and operation of three phase four wire Dynamic Voltage Restorer (DVR) based on supercapcitor as energy storage to compensate voltage sag/swell in electrical distribution system. The proposed system comprises of power circuit of the DVR, Voltage source inverter and supercapcitor as an energy storage device. It involves the control strategy for the operation of DVR during disturbances in the supply voltage. Control scheme is designed using SRF theory coupled to Proportional Integral (PI) controller technique which is applied to the DVR. The operation of the DVR depends on the control circuit topologies. The main aims of this paper is to design, operate DVR power circuit using SRF and PI techniques to mitigate unbalanced voltage sag/swells in three phase four wire Electrical distribution system. This paper is competent to mitigate voltage disturbances even at low voltage distribution system. The execution of supercapcitor as energy storage is to render real power to the inverter throughout malfunctioning in the supply voltage. The proposed controller was then simulated and outputs were carried out using MATLAB/SIMULINK software.

References

[1]. Ghosh A, Jindal AK, Joshi A, (2004). “Design of a capacitor – supported Dynamic Voltage Restorer (DVR) for unbalanced and distorted loads,” IEEE Transactions.

[2]. Lim PK, Dor DS, (2000). “Understanding and resolving voltage sag related problems for sensitive industrial customers,” IEEE Power Eng Soc Winter Meet, Vol.4: pp.2886–90.

[3]. Omar Rosli, Rahim NA, (2010). “Mitigation of voltage swells disturbances in low voltage distribution system using dynamic voltage restorer,” J Asian Power Electron, Vol.4(3).

[4]. Bollen MHJ, (2006). “Understanding power quality problems. Piscataway,” NJ: IEEE Press.

[5]. Moreno-Munoz Antonio, (2006). “Quality Power. Mitigation technologies in distribution environment,” London: Springer-Verlag London limited.

[6]. J. Praven, B. Muni, S. Venkatesh, H. Makthal, (2004). “Review of Dynamic Restorer for Power Quality Improvement,” In Proc IEEE industrial electronics society engineering society conf…; pp. 749–54.

[7]. Ghosh A, Ledwich G, (2002). “Compensation of distribution system voltage using DVR,” IEEE Trans Power Deliv.., Vol.17(4): pp.1030–6.

[8]. Hossein SH, Bannei MR, (2004). “A new minimal energy control of the DC Link energy in four-wire dynamic voltage restorer,” In: 30th Annual Conference of the IEEE Industrial Electronics Society, November 2–6. Busan, Korea.

[9]. Daehler P, Affolter R, (2000). “Requirements and solutions for dynamic voltage restorer, a case study,” IEEE Power Eng Soc Winter Meet Vol.4: pp.2881–5.

[10]. Vilathgamuwa MA, Ranjithperera AD, Choi SS, (2002). “Performance improvement of the dynamic voltage restorer with closed- loop load voltage and current-mode control,” Power Electron, Vol.17(9): pp.824–34.

[11]. Ramasamy M, Thangavel S, (2012). “Photovoltaic based dynamic voltage restorer with power saver capability using PI controller,” Int.., J Electrical Power Energy 2012, Vol.36: pp.51–9.

[12]. P.K.W. Chan, K.K.S. Leung, H.S.H. Chung, S.Y.R. Hui, (2006). “Boundry Controller for Dynamic Voltage Restorer to Achieve Fast Dynamic response,” In: Proc.., IEEE conf. pp. 1379–84.

[13]. Marei MI, EI-Saadany EF, Salama MMA, (2007). “A new approach to control DVR based on symmetrical components estimation,” IEEE Trans Power Delivered Vol.22(1): pp.2017–24.

[14]. Carl NM, Ho Henry, Chaung SH, (2007). “Fast dynamic control scheme for capacitor supported dynamic voltage restorer design issue,” Implementation and Analysis IEEE.

