UPQC Based Power Quality Enhancement for PV System in Single Phase Distribution Network
Keywords:
PV-UPQC, Fixed voltage level, MPPT, Voltage imbalance
Abstract
In order to get rid of voltage imbalances and voltage harmonics, a single-phase unified power quality conditioner (PV-UPQC) is discussed in this paper as a means of integrating photovoltaic systems into the grid. The current UPQC's DC-link voltage is insufficient to maintain a constant voltage. The proposed work combines the PV panel and UPQC. The boost converter runs the PV array in maximum power point tracking mode, which maximises power output. Matlab-Simulink is used to model the behaviour of PV-UPQC with nonlinear loads in the presence of varying irradiance and grid voltage fluctuations. To regulate photovoltaic (PV)-connected UPQC series and shunt inverters, a PLL-based control method is proposed. In addition to correcting for voltage and current fluctuations, the proposed controller can also detect phases and perfectly synchronise grids. The effectiveness and efficiency of PV-UPQC are studied through computer modelling.
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95. Chakrabarti P., Bhuyan B., Chaudhuri A. and Bhunia C.T., “A novel approach towards realizing optimum data transfer and Automatic Variable Key(AVK)” , International Journal of Computer Science and Network Security, 8(5), pp.241-250, 2008.
96. Chakrabarti P. , Goswami P.S., “Approach towards realizing resource mining and secured information transfer”, International Journal of Computer Science and Network Security, 8(7), pp.345-350, 2008.
97. Chakrabarti P., Choudhury A., Naik N. , Bhunia C.T., “Key generation in the light of mining and fuzzy rule”, International Journal of Computer Science and Network Security, 8(9), pp.332-337, 2008.
98. Chakrabarti P., De S.K., Sikdar S.C., “Statistical Quantification of Gain Analysis in Strategic Management” , International Journal of Computer Science and Network Security,9(11), pp.315-318, 2009.
99. Chakrabarti P. , Basu J.K. , Kim T.H., “Business Planning in the light of Neuro-fuzzy and Predictive Forecasting”, Communications in Computer and Information Science , 123, pp.283-290, 2010.
100. Prasad A. , Chakrabarti P., “Extending Access Management to maintain audit logs in cloud computing", International Journal of Advanced Computer Science and Applications ,5(3),pp.144-147, 2014.
101. Sharma A.K., Panwar A., Chakrabarti P. ,Viswakarma S., “Categorization of ICMR Using Feature Extraction Strategy and MIR with Ensemble Learning”, Procedia Computer Science, 57,pp.686-694,2015.
102. Patidar H. , Chakrabarti P., “A Novel Edge Cover based Graph Coloring Algorithm”, International Journal of Advanced Computer Science and Applications , 8(5),pp.279-286,2017.
103. Patidar H., Chakrabarti P., Ghosh A., “Parallel Computing Aspects in Improved Edge Cover based Graph Coloring Algorithm”, Indian Journal of Science and Technology ,10(25),pp.1-9,2017.
104. Tiwari M., Chakrabarti P, Chakrabarti T., “Novel work of diagnosis in liver cancer using Tree classifier on liver cancer dataset ( BUPA liver disorder )” , Communications in Computer and Information Science , 837, pp.155-160, 2018.
105. Verma K., Srivastava P. , Chakrabarti P., “Exploring structure oriented feature tag weighting algorithm for web documents identification”, Communications in Computer and Information Science ,837, pp.169-180, 2018.
106. Tiwari M., Chakrabarti P , Chakrabarti T., “Performance analysis and error evaluation towards the liver cancer diagnosis using lazy classifiers for ILPD”, Communications in Computer and Information Science , 837, pp.161-168,2018.
107. Patidar H. , Chakrabarti P., “A Tree-based Graphs Coloring Algorithm Using Independent Set”, Advances in Intelligent Systems and Computing, 714, pp. 537-546, 2019.
108. Chakrabarti P., Satpathy B., Bane S., Chakrabarti T., Chaudhuri N.S. , Siano P., “Business forecasting in the light of statistical approaches and machine learning classifiers”, Communications in Computer and Information Science , 1045, pp.13-21, 2019.
109. Shah K., Laxkar P. , Chakrabarti P., “A hypothesis on ideal Artificial Intelligence and associated wrong implications”, Advances in Intelligent Systems and Computing, 989, pp.283-294, 2020.
110. Kothi N., Laxkar P. Jain A. , Chakrabarti P., “Ledger based sorting algorithm”, Advances in Intelligent Systems and Computing, 989, pp. 37-46, 2020.
