Special Issue No. – 7, February, 2020

Abstract:

This paper presents the transient performance of Wind-Diesel hybrid power system. Induction generator is used for wind power plant and synchronous generator is used for diesel system, combining both, named as hybrid system, is subjected to step load variation for analysing stability issues. The small signal model of synchronous generator with excitation system, induction generator of wind turbine and UPFC is obtained based on the requirement. The system reactive power is monitored and controlled by a Unified Power Flow Controller (UPFC) which improves the voltage profile of the system and thereby the stability. The system performance is investigated for both constant wind speed and varying wind speed. The complete system is modelled and built using MATLAB Simulink and the results are verified for various cases with and without UPFC controller.

Keywords:

Induction Generator (IG),Synchronous Generator (SG),Unified Power Flow Controller (UPFC),Wind-Diesel Hybrid system,Diesel Generator set,

Refference:

I. A. Yazdani, H. Sepahvand, M. L. Crow, and M. Ferdowsi, “Fault detection and mitigation in multi-level converter STATCOMs,” IEEE Trans. Industrial Electronics., vol. 58, no. 4, pp. 1307–1315, Apr. 2011
II. B. Ackermann, “Single phase induction motor with permanent-magnet excitation,” IEEE Trans. Magn., vol. 36, no. 5, pp. 3530–3532, September 2000.
III. C. Abbey, W. Li, and G. Joos, “An online control algorithm for application of a hybrid ESS to a wind–diesel system,” IEEE Trans. Industrial Electronics., vol. 57, no. 12, pp. 3896–3904, December 2010
IV. Irving P.Girsang and Jaspreet S. Dhupia, Eduard Muljadi and Mohit Singh, “Gearbox and Drive Train models to study Dynamic Effects of Modern Wind Turbines”, NREL at www.nrel.gov/publications.
V. K. Suresh, P.Venkatesh, “Modelling and Controlling of Unified Power Flow Controller(UPFC)”, IJMER Vol.2, Issue 4, July-Aug 2012, pp 2574-2577.
VI. M. Liserre, R. Cardenas, M. Molinas, and J. Rodríguez, “Overview of multi-MW wind turbines and wind parks,” IEEE Trans. Industrial Electronics., vol. 58, no. 4, pp. 1081–1095, April 2011.
VII. N. G. Hingorani and L. Gyugyi, Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems. New York: IEEE Power Eng. Soc., 2000.
VIII. “Power System Block set User’s Guide Version I- Hydro-Quebec”, TEQSIM International.
IX. Pawn Sharma and T. S. Bhatti , “Performance Investigation of Isolated wind- diesel Hybrid Power systems with WECS Having PMIG” IEEE Trans. on Industrial Electronics, Vol. 60, NO.4, April 2013.
X. Quasy Abdul-Jabber Jawad, KaareemKadhumGasem, “Design and simulation of Hybrid systems for Electricity Generation”, Diyala Journal of Engineering Science, vol.06, no.02, pp.38-56, June 2013.
XI. R. C. Bansal, “Automatic reactive power control of isolated wind–diesel hybrid power systems,” IEEE Trans. Industrial Electronics., vol. 53, no. 4, pp. 1116–1126, June 2006.
XII. R. Cardenas, R. Pena, M. Perez, J. Clare, G. Asher, and F. Vargas, “Vector control of front-end converters for variable speed wind diesel systems,” IEEE Trans. Industrial Electronics., vol. 53, no. 4, pp. 1127–1136, June 2006.
XIII. R. Pena, R. Cardenas, J. Proboste, J. Clare and G. Asher. “Wind-Diesel generation using doubly fed induction machines,” IEEE Trans. Energy Converters. Vol 23, No.1.pp 202-2014, March 2008.
XIV. S. Roy, “Reduction of voltage dynamics in isolated wind–diesel units susceptible to gusting,” IEEE Trans. Sustainable Energy, vol. 1, no. 2, pp. 84–91, July 2010.
XV. SajjadAhmadnia, NasirBoroomand, “New modeling of UPFC for power flow study and setting parameters to Increase voltage Level and Reduce Power losses”, IJAPE 2012, 1:77-82, June 2012.
XVI. T. Fukami, K. Nakagawa, Y. Kanamaru, and T. Miyamoto, “A technique for the steady-state analysis of a grid-connected permanent-magnet induction generator,” IEEE Trans. Energy Converters., vol. 19, no. 2, pp. 318–324, June 2004.
XVII. T. Fukami, K. Nakagawa, Y. Kanamaru, and T. Miyamoto, “Effects of the built-in permanent magnet rotor on the equivalent circuit parameters of a permanent magnet induction generator,” IEEE Trans. Energy Converters, vol. 22, no. 3, pp. 798–799, September 2007
XVIII. T. Zhou and B. Francois, “Energy management and power control of a hybrid active wind generator for distributed power generation and grid integration,” IEEE Trans. Industrial Electronics, vol. 58, no. 1, pp. 95–104, January 2011.
XIX. Wilson Selony, “Dynamic Simulation and Economic Analysis of an Isolated Hybrid wind diesel system”, International Masters program in Electric Power Engineering, Taiwan.

