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Optimal fault-location in smart grids with bfa and ts algorithms with the approach of reducing losses and network costs | ||
Journal of Optimization in Industrial Engineering | ||
مقاله 23، دوره 16، شماره 1 - شماره پیاپی 34، شهریور 2023، صفحه 231-247 اصل مقاله (3.44 M) | ||
نوع مقاله: Application | ||
شناسه دیجیتال (DOI): 10.22094/joie.2023.1967370.1985 | ||
نویسندگان | ||
Mahmoud Zadehbagheri* ؛ Mohammadjavad Kiani | ||
Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran | ||
چکیده | ||
The smart grid is actually the result of the integration of the structures of the power system and its communication structures, and this is the key point in the implementation of smart grid projects. This means that the integration of electricity production and distribution reduces the costs of implementing a smart network, and in this regard, the preparation of the telecommunication platform is one of the most necessary requirements for moving in the direction of smartening the network. The ability to locate the fault and restore the power supply in time is one of the important indicators of a strong smart grid. Especially when a large number of DGs are connected to the system, the network structure and working mode will change, and the demands on the traditional fault location method will increase. In this article, due to the difficulty of using the traditional fault location method in distribution networks with DG, two smart algorithms, TS and BFA, have been used for fault location in this type of networks. So that the location of dynamic distributed generation sources such as wind turbines is done first, then their effect on providing the load profile in the presence of distribution network faults is discussed. The results of the simulation confirm the correctness and correctness of the performance of the proposed method, so that the case studies conducted can open the way for engineers to evaluate new fault. | ||
تازه های تحقیق | ||
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کلیدواژهها | ||
Fault Location؛ Optimization؛ Bacterial Foraging Algorithm (BFA)؛ Micro grid؛ DG؛ Tabu Search (TS) | ||
مراجع | ||
Abbasi, A. R., & Gandhi, C. P. (2022). A Novel Hyperbolic Fuzzy Entropy Measure for Discrimination and Taxonomy of Transformer Winding Faults. IEEE Transactions on Instrumentation and Measurement, 71, 1-8.
Abbasi, A. R., & Mahmoudi, M. R. (2021). Application of statistical control charts to discriminate transformer winding defects. Electric Power Systems Research, 191, 106890.
Abbasi, A., Abbasi, S., Ansari, J., & Rahmani, E. (2015). Effect of plug-in electric vehicles demand on the renewable micro-grids. Journal of Intelligent & Fuzzy Systems, 29(5), 1957-1966.
Alanazi, M. S. (2022). A MILP model for optimal renewable wind DG allocation in smart distribution systems considering voltage stability and line loss. Alexandria Engineering Journal, 61(8), 5887-5901
Balakrishna, P., Rajagopal, K., & Swarup, K. S. (2017). Distribution automation analysis based on extended load data from AMI systems integration. International Journal of Electrical Power & Energy Systems, 86, 154-162.
Barzegaran, M. R. & Mirzaie, M. ( 2012) .Detecting the position of winding short circuit faults in transformer using high frequency analysis,” European Journal of Scientific Research, vol. 23, , pp. 644-658.
Bo, Z. Q. (1998). A new non-communication protection technique for transmission lines. IEEE Transactions on Power Delivery, 13(4), 1073-1078.
Castillo, C. A., Conde, A., & Shih, M. Y. (2018). Improvement of non-standardized directional overcurrent relay coordination by invasive weed optimization. Electric Power Systems Research, 157, 48-58.
Cheena, K., Amgoth, T., & Shankar, G. (2022). Proportional‐integral‐derivative controller‐based self‐healing of distribution system using wireless sensor networks in smart grid. International Journal of Communication Systems, 35(7), e5095.
Cong, D. P., Raison, B., Rognon, J. P., Bonnoit, S., & Manjal, B. (2005, June). Optimization of fault indicators placement with dispersed generation insertion. In IEEE Power Engineering Society General Meeting, 2005 (pp. 355-362). IEEE.
Devi, S., & Geethanjali, M. (2014). Application of modified bacterial foraging optimization algorithm for optimal placement and sizing of distributed generation. Expert Systems with Applications, 41(6), 2772-2781.
Furse, C., Chung, Y. C., Lo, C., & Pendayala, P. (2006). A critical comparison of reflectometry methods for location of wiring faults. Smart Structures and Systems, 2(1), 25-46.
Hou, N., He, F., Zhou, Y., & Chen, Y. (2020). An efficient GPU-based parallel tabu search algorithm for hardware/software co-design. Frontiers of Computer Science, 14, 1-18.
Hussain, Irshad, et al. (2022) .Exploiting lion optimization algorithm for sustainable energy management system in industrial applications. Sustainable Energy Technologies and Assessments 52: 102237.