[15]. Kasuni Parera, Daniel Salomon, Arulampaiam, Atputharajah Sanath Alahakoon, (2006). “Automated control technique for a single phase dynamic voltage restorer,” IEEE.

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Research Paper

Multi-Objective Optimized Loadability Estimation by considering Power System Objectives using NSPSO

CHINTALAPUDI* , Sivanagaraju Sirigiri**

* Ph.D Scholar, Department of Electrical & Electronics Engineering, UCEK, JNTU Kakinada, A.P., India.

** Professor, Department of Electrical & Electronics Engineering, UCEK, JNTU Kakinada, A.P., India.

Suresh, V. C., and Sivanagaraju, S. (2015). Multi-Objective Optimized Loadability Estimation by considering Power System Objectives using NSPSO. i-manager’s Journal on Power Systems Engineering, 2(4), 7-20. https://doi.org/10.26634/jps.2.4.3111

Abstract

In Electrical Power Systems operation and management, simultaneous controlling of economic dispatch, emission, electrical power loss and voltage stability index, etc. can be formulated as a multi-objective optimization problem subject to the equality and inequality constraints. Development of an optimal strategy to achieve the above mentioned objectives, while maximizing the quantity of the load, is rather a difficult task. In practice finding a global solution for this type of problem is a matter of sheer diligence and expertise. A modern evolutionary algorithm namely Nondominated Sorting is adapted to Particle Swarm Optimization to solve multi-objective optimization problem. Since the nature of objective functions is not the same, a fuzzy decision making tool is used to choose the best solution. In common practice, maximizing load on a system needs sacrificing of some other objectives simultaneously. So, the main aim of this paper is to find maximum loadability on a given system without enormous violation of other objectives while satisfying the constraints. The efficiency of the proposed approach is examined on IEEE-30 bus with supporting results.

References

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[5]. T.Niknam, M.R.Narimani, J.Aghaei, R.Azizipanah- Abarghooee., (2012). “Improved particle swarm optimization for multi-objective optimal power flow considering the cost, loss, emission and voltage stability index,” IET Generation, Transmission & Distribution., Vol. 6, No. 6, pp. 515-527.

[6]. Momoh, JA., El-Hawary, ME., Adapa R., (1999). “A review of selected optimal power literature to 1993. Part I: non-liear and quadratic programming approaches,” IEEE Transactions on Power Systems., Vol. 14, No. 1, pp. 96- 104.

[7]. Momoh, JA., El-Hawary, ME., Adapa R., (1999). “A review of selected optimal power literature to 1993. Part II:Newton, linear programming and interior point methods,” IEEE Transactions on Power Systems., Vol. 14, No. 1, pp. 105-111.

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[9]. C.R. Fuerte-Esquivel., E. Acha., H. Ambriz-Perez, (2000). “A Comprehensive Newton-Raphson UPFC Model for the Quadratic Power Flow Solution of Practical Power Networks,” IEEE Transactions on Power Systems., Vol. 15, No. 1, Feb, pp. 102-109.

[10]. Khaled Z., Sayah S., (2008). “Optimal power flow with environmental constraints using a fast successive linear programming algorithm, applications to the Algerian power system,” Energy Conversion and Management., Vol. 49, No. 11, pp. 3363-3366.

[11]. Taher Niknam., Mohammad rasoul Narimani., Masoud Jabbari., Ahmad Reza Malekpour, (2011). “A modified shuffle frog leaping algorithm for multi-objective optimal power flow,” Journal of Energy., Vol. 36, pp. 6420- 6432.

[12]. S.F. Brodsky and R.W. Hahn., (1986). “Assessing the influence of power pools on emission constrained economic dispatch,” IEEE Transactions on Power Systems., Vol. PWRS-1., Feb.

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[18]. Coello CAC., (1999). “A comprehensive survey of evolutionar y-based multi-objective optimization techniques,” Knowledge Information System., Vol. 1, No. 3, pp.269-308.