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Abstract:

In recent years, Deep Learning technologies are more popular and used in many fields like agriculture, healthcare, manufacturing etc. One of the areas in deep learning is image classification and the results are useful, successful with more accuracy. Deep learning algorithm for image classification is CNN (Convolutional Neural Network). This paper uses the leaf image dataset like Good leaf images, leaf with worms and leaf with insect images. It is very important to classify the leaf in the agriculture field to spray the pesticide or insectides. Sometimes, some leaves are good in particular areas; those areas need only water for growth. This paper deals with deep learning techniques such CNN, used to classify leaf images using MATLAB. The objectives of the work is to classify leaves as Good, Worms, Insects for better understanding and spray of Pesticides, Insecticides, this helps farm owners for better yield and it indirectly increases the economic growth of the country.

Keywords:

CNN,Alexnet,Pesticides,Insecticides,MATLAB,

Refference:

I. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Adv. Neural Inf. Process. Syst., pp. 1–9, 2012.

II. H. Durmus¸ E. O. Gunes ¨ ¸ and M. Kırcı, “Disease detection on the leaves of the tomato plants by using deep learning”, In Agro-Geoinformatics, IEEE 6th International Conference on, pp. 1-5, 2017.
III. http://www.llojibwe.org/drm/greenteam/pesticides_Article.pdf

IV. https://in.mathworks.com/help/deeplearning/ug/transfer-learning-with-deep-network-designer.html

V. MelikeSardogan ;AdemTuncer ; YunusOzen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” 2018 3rd International Conference on Computer Science and Engineering (UBMK) on pp.382-385
VI. Y. Le Cun, Y. Bengio and G. Hinton, “Deep Learning”, Nature, vol. 521, pp. 436-444, 2015

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Abstract:

In the present study, interdependency between Physico-Chemical parameters of Bhavani River water samples collected from three stations of Coimbatore district, Tamilnadu, India is carried out.   For the water quality parameter under consideration, Descriptive Statistical model is developed for each station. Cross correlation coeffienct between the parameters of three stations are calculated and the parameters with high significant level of Cross correlation are identified. To identify the interdependency between the parameters, regression analysis is used for highly cross correlated water quality parameters. The few water quality characteristics are very high in station III compare to other two stations. It is observed that the collision of human action was rigorous on only some of the parameters and leads to deterioration in water quality, due to the lack of proper sanitation, unprotected river sites and high anthropogenic actions.

Keywords:

Bhavani River,Descriptive Statistics,Regression Analysis,ANOVA,

Refference:

