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A hybridized approach for design and optimization of ORPD under unbalanced conditions

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The issue of ORPD (Optimal Reactive Power Dispatch) for enhancing security and economy of a power system has been given substantial consideration in recent days. The major inspiration behind deploying an ORPD system for enhancing power system efficiency is to reallocate the RP (reactive power) in such a manner that power loss be minimized, and voltage profiles get enhanced. Hence, this paper concerns the major objectives, namely, reduction of power loss and voltage deviation that are related to solving ORPD problem under unbalanced condition. To attain these objectives, an amalgamation of two algorithms, called CS (Cuckoo Search) and GWSO (Glow Worm Swarm), is adopted for optimizing, and hence the proposed model is referred to as CP-GWSO. This algorithm functions with the control parameters, namely load reactance, voltage and transformer tap settings that are tuned to attain the optimum outcome. The entire empirical part of the investigations is performed on two IEEE standard test bus systems, the IEEE 14 and the IEEE 39 bus systems. Finally, the proposed scheme is compared to the conventional methods, and its efficiency is confirmed.
Słowa kluczowe
Rocznik
Strony
309--328
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
  • Department of EEE, Narasaraopeta Engineering College, Kotappakonda Road, Narasaraopet, Guntur, Andhra Pradesh 522601, India
autor
  • Department of EEE, University College of Engineering Kakinada, JNTUK, Kakinada, Andhra Pradesh, India
Bibliografia
  • [1] Attia, A., Sehiemy, R.A.E., Hasanien, H.M. (2018) Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. International Journal of Electrical Power & Energy Systems, 99, 331-343.
  • [2] Aydin, O., Tezcan, S.S., Eke, I., Taplamacioglu, M.C. (2017) Solving the Optimal Power Flow Quadratic Cost Functions using Vortex Search Algorithm. IFAC-PapersOnLine, 50(1): 239-244.
  • [3] Benedito, E., Puerto-Flores, D., D`oria-Cerezo, A., Scherpen, J. M. A. (2017) Optimal Power Flow for resistive DC Networks: a PortHamiltonian approach. IFAC-PapersOnLine, 50(1): 25-30.
  • [4] Biswas, P.P., Suganthan, P.N., Amaratunga, G. A. J. (2017) Optimal power flow solutions incorporating stochastic wind and solar power. Energy Conversion and Management, 148, 1194-1207.
  • [5] Davoodi, E., Babaei, E., Mohammadi-ivatloo, B. (2018) An efficient convexified SDP model for multi-objective optimal power flow. International Journal of Electrical Power & Energy Systems, 102: 254-264.
  • [6] El-Fergany, A.A., Hasanien, H.M. (2018) Tree-seed algorithm for solving optimal power flow problem in large-scale power systems incorporating validations and comparisons. Applied Soft Computing, 64, 307-316.
  • [7] Engelmann, A., M¨uhlpfordt, T., Jiang, Y., Houska, B., Faulwasser, T. (2017) Distributed AC Optimal Power Flow using ALADIN. IFACPapersOnLine, 50, 1, 5536-5541.
  • [8] Gao, K.Z., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F., Sadollah. A. (2016) Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. KnowledgeBased Systems, 109, 1-16.
  • [9] Ghasemi, M., Ghavidel, S., Ghanbarian, M.M., Habibi, A. (2014) A new hybrid algorithm for optimal RP dispatch problem with discrete and continuous control variables. Applied Soft Computing, 22: 126-140.
  • [10] Grover-Silva, E., Girard, R., Kariniotakis, G. (2018) Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm. Applied Energy, 219, 385-393.
  • [11] Heidari, A.A., Abbaspour, R.A., Jordehi, A.R. (2017) Gaussian barebones water cycle algorithm for optimal RP dispatch in electrical power systems. Applied Soft Computing, 57: 657-671.
  • [12] Hermann, A., Wu, Q., Huang, S., Nielsen, A.H. (2016) Convex Relaxation of Optimal Power Flow in Distribution Feeders with Embedded Solar Power. Energy Procedia, 100, 43-49.
  • [13] Iyapparaja, M., Tiwari. M. (2017) Security policy speculation of user uploaded images on content sharing sites. IOP Conference Series: Materials Science and Engineering, 263(4): 042019.
  • [14] Jangir, P., Parmar, S. A., Trivedi, I. N., Bhesdadiya, R. H. (2017) A novel hybrid Particle Swarm Optimizer with multi verse optimizer for global numerical optimization and Optimal RP Dispatch problem. Engineering Science and Technology, an International Journal, 20(2): 570-586.
