CN104763585B - Wind turbines dynamic reconfiguration method based on distributed data collection - Google Patents
Wind turbines dynamic reconfiguration method based on distributed data collection Download PDFInfo
- Publication number
- CN104763585B CN104763585B CN201510063090.3A CN201510063090A CN104763585B CN 104763585 B CN104763585 B CN 104763585B CN 201510063090 A CN201510063090 A CN 201510063090A CN 104763585 B CN104763585 B CN 104763585B
- Authority
- CN
- China
- Prior art keywords
- wind
- wind turbine
- data
- working mode
- server
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000013480 data collection Methods 0.000 title claims abstract description 24
- 230000002776 aggregation Effects 0.000 claims abstract description 17
- 238000004220 aggregation Methods 0.000 claims abstract description 17
- 238000012545 processing Methods 0.000 claims abstract description 9
- 230000000737 periodic effect Effects 0.000 claims abstract 2
- 230000008447 perception Effects 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 241000854291 Dianthus carthusianorum Species 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 7
- 238000011160 research Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000002411 adverse Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Wind Motors (AREA)
Abstract
针对风电场内各风电机组随风速、风向变化不一致的现象，本发明公开了一种基于分布式数据收集的风电机组动态重构方法。主要步骤：在一个周期内首先由分布式传感器收集各风机的风速和风向数据，再依次上传给区域汇聚节点、风机服务器和风电机组控制中心并进行处理，然后风电机组控制中心根据风向方位调整风电机组的迎风方向，并根据风速权值完成风电机组工作模式匹配重构，最后将重构信息传至各风机服务器，并完成各风机的工作模式调整。本发明采用周期性的工作方式，能实现风电机组工作模式动态重构和风机的动态调整，达到风电场对风电机组的动态分布式管理与控制的目的。
Aiming at the phenomenon that wind turbines in a wind farm are inconsistent with changes in wind speed and wind direction, the invention discloses a dynamic reconfiguration method for wind turbines based on distributed data collection. Main steps: In one cycle, the wind speed and direction data of each wind turbine are first collected by distributed sensors, and then uploaded to the regional aggregation node, wind turbine server and wind turbine control center in turn for processing, and then the wind turbine control center according to the wind direction and direction Adjust the windward direction of the wind turbine, and according to the wind speed weight Complete the matching reconstruction of the working mode of the wind turbine, and finally transmit the reconstruction information to the server of each wind turbine, and complete the adjustment of the working mode of each wind turbine. The invention adopts a periodic working mode, can realize the dynamic reconstruction of the working mode of the wind turbine and the dynamic adjustment of the wind turbine, and achieve the purpose of dynamic distributed management and control of the wind turbine by the wind farm.
Description
技术领域technical field
本发明涉及一种基于分布式数据收集的风电机组动态重构方法，特别涉及一种用分布式数据收集方法收集随机风向和风速并动态重构风电机组的方法。The invention relates to a dynamic reconfiguration method of a wind turbine based on distributed data collection, in particular to a method for dynamically reconfiguring a wind turbine by using a distributed data collection method to collect random wind directions and wind speeds.
背景技术Background technique
风力发电是通过风电机组将风能转化成电能的能量转换方式，风是风电机组的动力来源。由于风的随机性、间歇性、波动性和不可控性等自然特性引起风电机组输出功率的随机波动，这种波动无论对风电机组本身还是对与之相连的电力系统，都将产生一定程度的影响。随着我国近年来电力技术的快速发展，进行风电场接入电力系统的研究和评估、开展系统及关键点接纳电力的研究是当前风电发展中迫切需要解决的重要问题。Wind power generation is an energy conversion method that converts wind energy into electrical energy through wind turbines, and wind is the power source of wind turbines. Due to the randomness, intermittency, volatility and uncontrollability of the wind, the random fluctuation of the output power of the wind turbine will cause a certain degree of damage to both the wind turbine itself and the power system connected to it. influences. With the rapid development of power technology in my country in recent years, the research and evaluation of wind farm access to the power system, and the research on the system and key points to receive power are important issues that need to be solved urgently in the current development of wind power.
