Optimal under voltage load shedding based on stability index by using artificial neural network
Power system is exceptionally sensitive at the generation and consumer side. Inconsistent power requirement under general power production environment may cause power system to approach breakdown or power outages. Load shedding is deliberated as the final choice from the numerous techniques which ha...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/397/1/24p%20SUNDARAJOO%20SHARMAN.pdf http://eprints.uthm.edu.my/397/2/SUNDARAJOO%20SHARMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/397/3/SUNDARAJOO%20SHARMAN%20WATERMARK.pdf |
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| Summary: | Power system is exceptionally sensitive at the generation and consumer side. Inconsistent power requirement under general power production environment may cause power system to approach breakdown or power outages. Load shedding is deliberated as the final choice from the numerous techniques which have been achieved to prevent voltage breakdown. Various studies have been led on this part of the issue. Still, there are possibilities for other ways through optimization of the load shedding. Consequently, the primary reason for this work is to come up with an optimal undervoltage load shedding strategy. Voltage stability is one of the significant worries in functional and preparation of present-day power system. Nevertheless, to obtain the lowest amount to be shed in order to avoid voltage instability, optimization is required. An algorithm was developed to shed the optimal load by considering the load priority whereby the load with least priority will be shed first. The algorithm is working in one step to shed the load. The developed algorithm was tested on IEEE 33-Bus and IEEE 69-Bus radial distribution systems. The results show the equal accuracy of the application of the developed algorithm. In this project, a powerful technique is exhibited for evaluating the optimal amount of load to be shed in a radial distribution system by using artificial neural network. The results of these test cases confirm that 6.57% of bus voltage is increased at the weakest bus in the IEEE 33-Bus system and 10.23% of bus voltage is increased at the weakest bus in the IEEE 69-Bus system. This optimal load shedding algorithm does not over shed or under shed the load. Other achievement includes reduction in load shedding steps. For each test case, the complete load shedding was achieved in 1 step only and the amount of load shed is suitable in each test case respectively. In this project, 29.4% of load is curtailed to stabilize the system which is less compared to other works where about 30% of load is shed to stabilize the system. |
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