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[30]. El-Keib. A. A, Ma. H, Hart. J. L., (1994). “Economic Dispatch in view of the clean AIR ACT of 1990,” IEEE Transactions on Power Systems, Vol. 9, No. 2., pp. 972-978.

[31]. Ya-Chin Chang, Rung-Fang Chang, Tsun-Yu Hsiao, and Chan-Nan Lu., (2011). “Transmission System Loadability Enhancement Study by Ordinal Optimization Method”, IEEE Transactions on power systems, Vol. 26, No. 1., pp. 451-459.

[32]. Lai. LL, Ma JT, etal., (1997). “Improved genetic algorithms for optimal power flow under both normal and contingent operation states,” Electric Power Energy Systems., Vol. 19, No. 5., pp. 287-292.

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[34]. Kalyanmoy Deb, Samir Agarwal, Amrit Pratap, and T Meyarivan., “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II,” Kanpur Genetic Algorithms Laboratory, IIT Kanpur, http://www.iitk.ac.in/kangal.

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[39]. Jong-Bae Park, Yun-Won Jeong, Hyun-Houng Kim and Joong-Rin Shin., (2006). “An Improved Particle Swarm Optimization for Economic Dispatch with Valve-Point Effect,” International Journal of Innovations in Energy Systems and Power., Vol. 1, No. 1., pp. 1-7.

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Research Paper

Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI

M.Harini* , M. Damodar Reddy**

* Student, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

** Professor, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

Harini, M., and Reddy, M. D. (2015). Optimal Distributed Generation Placement For Maximum Loss Reduction Using Teaching Learning Based Optimization Through Matlab Gui. i-manager’s Journal on Power Systems Engineering, 2(4), 21-28. https://doi.org/10.26634/jps.2.4.3112

Abstract

In distribution systems, distributed generation (DG) plays a vital role in meeting the demands of electrical energy. It is a small and self generating plant which provides power to industries and homes by improving the voltage profile, stability and by reducing the system losses. Optimal location and sizing of distributed generation in a radial distribution network is an optimization problem with both discrete and continuous variables. This paper presents a fuzzy approach for finding optimal locations and Teaching Learning Based Optimization (TLBO) technique for finding optimal DG sizes. The main objective is to reduce the system power losses and improve the voltage profile of the distribution network. The TLBO is inspired by the influence of the teaching by the teacher on the students in a class. In this paper, GUI is developed in Matlab to perform load flow and to obtain optimal location and sizing of DG. The proposed method was tested on IEEE 15 bus, 33 bus and 69 bus systems and the results were discussed.

References

[1]. Dugan, R.C & McDermott, T.E (2002). “Distributed Generation”, Industry Applications Magazine, IEEE, Vol.8, Issue 2, pp.19-25.

[2]. Kyu-Ho Kim & Yu-Jeong Lee (2002). “Dispersed Generator Placement using Fuzzy-GA in distribution Systems”, Power Engineering Society Summer Meeting, IEEE, Vol 3.

[3]. Caisheng Wang & M. Hashem Nehrir (2004). “Analytical Approaches for optimal placement of Distributed Generation sources in Power Systems”, IEEE Transactions on Power Systems, Vol 19.

[4]. Damodar Reddy, M & Veera Reddy, V.C (2008). “Optimal Capacitor Placement using fuzzy and particle swarm optimization method for maximum annual savings”, APRN Journal of Engineering and Applied Sciences, Vol 3.

[5].Rajesh Kumar Singh, Swapan Kumar & G Goswami (2010). “Optimum allocation of distributed generations based on nodal pricing for profit, loss reduction, and voltage improvement including voltage rise issue” International Journal of Electrical Power and energy systems, Volume 32, issue 6, pp.637-644.

[6].Sudipta Ghosh, Sakti Prasad Ghoshal & Saradindu Ghosh (2010). “Optimal sizing and placement of distributed generation in a network system” International Journal of Electrical Power and Energy Systems, Vol 32, issue 8, pp.849-856.