从电力系统的角度来看，对风电场进行研究所关心的不是风电场内部每个风电机组的特性，而是风电场作为一个整体的动态特性以及对电力系统的影响，在风电场接入电力系统的分析中不可能也没有必要把每个风电机组都作为一个单独内容进行分析，且随着风电场的规模越来越大，这一特点越来越明显。但是，风电场不同于常规的发电厂，风电场是由大量分散布置的风电机组群组成的，因此风电场内每个风电机组的输入风速(来风风速和来风风向)因安装地点不同而具有明显差异，造成同一时刻风电场内风电机组的运行状态不完全相同，因此，对风电场内每组风电机组的分布式数据收集、分析及控制的研究变得十分重要。From the perspective of the power system, the research on the wind farm is not concerned with the characteristics of each wind turbine inside the wind farm, but the dynamic characteristics of the wind farm as a whole and its impact on the power system. It is impossible and unnecessary to analyze each wind turbine as a separate content in the analysis of the system, and this feature becomes more and more obvious as the scale of wind farms becomes larger and larger. However, wind farms are different from conventional power plants. Wind farms are composed of a large number of scattered wind turbines. Therefore, the input wind speed (incoming wind speed and incoming wind direction) of each wind turbine in the wind farm is different depending on the installation location. However, there are obvious differences, resulting in different operating states of the wind turbines in the wind farm at the same time. Therefore, it is very important to study the distributed data collection, analysis and control of each group of wind turbines in the wind farm.
分布式数据收集技术是一种以数据采集、传感器网络技术、以及存储测试技术为基础的综合数据采集技术。其广泛应用于船舶、飞机等采集数据多、实时要求高的场合；其针对系统智能化、网络程度不高对应变测量点多、规模大等引起的数据收集低效率等缺陷，能有效优化并降低成本，对系统综合性能有重要提升。Distributed data collection technology is a comprehensive data collection technology based on data collection, sensor network technology, and storage testing technology. It is widely used in ships, aircrafts and other occasions where there is a lot of collected data and high real-time requirements; it can effectively optimize and optimize the system for the defects of intelligent system, low network level and low efficiency of data collection caused by many strain measurement points and large scale. The cost is reduced, and the comprehensive performance of the system is greatly improved.
而在当前国内外风力发电的研究中，对于相关的处理方法有：In the current research on wind power generation at home and abroad, the related processing methods are as follows:
1)将风电场所有风电机组处理输入风速的情况归为一类：即所有风电机组的输入风速一致；1) Classify the input wind speeds of all wind turbines in the wind farm into one category: that is, the input wind speeds of all wind turbines are the same;
2)考虑到风电机组的输入风速不一致，但仍然把输入风速相同的某些风电机组归为一组，没有考虑风向因素；2) Considering that the input wind speeds of the wind turbines are inconsistent, some wind turbines with the same input wind speed are still grouped together without considering the wind direction factor;
3)考虑到风电机组的输入风速不一致，但没有考虑整个风电场的动态调整，导致发电机组产生不良影响及电网运行不稳定等缺陷。3) Taking into account the inconsistent input wind speed of wind turbines, but not considering the dynamic adjustment of the entire wind farm, resulting in defects such as adverse effects of generators and unstable operation of the power grid.
综上所述，将分布式数据收集技术应用于风电机组中且同时研究输入风速(来风风速和来风风向)并分别调整风电机组乃至考虑整个风电场性能的研究少之又少，这可能会影响到风电场相关研究的准确性，因此，一种基于分布式数据收集的风电机组动态重构方法的建立对风电机组的动态重构就变得非常必要和具有现实意义。In summary, there are very few studies that apply distributed data collection technology to wind turbines and simultaneously study the input wind speed (incoming wind speed and wind direction) and adjust the wind turbines separately or even consider the performance of the entire wind farm. Therefore, the establishment of a dynamic reconfiguration method for wind turbines based on distributed data collection becomes very necessary and has practical significance for the dynamic reconfiguration of wind turbines.
发明内容Contents of the invention
本发明在风电机组发展的基础上，针对现有技术存在的不足以及实际的应用需求，提供了一种基于分布式数据收集的风电机组动态重构方法，该方法利用分布式数据收集方法收集输入风速并动态重构风电机组发电，达到稳定电网输出电能的效果。On the basis of the development of wind turbines, the present invention provides a dynamic reconfiguration method for wind turbines based on distributed data collection, aiming at the deficiencies of the existing technology and actual application requirements. The method uses the distributed data collection method to collect input Wind speed and dynamic reconstruction of wind turbine power generation to achieve the effect of stabilizing the output power of the grid.
