Voltage collapse point calculation method comprising wind power random fuzzy injection power system fluctuation
A technology of voltage collapse and power system, applied in the direction of system integration technology, information technology support system, instrument, etc.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Publication Date
- 2016-03-30
- Estimated Expiration
- Not applicable · inactive patent
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Abstract
Description
technical field
[0001] The invention belongs to the field of safe and stable operation of power systems, and considers multiple uncertainties of wind power injected into the power system, a method for solving fluctuating voltage collapse points. Background technique
[0002] With people's demand for new energy development, wind power has been valued as a clean and renewable energy. The total installed capacity of wind power in the current power system continues to rise, and the use of wind power is also continuing to follow up. The impact of multiple uncertainties of wind power output on the power grid is also becoming more and more obvious. Considering the impact of wind power output on the static security of the power grid is very important for maintaining the security and stability of the power system and improving the utilization of wind power.
[0003] The literature "Review on the Impact of Wind Power Uncertainty on Power Systems" pointed out that uncertainty has both...
Examples
Embodiment Construction
[0022] The present invention comprises the following steps:
[0023] 1. Stochastic fuzzy uncertainty modeling of wind power output
[0024] 1) Establish a random fuzzy uncertainty model of wind speed
[0025] According to the actual multi-year wind speed data, the probability distribution shape parameter k of the daily wind speed probability distribution parameter can be extracted using the triangular fuzzy variable ξ k =(1.14,1.75,3.64) means that the scale parameter c can adopt the trapezoidal fuzzy variable ξ c =(2.95, 4.40, 6.40, 8.22), the corresponding membership functions can be expressed by formulas (1) and (2) respectively:
[0026]
[0027]
[0028] According to the fuzzy and uncertain characteristics of daily wind speed probability distribution parameters extracted above and their membership functions, a random fuzzy and uncertain model of daily wind speed is established, and a parameter that satisfies Pos{} > 0 is extracted in the respective confidence inte...