  • [15] Kota, P. N., Gaikwad. A. N. (2017) Optimized Scrambling Sequence To Reduce PAPR In Space Frequency Block Codes Based MIMO-OFDM System. Journal of Advanced Research in Dynamical and Control System, 502-525.
  • [16] Kshisundaram, M. and Sreedharan. S. (2015) Intelligent Risk Analysis Model for Mining Adaptable Reusable Component. International Arab Journal of Information Technology (IAJIT), 12.
  • [17] Kumar, B. S. S., Manjunath, A. S., Christopher. S. (2018) Improved entropy encoding for high efficient video coding standard. Alexandria Engineering Journal, 57(1): 1-9.
  • [18] Mareli, M., Twala, B. (2017) An adaptive Cuckoo Search algorithm for optimisation. Applied Computing and Informatics.
  • [19] Mei, R. N. S., Sulaiman, M. H., Mustaffa, Z., Daniyal, H. (2017) Optimal RP dispatch solution by loss minimization using moth-flame optimization technique. Applied Soft Computing, 59: 210-222.
  • [20] Mohagheghi, E., Gabash, A., Alramlawi, M., Li, P. (2018) Real-time optimal power flow with RP dispatch of wind stations using a reconciliation algorithm. Renewable Energy, 126, 509-523.
  • [21] Mouassa, S., Bouktir, T., Salhi, A. (2017) Ant lion optimizer for solving optimal RP dispatch problem in power systems. Engineering Science and Technology, an International Journal, 20(3): 885-895.
  • [22] Naderi, E., Narimani, H., Fathi, M., Narimani, M. R. (2017) A novel fuzzy adaptive configuration of particle swarm optimization to solve largescale optimal RP dispatch. Applied Soft Computing, 53, 441-456.
  • [23] Nuaekaew, K., Artrit, P., Pholdee, N., Bureerat, S. (2017) Optimal RP dispatch problem using a two-archive multi-objective grey wolf optimizer. Expert Systems with Applications, 87: 79-89.
  • [24] Pagnetti, A., Ezzaki, M., Anqouda, I. (2017) Impact of wind power production in a European Optimal Power Flow. Electric Power Systems Research, 152, 284-294.
  • [25] Rajan, A., Malakar, T. (2015) Optimal RP dispatch using hybrid Nelder– Mead simplex based firefly algorithm. International Journal of Electrical Power & Energy Systems, 66: 9-24.
  • [26] Roald, L., Vrakopoulou, M., Oldewurtel, F., Andersson, G. (2015) Risk-based optimal power flow with probabilistic guarantees. International Journal of Electrical Power & Energy Systems, 72, 66-74.
  • [27] Roberge, V., Tarbouchi, M., Okou, F. (2016) Optimal power flow based on parallel metaheuristics for graphics processing units. Electric Power Systems Research, 140, 344-353.
  • [28] Sarkar, A., Murugan. T. S. (2017) Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks, 1–18.
  • [29] Shareef, S. K. M., Rao, R. S. (2018) Optimal RP dispatch under unbalanced conditions using hybrid swarm intelligence. Computers & Electrical Engineering,69, 183-193.
  • [30] Shilaja, C., Ravi, K. (2017) Optimal Power Flow Using Hybrid DA-APSO Algorithm in Renewable Energy Resources. Energy Procedia, 117: 10851092.
  • [31] Vrionis, T. D., Koutiva, X. I. and Vovos, N. A. (2014) A Genetic Algorithm-Based Low Voltage Ride-Through Control Strategy for Grid Connected Doubly Fed Induction Wind Generators. IEEE Transactions on Power Systems, 29(3).
  • [32] Wagh, A. M., Todmal, S. R. (2015) Eyelids, Eyelashes Detection Algorithm and Hough Transform Method for Noise Removal in Iris Recognition. International Journal of Computer Applications, 112(3).
  • [33] Wang, H., Wang, W., Zhou, X., Sun, H., Cui, Z. (2017) Firefly algorithm with neighborhood attraction. Information Sciences, 382–383, March, 374-387.
  • [34] Xu, Y., Sun, H., Liu, H., Fu, Q. (2018) Distributed solution to DC optimal power flow with congestion management. International Journal of Electrical Power & Energy Systems, 95, 73-82.
  • [35] Zhang, J., Xia, P. (2017) An improved PSO algorithm for parameter identification of nonlinear dynamic hysteretic models. Journal of Sound and Vibration, 389, 153-167.
  • [36] Zhou, Y., Zhou, G., Wang, Y and Zhao, G. (2013). A Glowworm Swarm Optimization Algorithm Based Tribes. Applied Mathematics & Information Sciences, 7(2): 537-541.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-01e97c46-45f9-4f9b-8b6d-e3f250393232
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