[7].Naveen Jain Singh, S.N & S.C. Srivastava (2010 December). “Particle Swarm Optimization Based Method for Optimal Sitting and Sizing of Multiple DGs” 16th National Power System Conference.

[8].N. Khalesi , Nazkhanom Rezaei & Mahmoud Reza Haghifam (2011). “DG allocation with application of dynamic programming for loss reduction and reliability Improvement” International Journal of Electrical Power and Energy systems, Vol 33,issue 2, pp.288-295.

[9]. Mohammad Hasan Moradi & Mohammad Abedini Ghosh (2012). “A combination of genetic algorithm and Particle swarm optimization for optimal DG location and sizing in distribution systems” International Journal of Electrical Power and Energy systems, Vol 32, issue 8, pp.66-74.

[10].Ravipudivenkata Rao, Vimal J. Savsani & D.P.Vakharia (2012). “Teaching Learning Based Optimization: An optimization method for continuous non-linear large scale problems” International Journal of Electrical Power and Energy systems, Vol.183, issue 1, pp.1-15.

[11]. Jaikaran Singh, Mukesh Tiwan & Manan (2014). “Capacitor Placement in Radial Distribution System Using Ant Colony Search Algorithm”, IJETT, Vol 9.

[12].Sneha Sultana & Provas Kumar Roy (2014). “Optimal Capacitor Placement in Radial Distribution System Using Teaching Learning Based Optimizatio Algorithm”, Electrical power and Energy Systems, Science Direct, pp.387-398.

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Research Paper

Power Quality Improving Using a SPWM Voltage Source Converter

Salla Durga Prasad* , R.S Ravi Sankar, K. Sravan Kumar*

PG Scholar, Department of Electrical and Electronics Engineering, Vignan Institute of Information Technology, Visakhapatnam, A.P, India..

Associate Professor, Department of Electrical and Electronics Engineering, Vignan Institute of Information Technology, Visakhapatnam, A.P, India..

Assistant Professor, Department of Electrical and Electronics Engineering, Vignan Institute of Information Technology, Visakhapatnam, A.P, India.

Prasad, S. D., Sankar, R. R., and Kumar, K. S. (2015).Power Quality Improving Using a SPWM Voltage Source Converter. i-manager’s Journal on Power Systems Engineering, 2(4), 29-34. https://doi.org/10.26634/jps.2.4.3113

Abstract

Custom Power is the application of power electronics to improve the quality of power distribution for sensitive industrial plants. Industries reporting production stops due to voltage disturbances, like short interruptions and voltage dips. Power electronics converters connected in series or shunt with the grid and equipped with energy storage can provide protection against voltage disturbances. This work focuses on the voltage source converter (VSC) connected in series with the grid for mitigation of voltage dips as well as swell. The core of this work is the dual vector current controller provide the error voltage. By using the pulse width modulation (PWM) we will generate the switching pulses to the voltage source converter which provide the mitigation for sag and swell. The controller consists of two loops as name as voltage control and vector current control loop. In this work PWM is used. Controllable series compensation is used for power quality improvement under sag and swell.

References

[1]. Anton Belan, Martin Liska, Boris Cintula, Zaneta Eleschova, Voltage Sags Evaluating Methods, Power Quality and Voltage Sags Assessment regarding Voltage Dip Immunity of Equipment, Institute of Power and Applied Electrical Engineering, Slovak University of Technology in Bratislava Ilkovicova 3, 812 19 Bratislava, Slovakia.

[2]. O. Anaya-Lara, E. Acha, (2002). "Modeling and analysis of custom power systems by PSCAD/EMTDC," IEEE Trans. Power Delivery, Vol. 17, No.1, pp. 266-272, January.