I. A. Geetha, P. N. Palanisamy, P. Sivakumar, P. Ganesh Kumar and M. Sujatja, “Assessment of Underground Water Contamination and Effect of Textile Effluents on Noyyal River Basin Inand round Tiruppur Town, Tamil Nadu,”E-Journal of Chemistry, vol. 5(4), pp.696-705,2008.
II. AminuIbrahim, HafizanJuahir, MohdEkhwanToriman, Adamu Mustapha, AzmanAzid, Hamza A Isiyaka, “Assessment of surface water quality using multivariate statistical techniques in the Terengganu river Basin, Malaysian Journal of Analytical Sciences, vol. 19(2), pp. 338 – 348,2015.
III. Bonika Pant, RajinderKaur, N Soranganba,IqraNazir, VibhaLohani and RN Ram, “Role of catchment area on water quality and production pattern in two different riverine Ecosystems,” Journal of Entomology and Zoology Studies,vol 5(2), pp. 1545-1549,2017.
IV. David Noel S and Rajan MR, Impact of Dyeing Industry Effluent on Groundwater Quality by Water Quality Index and Correlation Analysis, J. Pollut. Eff. Cont.2 (2), 2014.
V. Dimowo Benjamin Onozeyi, “Assessment of Some Physico-Chemical Parameters of River Ogun (Abeokuta, Ogun State, Southwestern Nigeria) in Comparison with National and International Standards,” International Journal of Aquaculture, vol. 3 (15), 79-84, 2013.
VI. DominicRavichandra Y and Ramakrishnan K, Correlation and Regression Studies of water quality parameters – A case study of water from the Bhavani River, Asian J. Chem., 19,2679– 2685 (2007).
VII. Elham M. Ali, Sami A. Shabaan-Dessouki, Abdel Rahman I. Soliman, Ahlam, S. El Shenawy, Characterization of Chemical Water Quality in the Nile River, Egypt, Int. J. Pure App. Biosci. 2 (3), 35-53, 2014.
VIII. Gajendran C and Thamarai P, Study on Statistical relationship between ground water quality parameters in Nambiyar River basin, Tamilnadu, India, Poll Res. 27, No. 4 , 679-683, 2008.
IX. Jeyaraj M, Ramakrishnan K, Arunachalam S and Magudeswaran P.N., A modified water quality index for Ponds connected with river Noyyal, Coimbatore, India, Asian Journal of Chemistry, 28, No.7 1469-1473 (2016).
X. K. Varunprasath and Nicholas A. Daniel, “Physico-Chemical Parameters of River Bhavani in Three Stations, Tamil Nadu, India,”Iranica Journal of Energy & Environment,vol.1(4),pp.321-325,2010.
XI. M.T.H. Van Vliet, J.J.G.Zwolsman, Impact of summer droughts on the water quality of the Meuse river, Journal of Hydrology, 353, 1– 17, 2008.
XII. Md. Ashiqur Rahman and Dhia Al Bakri, “A Study on Selected Water Quality Parameters along the River Buriganga,” Iranica Journal of Energy & Environment, vol.1 (2), pp. 81-92,2010.
XIII. Mei-Lin Wu, You-Shao Wang, Cui-Ci Sun, Haili Wang, Zhi-Ping Lou and Jun-De Dong, “Using Chemo metrics to identify water quality in Daya Bay, China,”OCEANOLOGIA, vol. 51 (2), pp. 217–232,2009.
XIV. Narendra Singh Bhandari and Kapil Nayal, “Correlation Study on Physico-Chemical Parameters and Quality Assessment of Kosi River Water, Uttarakhand,”E-Journal of Chemistry, vol. 5 (2), 342-346, 2008.
XV. NidhiGuptaa, PankajPandeya,JakirHussainb, “Effect of physicochemical and biological parameters on the quality of river water of Narmada, Madhya Pradesh,” India Water Science, vol. 31, pp.11–23,2017.
XVI. P. Lilly Florence, A. PaulrajandT. Ramachandramoorthy, “Water Quality Index and Correlation Study for the Assessment of Water Quality and its Parameters of Yercaud Taluk, Salem District, Tamil Nadu, India,” Chem.Sci. Trans., vol. 1(1), pp.139-149,2012.
XVII. SnehGangwar, “Water Quality Monitoring in India: A Review,”International Journal of Information and Computation Technology, vol. 3(8),pp. 851-856,2013.
XVIII. Somphinith Muangthong, “Assessment of surface water quality using multivariate statistical techniques: A case study of the Nampong River Basin, Thailand,” The Journal of Industrial Technology, vol. 11 (1), 2015.
XIX. Sunita Verma, Divya Tiwari and Ajay Verma, “Comparison of Water Quality Parameters for Ganga and Pandu River in Kanpur,” International Journal of Engineering Inventions, vol. 6 (10), pp. 38-41,2017.
XX. Z. Vassilis. Antonopoulos, M. Dimitris Papamichail and A Konstantina. Mitsiou, “Statistical and trend analysis of water quality and quantity data for the Strymon River in Greece,” Hydrology and Earth System Sciences, vol. 5(4), 2001, pp. 679-691, 2001.

