Transmission line planning using global best artificial bee colony method
DOI:
https://doi.org/10.20998/2074-272X.2023.5.12Keywords:
artificial intelligence, artificial bee colony, transmission line network planning, load flow analysisAbstract
Introduction. Network expansion, substation planning, generating expansion planning, and load forecasting are all aspects of modern power system planning. The aim of this work is to solve network planning considering both future demand and all equality and inequality constraints. The transmission network design problem for the 6-bus system is considered and addressed using the Global Best Artificial Bee Colony (GABC) method in this research. The program is written in the Matrix Laboratory in MATLAB environment using the proposed methodology. Novelty of the work consist in considering the behavior of bees to find food source in most optimized way in nature with feature of user based accuracy selection and speed of execution selection on any scale of the system to solve Transmission Lines Expansion Problem (TLEP). The proposed method is implemented on nonlinear mathematical function and TLEP function. When demand grows, the program output optimally distributes new links between new generation buses and old buses, determines the overall minimum cost of those links, and determines if those linkages should meet power system limits. Originality of the proposed method is that it eliminated the need of load shedding while planning the future demand with GABC method. Results are validated using load flow analysis in electrical transient analyzer program, demonstrating that artificial intelligence approaches are accurate and particularly effective in non-linear transmission network planning challenges. Practical value of the program is that it can use to execute cost oriented complex transmission planning decision.
References
Desai J.P. Analysis of Power Swings and Blackouts. 2021 IEEE Congreso Estudiantil de Electrónica y Electricidad (INGELECTRA), 2021, pp. 1-6. doi: https://doi.org/10.1109/INGELECTRA54297.2021.9748070.
Seifi H., Sepasian M.S. Electric Power System Planning: Issues, Algorithms and Solutions. Springer Science & Business Media, 2011.
Rathore C., Roy R. Load uncertainty based transmission network expansion planning. 2013 3rd International Conference on Electric Power and Energy Conversion Systems, 2013, pp. 1-6. doi: https://doi.org/10.1109/EPECS.2013.6713034.
Mahdad B., Srairi K. Interactive artificial ecosystem algorithm for solving power management optimizations. Electrical Engineering & Electromechanics, 2022, no. 6, pp. 53-66. doi: https://doi.org/10.20998/2074-272X.2022.6.09.
Mehdi M.F., Ahmad A., Ul Haq S.S., Saqib M., Ullah M.F. Dynamic economic emission dispatch using whale optimization algorithm for multi-objective function. Electrical Engineering & Electromechanics, 2021, no. 2, pp. 64-69. doi: https://doi.org/10.20998/2074-272X.2021.2.09.
Desai J.P. Three-Terminal Transmission Line Protection By Considering Effect of Infeed Current. Journal of The Institution of Engineers (India): Series B, 2022, vol. 103, no. 6, pp. 2165-2175. doi: https://doi.org/10.1007/s40031-022-00795-7.
Razavi S.E., Arefi A., Ledwich G., Nourbakhsh G., Smith D.B., Minakshi M. From Load to Net Energy Forecasting: Short-Term Residential Forecasting for the Blend of Load and PV Behind the Meter. IEEE Access, 2020, vol. 8, pp. 224343-224353. doi: https://doi.org/10.1109/ACCESS.2020.3044307.
Garver L. Transmission Network Estimation Using Linear Programming. IEEE Transactions on Power Apparatus and Systems, 1970, vol. PAS-89, no. 7, pp. 1688-1697. doi: https://doi.org/10.1109/TPAS.1970.292825.
Gao W., Liu S., Huang L. A global best artificial bee colony algorithm for global optimization. Journal of Computational and Applied Mathematics, 2012, vol. 236, no. 11, pp. 2741-2753. doi: https://doi.org/10.1016/j.cam.2012.01.013.
Saif U., Guan Z., Zhang L., Mirza J., Lei Y. Hybrid Pareto artificial bee colony algorithm for assembly line balancing with task time variations. International Journal of Computer Integrated Manufacturing, 2017, vol. 30, no. 2-3, pp. 255-270. doi: https://doi.org/10.1080/0951192X.2016.1145802.
Desai J., Makwana V. Power Swing Blocking Algorithm based on Real and Reactive Power Transient Stability. Electric Power Components and Systems, 2020, vol. 48, no. 16-17, pp. 1673-1683. doi: https://doi.org/10.1080/15325008.2021.1906794.
Desai J.P., Makwana V.H. A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model. Protection and Control of Modern Power Systems, 2021, vol. 6, no. 1, art. no. 40. doi: https://doi.org/10.1186/s41601-021-00221-y.
Aoyang H., Shengqi Z., Xuehui J., Zhisheng Z. Short-term Load Forecasting Model Based on RBF Neural Network Optimized by Artificial Bee Colony Algorithm. 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2021, pp. 486-489. doi: https://doi.org/10.1109/ICBAIE52039.2021.9390043.
Bozogullarindan E., Bozogullarindan C., Ozturk C. Transfer Learning in Artificial Bee Colony Programming. 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, pp. 1-7. doi: https://doi.org/10.1109/ASYU50717.2020.9259801.
Mínguez R., García-Bertrand R., Arroyo J.M. Adaptive Robust Transmission Network Expansion Planning using Structural Reliability and Decomposition Techniques. Computational Engineering, Finance, and Science, 2015, 12 p. doi: https://doi.org/10.48550/arXiv.1501.06613.
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