111. Chakrabarti P. ,Chakrabarti T., Sharma M . , Atre D, Pai K.B., “Quantification of Thought Analysis of Alcohol-addicted persons and memory loss of patients suffering from stage-4 liver cancer”, Advances in Intelligent Systems and Computing, 1053, pp.1099-1105, 2020.
112. P. Paramasivan, “A Novel Approach: Hydrothermal Method of Fine Stabilized Superparamagnetics of Cobalt Ferrite (CoFe2O4) Nanoparticles,” Journal of Superconductivity and Novel Magnetism, vol. 29, pp. 2805–2811, 2016.
113. P. Paramasivan, “Controllable synthesis of CuFe2O4 nanostructures through simple hydrothermal method in the presence of thioglycolic acid,” Physica E: Low-dimensional Systems and Nanostructures, vol. 84, pp. 258–262, 2016.
114. S. Ambika, T. A. Sivakumar, and P. Sukantha, “Preparation and characterization of nanocopper ferrite and its green catalytic activity in alcohol oxidation reaction,” Journal of Superconductivity and Novel Magnetism, vol. 32, pp. 903–910, 2019.
115. P. Paramasivan, “Comparative investigation of NiFe2O4 nano and microstructures for structural, optical, magnetic and catalytic properties,” Advanced Science, Engineering and Medicine, vol. 8, pp. 392–397, 2016.
116. P. Paramasivan, S. Narayanan, and N. M. Faizee, “Enhancing Catalytic Activity of Mn3O4 by Selective Liquid Phase Oxidation of Benzyl Alcohol,” Advanced Science, Engineering and Medicine, vol. 10, pp. 1–5, 2018.
117. Chakrabarti P., Bane S.,Satpathy B.,Goh M, Datta B N , Chakrabarti T., “Compound Poisson Process and its Applications in Business”, Lecture Notes in Electrical Engineering, 601, pp.678-685,2020.
118. Chakrabarti P., Chakrabarti T., Satpathy B., SenGupta I . Ware J A., “Analysis of strategic market management in the light of stochastic processes, recurrence relation, Abelian group and expectation”, Advances in Artificial Intelligence and Data Engineering, 1133 , pp.701-710, 2020.
119. Priyadarshi N., Bhoi A.K., Sharma A.K., Mallick P.K. , Chakrabarti P., “An efficient fuzzy logic control-based soft computing technique for grid-tied photovoltaic system”, Advances in Intelligent Systems and Computing, 1040,pp.131-140,2020.
120. Priyadarshi N., Bhoi A.K., Sahana S.K., Mallick P.K. , Chakrabarti P., Performance enhancement using novel soft computing AFLC approach for PV power system”, Advances in Intelligent Systems and Computing, 1040, pp.439-448,2020.
121. Magare A., Lamin M., Chakrabarti P., “Inherent Mapping Analysis of Agile Development Methodology through Design Thinking”, Lecture Notes on Data Engineering and Communications Engineering, 52, pp.527-534,2020.
122. Ali Y., Shreemali J., Chakrabarti T., Chakrabarti P. , Poddar S., “Prediction of Reaction Parameters on Reaction Kinetics for Treatment of Industrial Wastewater: A Machine Learning Perspective”, Materials Today :Proceedings,2020.
123. Chakrabarti P., Satpathy B., Bane S., Chakrabarti T., Poddar S., “Business gain forecasting in Materials Industry - A linear dependency, exponential growth, moving average, neuro-associator and compound Poisson process perspective”, Materials Today: Proceedings, 2020
124. Batool, Kiran; Zhao, Zhen-Yu; Irfan, Muhammad; Żywiołek, Justyna (2023): Assessing the role of sustainable strategies in alleviating energy poverty: an environmental sustainability paradigm. w: Environ Sci Pollut Res, s. 1–22.
125. Khan, Muhammad Asghar; Kumar, Neeraj; Mohsan, Syed Agha Hassnain; Khan, Wali Ullah; Nasralla, Moustafa M.; Alsharif, Mohammed H. i wsp. (2023): Swarm of UAVs for Network Management in 6G: A Technical Review. w: IEEE Trans. Netw. Serv. Manage. 20 (1), s. 741–761.
126. Mohsan, Syed Agha Hassnain; Othman, Nawaf Qasem Hamood; Khan, Muhammad Asghar; Amjad, Hussain; Żywiołek, Justyna (2022): A Comprehensive Review of Micro UAV Charging Techniques. w: Micromachines 13 (6).
127. Tucmeanu, Elena Roxana; Tucmeanu, Alin Iulian; Iliescu, Madalina Gabriela; Żywiołek, Justyna; Yousaf, Zahid (2022): Successful Management of IT Projects in Healthcare Institutions after COVID-19: Role of Digital Orientation and Innovation Adaption. w: Healthcare (Basel, Switzerland) 10 (10).