[3]. H. Awad, (2004). Control of static series compensator for mitigation of power quality problems, Ph.D. dissertation, Chalmers University of Technology, Goteborg, Sweden, April.

[4]. W. Freitas, A. Morelato, (2003). "Comparitive study between power system blockset and PSCAD/EMTDC for transient analysis of custom power devices based on voltage source converter," IPST, New Orleans, USA, pp. 1-6.

[5]. J. W . Choi and S. K. Sul, (1998). Fast current controller in three-phase AC/DC boost converter using d-q axis cross coupling, IEEE Trans. on Power Electronics, Vol. 13, No. 1, pp. 179“185, January.

[6]. Sunil Panda, Anupam Mishra, B. Srinivas, (2009). Control of Voltage Source Inverters using PWM/SVPWM for Adjustable Speed Drive Applications, National Institute Of Technology Rourkela, Rourkela - 769008,May.

[7]. Pulsewidth modulation From Wikipedia, the free encyclopedia.

[8]. F. A. Magueed, A.Sannino, and J. Svensson, (2004). Transient performance of voltage source converter under unbalanced voltage dips, in Proc. of IEEE Power Electronics Specialists Conference 2004 (PESC04), Vol. 2, June, pp.1163“1168

[9]. Massimo Bongiorno, Jan Svensson, Ambra Sannino, Dynamic Performance of Current Controllers for Gridconnected Voltage Source Converters under Unbalanced Voltage Conditions, IEEE.

[10]. Roshani patel, S.N. Purohit. Voltage Quality Improvement using Dynamic Voltage Restorer.

[11]. MHJ Bollen (2000).Voltage power and current ratings of series voltage controllers, in Proc. of IEEE Power Engineering Society Winter Meeting, Vol. 33, No. 2, March-April, pp. 1132“1137.

[12]. Hassan Abdullah Khalid, Ghullam Mustafa Bhutto, (2010). Optimal use of energy storage for series connected voltage source converter, Master of Science Thesis in the Master Degree Programme, Electric Power Engineering, Chalmers University of Technology Goteborg, Sweden.

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Review Paper

Applications of Artificial Neural Networks in various areas of Power System; A Review

Manjula S. Sureban* , Shekhappa G. Ankaliki **

* P.G. Scholar, Department of Electrical and Electronics Engineering, SDM College of Engineering & Tech, Dharwad, India.

** Professor, Department of Electrical and Electronics Engineering, SDM College of Engineering & Tech, Dharwad, India.

Sureban, M. S., and Ankaliki, G. S.(2015). Applications of Artificial Neural Networks In Various Areas of Power System; A Review. i-manager’s Journal on Power Systems Engineering, 2(4), 35-44. https://doi.org/10.26634/jps.2.4.3114

Abstract

The Indian power sector has made remarkable progress since Independence. The total installed capacity has gone up from 1,362 MW in 1947 to more than 2,00,000 MW in 2012 and the transmission network has increased from the isolated system concentrated around urban and industrial areas to country wide National Grid. Increased interconnection and loading of the power system along with deregulated structure and environmental concerns has brought new challenges for electric power system operation, control and automation. In liberalized electricity market, the operation and control of power system become complex due to complexity in modeling and uncertainties. In competitive electricity market along with automation, artificial intelligent techniques are very useful. Intelligent techniques make the power system more efficient and effective by operating in the desired manner as per the design and training of the systems. This paper is a review of the applications of Artificial Neural Networks into various areas of power system.

References

[1]. D. Saxena, S.N. Singh and K.S. Verma, (2010). Application of computational intelligence in emerging power systems. International Journal of Engineering, Science and Technology, Vol. 2, No. 3, 2010, pp. 1-7.

[2]. D.C. Park, M.A. El-Sharkawi, R.J. Marks , L.E. Atlas and M.J. Damborg, (1991). Electric Load Forecasting Using An Artificial Neural Network IEEE Transactions on Power Systems, Vo1.6, No. 2, May 1991.