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Abstract:

In this paper, the concepts of intuitionistic fuzzy weakly g″-closed sets and intuitionistic fuzzy weakly g″-open sets and its properties in intuitionistic fuzzy topological space is introduced.

Keywords:

Intuitionistic fuzzy topology,intuitionistic fuzzy weakly g″ closed sets ,intuitionistic fuzzy weakly g″-open sets,

Refference:

I. C. L. Chang, Fuzzy topological spaces, J. Math. Anal. Appl., 24 (1986), 182-190.

II. D. Coker, An introduction to intuitionistic fuzzy topological spaces, Fuzzy sets and Systems 88 (1997), 81-89.

III. D. Kalamani, K. Sakthivel and C. S. Gowri, Generalized alpha closed sets in intuitionistic fuzzy topological spaces, Applied Mathematical Sciences, 6 (94) (2012), 4691-4700.

IV. H. Gurcay, D. Coker and Es. A. Haydar, On fuzzy continuity in intuitionistic fuzzy Topological spaces, The Journal of Fuzzy Mathematics, 5 (1997), 365-378.

V. K .T. Atanassov, intuitionistic fuzzy sets, Fuzzy sets and systems, 20 (1986), 87-96.

VI. L. A. Zadah, Fuzzy sets, Information and control, 8 (1965), 338-353.

VII. M. Thirumalaiswamy, Intuitionistic fuzzy gα** -closed sets, International Refered Journal of Engineering and sciences, 2 (2013), 11-16.

VIII. R. Santhi and K. Sakthivel, Intuitionistic fuzzy alpha generalized closed sets(Accepted in Mathematics Education).
IX. R. Santhi and K.S akthivel, Intuitionistic fuzzy generalized semi continuous mappings, Advances in Theoretical And Applied Mathematics, 5 (2009), 11-20.

X. S. S. Thakur and RekhaChaturvedi, Generalized closed sets in intuitionistic fuzzy topology, The Journal of Fuzzy Mathematics, 16 (3) (2008), 559-572.

XI. S. S. Thakur and RekhaChaturvedi, Regular generalized closed sets in intuitionistic fuzzy Topological spaces, Universitatea Din Bacau, Studii Si Cercetari Stiintifice, Seria: Mathematica, 16 (2006), 257-272.

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Abstract:

In textile spinning mills, the quality of yarn depends on twist and hence needs to be monitored continuously in online.  Ring spinning machine is used to produce yarn in textile industries, twist of yarn has been calculated by measuring spindle speed, measured at the common drive shaft and delivery speed of yarns is measured at front roller. Here, individual speed variation of spindle caused due to looseness or tightness of belt cannot be monitored separately. In this paper, the problem has been addressed by providing a hand-held device to the operator, which can measure individual spindle speed by Hall Effect sensor. Through wireless technology, the handheld device receives delivery speed from machine mounted controller unit which measures delivery speed. Handheld device will then calculate twist based on individual spindle speed and common delivery speed received from machine mounted unit. This device is highly needed in the industries, so that quality, production and maintenance can be improved.

Keywords:

Dynamic C,Ring spinning machine,Twist,Rabbit microcontroller,

Refference:

I. A. Goel, R. S. Mishra, “Remote data acquisition using wireless-SCADA system”, International Journal of Engineering (IJE), pp. 58-65, 2009.

II. A. M. Alexandru, A. De Mauro, “A smart web-based maintenance system for a smart manufacturing environment”, Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) 2015 IEEE 1st International Forum on, pp. 579-584, 2015.

III. B SeethaRamanjaneyulu “Wireless Sensor Networks in Industrial Automation”, IEEE technical Review, Vol.22, No.2, March-April 2005, pp 139-149.

IV. Chadhuri, A, “Effect of spindle speed on the properties of ring spun acrylic yarn”, Vol. 84.p 10-13 2003

V. Chengdu, China. Z. Shunyang X. Du, J. Yongping and W. Riming, “ Realization of Home Remote Control Network Based on ZigBee ”, Proceedings of the 8th International Conference on Electronic Measurement and Instruments, August 16 -18, (2007).

VI. D. Giusto, A. lera, G. Morabito, L. Atzori, “The internet of things: 20th Tyrrhenian workshop on digital communications”, Springer Science & Business Media, 2010.

