Abstract
This research focuses on load-forecasting using Cartesian Genetic Programming evolved Artificial Neural Networks (CGPANN) and load-balancing using Genetic Algorithm in an electrical system. An unbalanced load in a distribution feeder has adverse effects on the system. All the transformer units connected to the feeder have different operating loads, and the system’s overall behaviour depends on them. Even if the transformers are not overloaded, any feeder phase can become overloaded due to excessive load contributed by individual transformers on that phase which results in a system-wide blackout. A custom-built monitoring device is installed on each transformer to monitor real-time electrical load data. A switching mechanism introduced at the transformer level can interchange the load between phases. This combination helps eliminate excessive load conditions and minimize unbalanced load conditions. Considering the involved parameters, i.e. transformers, phases, switching possibilities, and operating load, this constitutes a search problem within an available solution set. An optimum solution could be searched for and identified by interchanging the loads. This study aims to develop a feasible algorithm for such a search problem. The developed Genetic Algorithm can arrive at an optimum solution in minimum iterations. The load-forecasting model is used to predict load and identify system anomalies while the load-balancing model can re-adjust the system by shifting loads on individual transformers so as to balance the feeder’s overall load with no excessive load condition.
Supported by National Centre of Artificial Intelligence, UET Peshawar.
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References
Shokooh, F., Dail, J.: An intelligent load shedding (ILS) system application in a large industrial facility. In: IEEE IAS (2005)
Tang, J., Liu, J.: Adaptive load shedding based on combined frequency and voltage stability assessment using synchrophasor measurements. IEEE Trans. Power Syst. 28 (2013)
Xu, Y., Tolbert, L., Kueck, J., Rizy, D.: Voltage and current unbalance compensation using a static VAR compensator. IEEE Trans. Power Electron. 3, 977–988 (2010)
Sahito, A., Memon, Z., Shaikh, P., Raj, A.: Unbalanced loading: an overlooked contributor to power losses in HESCO. Sindh Univ. Res. J. (Sci. Ser.) 47, 779–782 (2015)
Hatziargyriou, N., Asano, H., Iravani, R., Marnay, C.: Microgrids. Energy Power Mag. 5, 78–94 (2007)
Kim, G., Hwang, C., Jeon, J., Ahn, J., Kim, E.: A novel three-phase four-leg inverter based load unbalance compensator for stand-alone microgrid. Int. J. Electr. Power Energy Syst. 65, 70–75 (2015)
Shahnia, F., Majumder, R., Ghosh, A., Ledwich, G., Zare, F.: Operation and control of a hybrid microgrid containing unbalanced and nonlinear loads. Electr. Power Syst. Res. 80, 954–965 (2010)
Li, Y., Vilathgamuwa, D., Loh, P.: Microgrid power quality enhancement using a three-phase four-wire grid interfacing compensator. IEEE Trans. Ind. Appl. 41, 1707–1719 (2005)
Gyugyi, L.: Power electronics in electric utilities: static VAR compensators. Proc. IEEE 76, 483–494 (2002)
Kern, A., Schroder, G.: A novel approach to power factor control and balancing problems. Ind. Electron. Control Instrum. (1994)
El-Sadek, M.: Balancing of unbalanced loads using static VAR compensators. Electric Power Syst. Res. 12, 137–148 (1987)
Rao, K., Bhat, H.: A novel grading scheme for loads to optimize load shedding using genetic algorithm in a smart grid environment. In: IEEE ISGT Asia (2013)
Shokooh, S., Khandelwal, T., Shokooh, F., Tastet, J., Dai, J.: Intelligent load shedding need for a fast and optimal solution. In: IEEE PCIC Europe (2005)
Mortaji, H., Ow, S., Moghavvemi, M., Almurib, H.: Smart grid demand response management using Internet of Things for load shedding and smart-direct load control. IEEE Trans. Ind. Appl. 53, 7 (2017)
Azasoo, J., Boateng, K.: Optimizing the effects of load-shedding through micro-load management in generation. In: 2nd IEEE International Conference on Computer and Communication, Chengdu (2016)
Ali, M., Yousaf, A., Usman, F.: Designing and simulation of load control & monitoring system through demand side management technique. In: The 8th International Renewable Energy Congress (IREC 2017), Jordan (2017)
Miller, J.: Cartesian genetic programming. In: Miller, J. (eds.) Genetic Programming, Natural Computing Series. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-17310-3_2
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This research became possible with the support of National Centre of Artificial Intelligence, UET Peshawar which provided all the resources and equipment to collect data for this study and conclude with results that have practical value and could be deployed as a solution.
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Khan, M.F., Khan, G.M. (2023). Load-Shedding Management in a Smart Grid Architecture Through Smart Metering. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_11
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