[3]. Dharmendra Kumar Mishra, A.K.D. Dwivedi, S.P. Tripathi, (2012). Efficient Algorithms for Load Forecasting in Electric Power System using Artificial Neural Network

International Journal of Latest Research in Science and Technology. ISSN (Online):2278-5299 Vol.1,Issue 3: pp.254-258 ,September-October (2012).

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[5]. J. Jasni and M.Z. A AB Kadir, (2011). Static Power System Security Assessment via Artificial Neural Network Journal of Theoretical and Applied Information Technology, 30th September 2011. Vol. 31 No.2.

[6]. I. S. Saeha, b, M. W. Mustafaa, (2014). Artificial Neural Network for Power System Static Security Assessment: A Survey. 66:1 (2014) 35–41 www.jurnalteknologi.utm.my eISSN 2180–3722.

[7]. Bidyut Ranjan Das and Dr. Ashish Chaturvedi, (2013). Static Security analysis in Real time using ANN”, IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE) e-ISSN: 2278-1676 Volume 5, Issue 1 (Mar. - Apr. 2013), PP 50-54.

[8]. Kusum Verma and K. R. Niazi, (2012). Contingency “Constrained Power System Security Assessment using Cascade Neural Network”, J. Electrical Systems Vol.8, No.1 (2012): 1-12Regular paper.

[9]. Po-Hung Chen and Hung-Cheng Chen, (2006). Application of Evolutionary Neural Network to Power System Unit Commitment ISBN 2006, LNCS 3972, pp. 1296 – 1303, 2006. © Springer-Verlag Berlin Heidelberg 2006.

[10]. R. Nayak and J. D. Sharma. A Hybrid Neural Network and Simulated Annealing Approach to the Unit Commitment Problem Machine Learning, Research Centre1 School of Comp. Science, QUT Brisbane, QLD 4001, Australia University, of Roorkee, Roorkee, UP 247667, India.

[11]. Aejaz Ahmed K.R1, Mohd. Z. A. Ansari, Mohamed Jalaluddin, (2013). “Simulation Analysis of a Power System Protection using Artificial Neural Network”, International

[12]. M.M. Saha E. Rosolowski J. Izykowski Artificial Intelligent Application to Power System Protection Substation Automation Division ABB Automation Products AB SE-721 59 Västerås, SWEDEN.

[13]. K C P Wong, H M Ryan, J Tindle. Power System Fault Prediction Using Artificial Neural Networks.

[14]. Eisa Bashier M Tayeb. “Faults Detection in Power Systems Using Artificial Neural Network”, American Journal of Engineering Research (AJER) e-ISSN : 2320-0847 p-ISSN : 2320-0936 Volume-02, Issue-06, pp-69-75.

[15]. Worawat Nakawiro and István Erlich, (2008). Online Voltage Stability Monitoring using Artificial Neural Network, DRPT2008 6-9 April 2008 Nanjing China.

[16]. Ayman Hoballah, IEEE Transient Stability Assessment using ANN Considering Power System Topology Changes.

[17]. Swetha G C and H.R. Sudarshana Reddy, (2007). International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 3, Issue 3, March 2014 Copyright to IJAREEIE www.ijareeie.com 7993.

[18]. H Nagaraja Udupa , Dr.(Prof.) R. Kamath , Ms. Minal and Ms. Thoshi Mishra, (2014). Node Level ANN technique for Real Time Power System State estimation. International Journal of Scientific & Engineering Research, Volume 5, Issue 1, January-2014, 1500 ISSN 2229-5518.

[19]. Amamihe Onwuachumba, Yunhui Wu1 and Mohamad Musavi, (2014). Reduced Model for Power System State Estimation Using Artificial Neural Networks. Journal of Energy and Power Engineering 8 (2014) pp.957-965.