根据本发明的一个方面，考虑到风电场来风风速(后面简称风速，变化范围为0m/s-25m/s)和来风风向(后面简称风向，变化范围为0°-360°)的变化范围比较宽，并且伴随随机不确定性的特性，提供一种基于分布式数据收集的风电机组动态重构方法，具体步骤如下：According to one aspect of the present invention, considering the change of the incoming wind speed of the wind farm (hereinafter referred to as wind speed, the range of variation is 0m/s-25m/s) and the direction of incoming wind (hereinafter referred to as wind direction, the range of variation is 0°-360°) The range is relatively wide, and with the characteristics of random uncertainty, a dynamic reconfiguration method for wind turbines based on distributed data collection is provided. The specific steps are as follows:
1、在各风机周围布置传感器以及对风速和风向进行分布式数据收集并分别作相应处理。1. Arrange sensors around each wind turbine and collect distributed data on wind speed and wind direction and process them accordingly.
布置传感器：对于实际运行的风电场来说，场内每个风电机组的工作模式随风速和风向的变化而变化，而风电机组由许多风机构成，因此在模拟的各风机周围分别布置M个风速传感器和N个风向传感器，各传感器在一个周期T内采集K次数据，并将数据上传给区域汇聚节点，区域汇聚节点分别处理风速和风向信息；传感器分别对风速和风向信息进行分布式数据收集，并分别作相应处理，具体过程如下：Arrangement of sensors: For a wind farm in actual operation, the working mode of each wind turbine in the field changes with the change of wind speed and wind direction, and the wind turbine is composed of many wind turbines, so M wind turbines are arranged around each simulated wind turbine. Wind speed sensor and N wind direction sensors, each sensor collects data K times in a period T, and uploads the data to the regional convergence node, the regional convergence node processes wind speed and wind direction information respectively; the sensors perform distributed data processing on wind speed and wind direction information respectively Collect and process accordingly, the specific process is as follows:
1)计算节点ni与风机kj的相关度D为传感器之间的最远距离，dij为传感器与风机之间的距离；1) Calculate the correlation between node n i and fan k j D is the farthest distance between the sensors, d ij is the distance between the sensor and the fan;
2)节点按相关度分为3个区域：当RT＜R(ni,kj)≤1，为有效感知区域；当RT/4≤R(ni,kj)≤1，为模糊感知区域；当0≤R(ni,kj)≤RT/4，为无效感知区域；且0＜RT＜1；2) The nodes are divided into three areas according to the degree of correlation: when R T <R(n i ,k j )≤1, it is the effective sensing area; when R T /4≤R(n i ,k j )≤1, it is Fuzzy perception area; when 0≤R(n i , k j )≤R T /4, it is an invalid perception area; and 0<R T <1;
3)设置有效感知区域内的节点采样周期和频率，触发方式为同步触发，每周期上传一次数据至风机服务器；3) Set the sampling cycle and frequency of nodes in the effective sensing area, the trigger mode is synchronous triggering, and upload data to the fan server once per cycle;
4)对模糊感知区域节点按随机方式分成m组，不同分组在一个周期T内依次采集数据，每组采集周期为T/m，采集次数为 4) The nodes in the fuzzy perception area are randomly divided into m groups, and different groups collect data sequentially within a period T, the collection period of each group is T/m, and the number of collections is
5)无效感知区域内的节点，不参与对应范围内的风机数据采集；5) Nodes in the invalid sensing area do not participate in the fan data collection within the corresponding range;
6)将采集到的数据上传至区域汇聚节点。6) Upload the collected data to the regional aggregation node.
2、首先，设置区域汇聚节点；然后，区域汇聚节点分析处理风向和风速数据，并将处理后的数据上传至风机服务器存储，步骤如下：2. First, set up the regional aggregation node; then, the regional aggregation node analyzes and processes the wind direction and wind speed data, and uploads the processed data to the wind turbine server for storage. The steps are as follows:
1)对有效感知区域和模糊感知区域的节点以大小相同的正六边形形成分簇，采用随机方式选取簇头并作为区域汇聚节点；1) The nodes in the effective sensing area and the fuzzy sensing area are clustered with regular hexagons of the same size, and the cluster heads are randomly selected as the regional convergence nodes;
2)区域汇聚节点计算并对比当前周期采集数据与前一周期风向方位和风速权值D(v)的差值：对于风向，若差值大于或等于门限则上传数据，否则丢弃数据；对于风速，若差值大于或等于门限θT，则上传数据，否则丢弃数据；2) The regional convergence node calculates and compares the data collected in the current cycle with the wind direction and direction of the previous cycle The difference with the wind speed weight D(v): For the wind direction, if the difference is greater than or equal to the threshold Then upload the data, otherwise discard the data; for wind speed, if the difference is greater than or equal to the threshold θ T , then upload the data, otherwise discard the data;
3)区域汇聚节点将处理后的数据上传至风机服务器进行存储。3) The regional aggregation node uploads the processed data to the fan server for storage.
