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In the context of new power systems, represented by renewable energy sources such as wind and solar, low system inertia and high uncertainty have led to prominent issues with grid frequency stability. While new energy sources with virtual inertia control have improved frequency stability to some extent in lowinertia grids, they have simultaneously increased the difficulty of inertia assessment in the grid. Addressing the challenge where traditional online inertia monitoring methods struggle to accurately estimate synchronous machine rotational inertia alongside virtual inertia from new energy sources, this paper proposes a comprehensive estimation method for rotational and virtual inertia in power systems based on multiimportance sampling and Bayesian inference without requiring any linear assumptions. This approach utilizes local measurements from PMUs (Phasor Measurement Units) within a Bayesian inference framework and employs multiimportance sampling algorithms to sample from the nonGaussian posterior distribution of inertia parameters, ensuring the accuracy of inertia estimation. Simulation results demonstrate that this method exhibits high precision in online inertia estimation for both synchronous and asynchronous generators and can be widely applied in novel electric power systems dominated by new energy sources.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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