[20]. U. Arumugam, N. M. Nor, and M. F. Abdullah, (2011). A Brief Review on Advances of Harmonic State Estimation Techniques in Power Systems. International Journal of Information and Electronics Engineering, Vol. 1, No. 3, November 2011.

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

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Volume 2 Issue 4 November - January 2015

Mitigation of Unbalanced Voltage Sag/Swells Using Dynamic Voltage Restorer Based on Super Capacitor

K. Venkateswarlu* , B. Ashwin Kumar**, N. Srinivas***, V. Mallikarjuna Rao****

Koppolu, V., Kumar, B. A., Srinivas, N., and Rao, V. M. (2015). Mitigation of Unbalanced Voltage Sag/Swells Using Dynamic Voltage Restorer Based on Super Capacitor. i-manager’s Journal on Power Systems Engineering, 2(4), 1-6. https://doi.org/10.26634/jps.2.4.3110

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Multi-Objective Optimized Loadability Estimation by considering Power System Objectives using NSPSO

CHINTALAPUDI* , Sivanagaraju Sirigiri**

* Ph.D Scholar, Department of Electrical & Electronics Engineering, UCEK, JNTU Kakinada, A.P., India. ** Professor, Department of Electrical & Electronics Engineering, UCEK, JNTU Kakinada, A.P., India.

Suresh, V. C., and Sivanagaraju, S. (2015). Multi-Objective Optimized Loadability Estimation by considering Power System Objectives using NSPSO. i-manager’s Journal on Power Systems Engineering, 2(4), 7-20. https://doi.org/10.26634/jps.2.4.3111

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Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI

M.Harini* , M. Damodar Reddy**

* Student, EEE Department, SV University College of Engineering, Andhra Pradesh, India. ** Professor, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

Harini, M., and Reddy, M. D. (2015). Optimal Distributed Generation Placement For Maximum Loss Reduction Using Teaching Learning Based Optimization Through Matlab Gui. i-manager’s Journal on Power Systems Engineering, 2(4), 21-28. https://doi.org/10.26634/jps.2.4.3112

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Power Quality Improving Using a SPWM Voltage Source Converter

Salla Durga Prasad* , R.S Ravi Sankar**, K. Sravan Kumar***

Prasad, S. D., Sankar, R. R., and Kumar, K. S. (2015).Power Quality Improving Using a SPWM Voltage Source Converter. i-manager’s Journal on Power Systems Engineering, 2(4), 29-34. https://doi.org/10.26634/jps.2.4.3113

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Applications of Artificial Neural Networks in various areas of Power System; A Review

Manjula S. Sureban* , Shekhappa G. Ankaliki **

* P.G. Scholar, Department of Electrical and Electronics Engineering, SDM College of Engineering & Tech, Dharwad, India. ** Professor, Department of Electrical and Electronics Engineering, SDM College of Engineering & Tech, Dharwad, India.

Sureban, M. S., and Ankaliki, G. S.(2015). Applications of Artificial Neural Networks In Various Areas of Power System; A Review. i-manager’s Journal on Power Systems Engineering, 2(4), 35-44. https://doi.org/10.26634/jps.2.4.3114

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K. Venkateswarlu* , B. Ashwin Kumar, N. Srinivas*, V. Mallikarjuna Rao****

* Ph.D Scholar, Department of Electrical & Electronics Engineering, UCEK, JNTU Kakinada, A.P., India.

** Professor, Department of Electrical & Electronics Engineering, UCEK, JNTU Kakinada, A.P., India.

* Student, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

** Professor, EEE Department, SV University College of Engineering, Andhra Pradesh, India.

Salla Durga Prasad* , R.S Ravi Sankar, K. Sravan Kumar*

* P.G. Scholar, Department of Electrical and Electronics Engineering, SDM College of Engineering & Tech, Dharwad, India.

** Professor, Department of Electrical and Electronics Engineering, SDM College of Engineering & Tech, Dharwad, India.