128. D. R. Patil, B. S. Borkar, A. V. Markad, and H. P. Singh, ‘Applications of Artificial Intelligence using Baye’s Theorem: Survey’, Universal Review, vol. 8, no. 02, pp. 198–203, 2019.
129. D. R. Patil and R. Purohit, ‘Dynamic Resource Allocation and Memory Management using Deep Convolutional Neural Network’, IJEAT, vol. 9, no. 02, pp. 608–612, 2019.
130. D. R. Patil and M. Sharma, ‘Dynamic Resource Allocation and Memory Management Using Machine Learning for Cloud Environments’, International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 04, pp. 5921–5927, 2020.
131. B. Adgaonkar, D. R. Patil, and B. S. Borkar, ‘Availability-Aware Multi-Objective Cluster Allocation Optimization in Energy-Efficient Datacenters’, in 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), 2022, pp. 1–6.
132. D. R. Patil, G. Mukesh, S. Manish, and B. Malay, ‘Memory and Resource Management for Mobile Platform in High Performance Computation Using Deep Learning’, ICIC Express Letters:Part B: Applications, vol. 13, no. 9, pp. 991–1000, 2022.
133. B. S. Borkar, D. R. Patil, A. V. Markad, and M. Sharma, ‘Real or Fake Identity Deception of Social Media Accounts using Recurrent Neural Network’, in 2022 International Conference on Fourth Industrial Revolution Based Technology and Practices (ICFIRTP), 2022, pp. 80–84.
134. D. R. Patil, B. Borkar, A. Markad, S. Kadlag, M. Kumbhkar, and A. Jamal, ‘Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems’, in 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC), 2022, pp. 1579–1583.
135. Żywiołek, Justyna; Tucmeanu, Elena Roxana; Tucmeanu, Alin Iulian; Isac, Nicoleta; Yousaf, Zahid (2022): Nexus of Transformational Leadership, Employee Adaptiveness, Knowledge Sharing, and Employee Creativity. w: Sustainability 14 (18), s. 11607.
136. A. R. Yeruva and V. B. Ramu, “Optimising AIOps system performance for e-commerce and online retail businesses with the ACF model,” Int. J. Intellect. Prop. Manag., vol. 1, no. 1, p. 1, 2022.
137. V. B. Ramu and A. R. Yeruva, “AIOps research innovations, performance impact and challenges faced,” Int. J. Syst. Syst. Eng., vol. 13, no. 3, p. 1, 2023.
138. V. S. R. Kosuru and A. K. Venkitaraman, “Developing a Deep Q-Learning and Neural Network Framework for Trajectory Planning”, EJENG, vol. 7, no. 6, pp. 148–157, Dec. 2022.
139. K. Venkitaraman and V. S. R. Kosuru, “Hybrid Deep Learning Mechanism for Charging Control and Management of Electric Vehicles”, EJECE, vol. 7, no. 1, pp. 38–46, Jan. 2023.
140. D. K. Sharma, B. Singh, R. Regin, R. Steffi, and M. K. Chakravarthi, “Efficient Classification for Neural Machines Interpretations based on Mathematical models,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021.
141. F. Arslan, B. Singh, D. K. Sharma, R. Regin, R. Steffi, and S. Suman Rajest, “Optimization technique approach to resolve food sustainability problems,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021.
142. G. A. Ogunmola, B. Singh, D. K. Sharma, R. Regin, S. S. Rajest, and N. Singh, “Involvement of distance measure in assessing and resolving efficiency environmental obstacles,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021.
143. D. K. Sharma, B. Singh, M. Raja, R. Regin, and S. S. Rajest, “An Efficient Python Approach for Simulation of Poisson Distribution,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021.
144. K. Sharma, B. Singh, E. Herman, R. Regine, S. S. Rajest, and V. P. Mishra, “Maximum information measure policies in reinforcement learning with deep energy-based model,” in 2021 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), 2021.
145. D. K. Sharma, N. A. Jalil, R. Regin, S. S. Rajest, R. K. Tummala, and Thangadurai, “Predicting network congestion with machine learning,” in 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), 2021.
Published
2023-10-08
How to Cite
Abdus Shakoor M.A.C, Mohamed Anas A, Peer Mohamed S, P. Anand, & 5. Shagar Banu. M. (2023). UPQC Based Power Quality Enhancement for PV System in Single Phase Distribution Network. Central Asian Journal of Medical and Natural Science, 4(5), 452-471. Retrieved from https://www.cajmns.centralasianstudies.org/index.php/CAJMNS/article/view/1848
Section
Articles
