CN103401273B - Wind energy turbine set feather type power of fan optimizing distribution method - Google Patents

Abstract

A kind of wind energy turbine set feather type power of fan optimizing distribution method, comprises the following steps: 1. add up wind turbine real power historical data, propeller pitch angle change histories data, power of fan rate of change historical data, propeller pitch angle rate of change historical data; 2. receiving scheduling power instruction, obtains wind power higher limit

Description

Wind energy turbine set feather type power of fan optimizing distribution method

Technical field:

The present invention relates to technical field of wind power generation, particularly a kind of wind energy turbine set feather type power of fan optimizing distribution method.

Background technology:

Electric power system is complicated dynamical system, and its safe and stable operation requires must the moment to keep balancing between generating and workload demand in essence.If electric power system can not control effectively and occur imbalance of supply and demand, the reliable electricity consumption affecting load even may be caused the large-scale accident of system.

Wind energy fluctuation is strong, when power division is carried out in large-scale wind power field, if can not carry out reasonable distribution, then easily causes power delivery to fluctuate, easily reduces grid stability.To the greatest extent in the past few years China confirms by distributing the importance improving wind power output stability to wind power optimization more to the raising that the New-energy power system quality of power supply requires.Due to wind power export unstable add scheduling commander's difficulty, the risk adding electrical network instability, reduce the quality of power supply, so just the stability of wind power output is had higher requirement.

In order to improve the ability of wind power output stability, extensively adopting at present and installing energy storage device additional in wind energy turbine set.But energy storage device is expensive, capacity is less, add the cost of electricity-generating of new forms of energy.

Summary of the invention:

Given this, be necessary to design a kind of wind energy turbine set feather type power of fan optimizing distribution method.

Factor power of fan being exported to impact is a lot, and the mainly uncontrollable factor such as geographical environment, climate change, therefore considers these factors, proposes following methods step:

A kind of wind energy turbine set feather type power of fan optimizing distribution method, comprises the following steps:

1. add up wind turbine real power historical data, propeller pitch angle change histories data, power of fan rate of change historical data, propeller pitch angle rate of change historical data, propeller pitch angle is large, and wind energy is higher, has the leeway of release during wind speed change; Wherein, blower fan is real sends out power historical data P _i, propeller pitch angle change histories data α _istatistical method be: push away H hour forward from current time, every M minute gets a value, altogether n sampling point, 1≤i≤n;

Blower fan is real sends out power historical data P _ialgorithm be prior art, be not repeated.

2. receiving scheduling power instruction, obtains wind power higher limit

3. send out power historical data according to wind turbine is real, carry out power prediction, and according to propeller pitch angle historical data, prediction wind turbine can transform the total amount p of wind energy.

According to wind turbine power variation rate historical data, calculate wind turbine wind power variation rate history accumulated value η, $\mathrm{\η}=\underset{1}{\overset{n}{\Σ}}\frac{P_i-P_{i-1}}{M};$

Calculate wind turbine current propeller pitch angle rate of change numerical value β,

4. calculate the power partition coefficient S of wind turbine, utilize the unit that propeller pitch angle is larger, like this when wind energy changes among a small circle, lower powered fluctuation can be fallen

S＝p×0.4+α _n×0.3+η×0.2+β×0.1。

5. preferential by power division to the large blower fan of power partition coefficient S, until wind power higher limit be assigned with.

Preferably, step 5. in, power be distributed on the basis of S, according to the pro rate of each blower fan prediction energy output, when the apportioning cost of certain blower fan be less than it and regulate lower limit time, apportioning cost regulates lower limit for it when being greater than its installed capacity time, be then its installed capacity mathematical Modeling is as follows:

$P_{WTi}^{DIS}\left(t\right)=P_{WTG}^{MAX}\left(t\right)\frac{P_{WTi}^{FOR}}{\underset{1}{\overset{N}{\Σ}}{P}_{WTi}^{FOR}}$

$s.t.\underset{i}{\Σ}{P}_{WTi}^{DIS}\left(t\right)\≤P_{WTG}^{MAX}\left(t\right)$

$P_{WTi}^{DIS}\left(t\right)<P_{WTi}^{MIN}\left(t\right)$ Time, $P_{WTi}^{DIS}\left(t\right)=P_{WTi}^{MIN}\left(t\right)$

$P_{WTi}^{DIS}\left(t\right)>P_{WTi}^{MAX}\left(t\right)$ Time, $P_{WTi}^{DIS}\left(t\right)=P_{WTi}^{MAX}\left(t\right)$

Wherein, for the wind power prediction data of blower fan i within these regulation and control period, available existing algorithm calculates, and N is blower fan total amount.

The performance synthesis that the optimization of wind energy turbine set feather type power of fan distributes considers wind turbine real power historical data, propeller pitch angle change histories data, power of fan rate of change historical data, propeller pitch angle rate of change historical data, judges the power stability of blower fan from the result of statistics.The impact that this statistical method has examined geographical environment to greatest extent, climate change exports wind power, and this impact is reacted in follow-up power division by propeller pitch angle.

