Reactive power optimization for distribution network system with wind power based on improved multi-objective particle swarm optimization algorithm
•In the multi-objective particle swarm optimization algorithm, the adaptive mesh is introduced to reflect the density of particles, and according to the density information, the global optimal particles are selected by roulette mechanism and the scale of external repository is maintained, which effe...
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| Vydáno v: | Electric power systems research Ročník 213; s. 108731 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.12.2022
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| Témata: | |
| ISSN: | 0378-7796, 1873-2046 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | •In the multi-objective particle swarm optimization algorithm, the adaptive mesh is introduced to reflect the density of particles, and according to the density information, the global optimal particles are selected by roulette mechanism and the scale of external repository is maintained, which effectively ensures the uniformity and diversity of Pareto frontier distribution.•The proposed IMOPSOA has faster convergence speed and shorter average computing time than NSGA-II algorithm, can get Pareto frontier with better distribution and better results, which makes the voltage stability of the distribution network system with wind power higher.
Aiming at the uncertainty of the grid-connected output of wind turbines, a scenario analysis method based on probability occurrence is used to transform the uncertainty model into multi scenario problems with different occurrence probabilities, a reactive power optimization model is established with the goal of minimizing the active power network loss and voltage deviation. Aiming at the poor diversity of Pareto frontiers obtained by traditional methods, an improved multi-objective particle swarm optimization algorithm is proposed. The algorithm uses adaptive grids to obtain the density of particles in external archives, selects the global optimal particle and maintains the scale of the external repository according to the density information using a roulette mechanism, effectively ensuring the uniformity and diversity of the Pareto frontier distribution. The algorithm is used to calculate reactive power optimization of the IEEE 33-bus system with wind power, and compared with the existing NSGA-Ⅱ algorithm. The results show that the Pareto frontier obtained by the proposed algorithm is better, the voltage stability and active power loss reduction rate of the distribution network system with wind power is higher. |
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| ISSN: | 0378-7796 1873-2046 |
| DOI: | 10.1016/j.epsr.2022.108731 |