Optimization of power quality monitors in transmission system network
Power quality disturbances cause a great financial loss in the order of billions worldwide due to population growth, more sensitive devices and the significant usage of electricity. The power quality monitoring system aimed at determining the causes and the classification of power quality disturbanc...
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| 格式: | Thesis |
| 語言: | English |
| 出版: |
2015
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| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/78667/1/AbubakarKabir%20AliyuMFKE2015.pdf |
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| 總結: | Power quality disturbances cause a great financial loss in the order of billions worldwide due to population growth, more sensitive devices and the significant usage of electricity. The power quality monitoring system aimed at determining the causes and the classification of power quality disturbances so that proper action can be taken. Among all power disturbances, voltage sags are considered as the most frequent and severe type of disturbances that lead to loss of operation of equipment’s. The power quality monitoring system is the first to consider in power quality assessment and mitigation so as to get a reliable and efficient power supply. Installing power quality monitors (PQM) in every component of the power system network is not feasible due to economic reasons and its need to be minimized. And then how to get the optimal number and locations of power quality monitors while maintaining system observability becomes an important problem. The aim of this research is to find the optimal number and the best location of power quality monitors in the system network. The IEEE 14 bus test system was modelled and analyzed using POWERWORLD software so as to obtain fault voltage and monitor reach area matrixes considering balanced and unbalanced faults in the system. The optimization formulation problem is also formed and solved using a MATLAB toolbox of an integer programming algorithm and sag occurrence value is used to find the best placement position. Finally, this research end with the comparison with the MATLAB toolbox of genetic algorithm. Thus, both the IP and GA techniques give the same optimal number for a different set threshold value. However, for a threshold value of 0.9 p.u ,the optimal number of PQM is 1 for each of the simulated fault type in the system and different number of PQM for a threshold value of 0.55p.u and o.2p.u depending on the sensitivity of voltage sag occurrences of each of the simulated fault type in the system. |
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