VII. D. J. Gaushell, H. T. Darlington, “Supervisory control and data acquisition”, Proceedings of the IEEE, pp. 1645-1658, 1987.

VIII. D. Lucke, C. Constantinescu, E. Westkämper, “Smart factory-a step towards the next generation of manufacturing” in Manufacturing Systems and Technologies for the New Frontier, Springer London, pp. 115-118, 2008.

IX. “Hall Effect Sensor”, MICRO SWITCH sensing and control, Honeywell.

X. “Monitor and Control your Factory Floor from your Desk, or Anywhere

XI. Jen HaoTeng et.al, “Integration of networked embedded systems into power equipment remote control and monitoring”, ISBN: 0-7803-8560-8, IEEE, 2004.

XII. Kolandaisamy, Peer Mohamed, “Yarn Twisting” AUTEX Reasearch Journal, Vol.5, No.2, June 2005.

XIII. Stephanie White, Mack Alford & Julian Hotlzman, “Systems Engineering of Computer – Based Systems.” In: Lawson (ed.), Proceedings 1994 Tutorial and Workshop on Systems Engineering of Computer -Based Systems, IEEE Computer Society, Los Alamitos CA, 1994, pp. 18 -29.

XIV. Thompson H. A, “Wireless and Internet communications technologies for monitoring and control”, Control EngineeringPractice, no. 12, pp. 781 –79, 2004

XV. US Patent 4598540 – “Ring spinning or twisting machine having a device for the automatic and simultaneous removal of all full cops”, US Patent Issued on July 8, 01986.
XVI. Xian, China D. Yan and Z. Dan, “ZigBee – based Smart Home System Design ” , Proceedings of the 3rd International Conference on Advanced Computer Theory and Engineering, , August 20 – 22, (2010)

XVII. Xiaorong .C, Zhan .S, G. Zhenhua, “Research on remote data acquisition system based on GPRS”, 8th Int. Conf. Electron icMeasurement and Instruments ICEMI 2007 Xian, China

XVIII. Z. Shunyang X. Du, J. Yongping and W. Riming, “Realization of Home Remote Control Network Based on ZigBee ”, Proceedings of the 8th International Conference on Electronic Measurement and Instruments, , August 16 – 18, (2007)

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Abstract:

The notion of the paper is to introduce new separation axioms namely (i₁,i₂)mg-T_1/2 , (i₁,i₂)smg-T_1/2 and (i₁,i₂) smg-T_1/2 spaces. Furthermore, we introduce a new closure operator (i₁,i₂)smg– cl(E)

Keywords:

(i₁,i₂)mg- T_1/2,(i₁,i₂)smg-T_1/2,(i₁,i₂)smg-T_1/2,(i₁,i₂)smg-cl(E),

Refference:

I. Al-Saadi.H.S, Zakari.A.H, On some separation axioms and strongly generalized closed sets in Biminimal spaces, International Mathematical Form, 3, 2008, no.21, 1039-1054.
II. Boonpok.C, Biminimal structure spaces, Int.Math.Forum, 5(15) (2010), 703-707.
III. ChokchaiViriyapong, MaliwanTunapan,WitchayaRattanametawee, ChwalitBoonpok , Generalized m-closed sets in Biminimal structure spaces, Int. Journal of Math . Analysis, Vol.5, 2011, no.7, 333-346.
IV. Fututake, On generalized closed sets in bitopological space, Bull. Fukuoka. Univ. Ed. part III, 35 (1985), 19-28.
V. Kelly.J.C, Bitopological spaces, Proc. London Math.Soc.(3),13 (1963),71-89.
VII. Noiri.T, 11th meetings on topological spaces and its Applications,Fukuoka University Seminar House,2006,1-9.
VII. Noiri.T and V.Popa,” A Decomposition of m-continuity” SeriaMatematica-
Informatica- Fizica,Vol.LXII,No.2/2010, 46-53.
VIII. Pushpalatha.A and Subha.E, Strongly Generalized closed sets in Minimal
structures, Int.Journal of Math. Analysis Vol.3, 2009, no.26, 1259-1263.
IX. Subha.E and Nagaveni.N, Strong Separation Axioms of -spaces, Int.Journal of Math. Analysis,Vol.8,2014,no.35,1723-1732.

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