Ishraque, M. F., Shezan, S. A., Rashid, M. M., Bhadra, A. B., Hossain, M. A., Chakrabortty, R. K., ... & Das, S. K. (2021). Techno-economic and power system optimization of a renewable rich islanded microgrid considering different dispatch strategies. IEEE Access, 9, 77325-77340.
Javadian, A., Zadehbagheri, M., Kiani, M. J., Nejatian, S., & Sutikno, T. (2021). Modeling of static var compensator-high voltage direct current to provide power and improve voltage profile. International Journal of Power Electronics and Drive Systems (IJPEDS), 12(3), 1659-1672.
Kamarposhti, M. A., Lorenzini, G., & Solyman, A. A. A. (2021). Locating and sizing of distributed generation sources and parallel capacitors using multiple objective particle swarm optimization algorithm. Math Model Eng Probl, 8, 10-24.
Kim, Insu, Beopsoo Kim, and Denis Sidorov(2022). Machine Learning for Energy Systems Optimization. Energies 15.11: 4116.
Kong, T., Jia, M., & Sun, G. (2016,). Application of bacterial foraging algorithm for fault location in distribution networks with DG. In 2016 China International Conference on Electricity Distribution (CICED) (pp. 1-4). IEEE.
Kumar, Jitendra, and Nagendra Kumar (2022).Optimal Scheduling of Grid Connected Solar Photovoltaic and Battery Storage System Considering Degradation Cost of Battery.Iranian Journal of Science and Technology, Transactions of Electrical Engineering: 1-14.
Li, Z., Qiao, J., Wang, Y., & Yin, X. (2022). A Comprehensive Method for Fault Location of Active Distribution Network Based on Improved Matrix Algorithm and Optimization Algorithm. International Transactions on Electrical Energy Systems, 2022.
Mengelkamp, E., Gärttner, J., Rock, K., Kessler, S., Orsini, L., & Weinhardt, C. (2018). Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Applied energy, 210, 870-880.
Ngaopitakkul A. & Kunakorn A. (2014).Internal fault classification in transformer windings using combination of discrete wavelet transforms and back-propagation neural networks,. International Journal of Control, Automation, and Systems, vol. 4, no. 3, pp. 365-37.
Omar, F., Habib, H., Ahmed, N. E. I. A., & Sid, A. E. M. A. (2022). Adaptive control of DC motor without identification of parameters. Facta universitatis-series: Electronics and Energetics, 35(3), 301-312.
Reddy, A. S., & Vijaykumar, M. (2008). Neural network modeling of distribution transformer with internal winding faults using double fourier series. International Journal of Computer Science and Applications, 1(3), 160-163.
Reddy, R., Shah, K., & Kallamadi, M. (2023). Towards Unique Circuit Synthesis of Power Transformer Winding Using Gradient and Population Based Methods. IEEE Latin America Transactions, 21(3), 490-497.
Satish L.& Subrat Sahoo, K. (2015). Locating faults in a transformer winding: an experimental study. Electric Power Systems Research, vol. 79, pp. 89–97.
Satyanarayana, C., Rao, K. N., Bush, R. G., Sujatha, M. S., Roja, V., & Nageswara Prasad, T. (2019). Multiple DG placement and sizing in radial distribution system using genetic algorithm and particle swarm optimization. Computational Intelligence and Big Data Analytics: Applications in Bioinformatics, 21-36.
Shintemirov, A.. Tang, W. J, Tang, W. H. & Wu, Q.H.( 2010). Improved modelling of power transformer winding using bacterial swarming algorithm and frequency response analysis,. Electric Power Systems Research, vol. 80, , pp. 1111–1120.
Shirazi, E., & Jadid, S. (2019). A multiagent design for self-healing in electric power distribution systems. Electric Power Systems Research, 171, 230-239.
Singh, Bindeshwar, and Pankaj Kumar Dubey (2022). Distributed power generation planning for distribution networks using electric vehicles: Systematic attention to challenges and opportunities. Journal of Energy Storage 48: 104030.
Yadav, A., & Dash, Y. (2014). An overview of transmission line protection by artificial neural network: fault detection, fault classification, fault location, and fault direction discrimination. Advances in Artificial Neural Systems, 2014.
Zadehbagheri, M., & Payedar, A. (2015). The feasibility study of using space vector modulation inverters in two-level of integrated photovoltaic system. TELKOMNIKA Indonesian Journal of Electrical Engineering, 14(2), 205-214.
Zadehbagheri, M., Ildarabadi, R., Nejad, M. B., & Sutikno, T. (2017). A new structure of dynamic voltage restorer based on asymmetrical γ-source inverters to compensate voltage disturbances in power distribution networks. International Journal of Power Electronics and Drive Systems, 8(1), 344.
Zadehbagheri, M., Kiani, M. J., Sutikno, T., & Moghadam, R. A. (2022). Design of a new backstepping controller for control of microgrid sources inverter. International Journal of Electrical & Computer Engineering (2088-8708), 12(4). | ||
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