Embodiment:

A kind of wind energy turbine set feather type power of fan optimizing distribution method, comprises the following steps:

1. wind turbine real power historical data, propeller pitch angle change histories data, power of fan rate of change historical data, propeller pitch angle rate of change historical data is added up; Wherein, blower fan is real sends out power historical data P _i, propeller pitch angle change histories data α _istatistical method be: push away H hour forward from current time, every M minute gets a value, altogether n sampling point, 1≤i≤n;

Blower fan is real sends out power historical data P _ialgorithm be prior art, be not repeated.

2. receiving scheduling power instruction, obtains wind power higher limit

3. send out power historical data according to wind turbine is real, carry out power prediction, and according to propeller pitch angle historical data, prediction wind turbine can transform the total amount p of wind energy;

According to wind turbine power variation rate historical data, calculate wind turbine wind power variation rate history accumulated value η, $\mathrm{\&eta;}=\underset{1}{\overset{n}{\&Sigma;}}\frac{P_i-P_{i-1}}{M};$

Calculate wind turbine current propeller pitch angle rate of change numerical value β,

4. the power partition coefficient S of wind turbine is calculated,

S＝p×0.4+α _n×0.3+η×0.2+β×0.1。

5. preferential by power division to the large blower fan of power partition coefficient S, until wind power higher limit be assigned with.

Preferably, step 5. in, power be distributed on the basis of S, according to the pro rate of each blower fan prediction energy output, when the apportioning cost of certain blower fan be less than it and regulate lower limit time, apportioning cost regulates lower limit for it when being greater than its installed capacity time, be then its installed capacity mathematical Modeling is as follows:

$P_{WTi}^{DIS}\left(t\right)=P_{WTG}^{MAX}\left(t\right)\frac{P_{WTi}^{FOR}}{\underset{1}{\overset{N}{\&Sigma;}}{P}_{WTi}^{FOR}}$

$s.t.\underset{i}{\&Sigma;}{P}_{WTi}^{DIS}\left(t\right)\&le;P_{WTG}^{MAX}\left(t\right)$

$P_{WTi}^{DIS}\left(t\right)<P_{WTi}^{MIN}\left(t\right)$ Time, $P_{WTi}^{DIS}\left(t\right)=P_{WTi}^{MIN}\left(t\right)$

$P_{WTi}^{DIS}\left(t\right)>P_{WTi}^{MAX}\left(t\right)$ Time, $P_{WTi}^{DIS}\left(t\right)=P_{WTi}^{MAX}\left(t\right)$

Wherein, for the wind power prediction data of blower fan i within these regulation and control period, available existing algorithm calculates, and N is blower fan total amount.

Claims (2)

1. a wind energy turbine set feather type power of fan optimizing distribution method, is characterized in that, comprise the following steps:

1. wind turbine real power historical data, propeller pitch angle change histories data, power of fan rate of change historical data, propeller pitch angle rate of change historical data is added up; Wherein, blower fan is real sends out power historical data P _i, propeller pitch angle change histories data α _istatistical method be: push away H hour forward from current time, every M minute gets a value, altogether n sampling point, 1≤i≤n;

2. receiving scheduling power instruction, obtains wind power higher limit

3. send out power historical data according to wind turbine is real, carry out power prediction, and according to propeller pitch angle historical data, prediction wind turbine can transform the total amount p of wind energy;

According to wind turbine power variation rate historical data, calculate wind turbine wind power variation rate history accumulated value η, $\mathrm{\&eta;}=\underset{1}{\overset{n}{\&Sigma;}}\frac{P_i-P_{i-1}}{M};$

Calculate wind turbine current propeller pitch angle rate of change numerical value β,

4. the power partition coefficient S of wind turbine is calculated,

S＝p×0.4+α _n×0.3+η×0.2+β×0.1；

5. preferential by power division to the large blower fan of power partition coefficient S, until wind power higher limit be assigned with.

2. wind energy turbine set feather type power of fan optimizing distribution method as claimed in claim 1, is characterized in that, step 5. in, power be distributed on the basis of S, according to the pro rate of each blower fan prediction energy output, when the apportioning cost of certain blower fan be less than it and regulate lower limit time, apportioning cost regulates lower limit for it when being greater than its installed capacity time, be then its installed capacity mathematical Modeling is as follows:

$P_{WTi}^{DIS}\left(t\right)=P_{WTG}^{MAX}\left(t\right)\frac{P_{WTi}^{FOR}}{\underset{1}{\overset{N}{\&Sigma;}}{P}_{WTi}^{FOR}}$

$s.t.\underset{i}{\&Sigma;}{P}_{WTi}^{DIS}\left(t\right)\&le;P_{WTG}^{MAX}\left(t\right)$

$P_{WTi}^{DIS}\left(t\right)<P_{WTi}^{MIN}\left(t\right)$ Time, $P_{WTi}^{DIS}\left(t\right)=P_{WTi}^{MIN}\left(t\right)$

$P_{WTi}^{DIS}\left(t\right)>P_{WTi}^{MAX}\left(t\right)$ Time, $P_{WTi}^{DIS}\left(t\right)=P_{WTi}^{MAX}\left(t\right)$

Wherein, for the wind power prediction data of blower fan i within these regulation and control period, N is blower fan total amount.