3、风电机组控制中心分别均值化处理所有风机服务器上传的风速和风向信息，并根据风速权值D(v)判断整个风电机组的工作模式是否一致，如果不一致则执行风电机组工作模式重构，最后将重构信息传至各风机服务器，各风机服务器根据风向方位动态调整风电机组的迎风方向，完成工作模式的调整，即实现了风机的分布式管理与控制，具体步骤如下：3. The wind turbine control center averages the wind speed and wind direction information uploaded by all wind turbine servers, and judges whether the working mode of the entire wind turbine is consistent according to the wind speed weight D(v). If not, it performs the reconstruction of the working mode of the wind turbine. Finally, the reconstruction information is transmitted to each wind turbine server, and each wind turbine server Dynamically adjust the windward direction of the wind turbine and complete the adjustment of the working mode, that is, realize the distributed management and control of the wind turbine. The specific steps are as follows:
1)风电机组控制中心处理所有风机服务器上传的数据：1) The wind turbine control center processes the data uploaded by all wind turbine servers:
a)分别均值化处理风向数据γ(t)和风速数据v(t)；a) Meaning processing of wind direction data γ(t) and wind speed data v(t) respectively;
b)将均值化处理后的风向数据γ(t)转换为风向方位其中γ(t)∈[0°,360°)具体如下：b) Convert the mean valued wind direction data γ(t) into wind direction and azimuth Where γ(t)∈[0°,360°) is as follows:
将360°的风向均匀分成l等份，设定正北方向为0°的风向，按顺时针方向计算角度，则可将风向数据γ(t)表示为风向方位 Divide the 360° wind direction into l equal parts, set the north direction as the wind direction of 0°, and calculate the angle according to the clockwise direction, then the wind direction data γ(t) can be expressed as the wind direction azimuth
则的取值为{0,1,2,...,l-1}；but The value of is {0,1,2,...,l-1};
c)将均值化处理后的风速数据v(t)转换为风速权值D(v)，转换公式如下：c) Convert the mean valued wind speed data v(t) into wind speed weight D(v), the conversion formula is as follows:
VT为风电机组额定功率上限值，则D(v)的取值为{1,2,3,4}；V T is the upper limit value of the rated power of the wind turbine, and the value of D(v) is {1,2,3,4};
2)风电机组控制中心根据风速权值D(v)匹配风电机组的工作模式，并执行风电机组工作模式的匹配重构：2) The wind turbine control center matches the working mode of the wind turbine according to the wind speed weight D(v), and performs the matching reconstruction of the working mode of the wind turbine:
a)预先设定风电机组的工作模式：风电机组控制中心根据风速权值设定风电机组的四种工作模式：当风速权值分别为1、2、3和4时，工作模式分别为无风模式、弱风模式、中风模式和强风模式；a) Preset the working mode of the wind turbine: the wind turbine control center sets four working modes of the wind turbine according to the wind speed weights: when the wind speed weights are 1, 2, 3 and 4 respectively, the working modes are no wind mode, weak wind mode, medium wind mode and strong wind mode;
b)风电机组控制中心根据风速权值D(v)判断风电机组的工作模式是否一致，如果不一致则重构风电机组工作模式，否则保持原有工作模式；b) The wind turbine control center judges whether the working modes of the wind turbines are consistent according to the wind speed weight D(v), and if not, reconstructs the working modes of the wind turbines, otherwise maintains the original working mode;
3)若风电机组控制中心重构风电机组工作模式，则将重构信息传至各风机服务器，并完成各风机的工作模式调整，从而实现一个周期内风电机组对风机的分布式管理与控制：3) If the wind turbine control center reconfigures the working mode of the wind turbine, it will transmit the reconstruction information to each wind turbine server, and complete the adjustment of the working mode of each wind turbine, so as to realize the distributed management and control of the wind turbine by the wind turbine within a cycle:
a)根据风向方位调整风机的迎风方向；a) according to the wind direction Adjust the windward direction of the fan;
b)调整风机的工作模式。b) Adjust the working mode of the fan.
4、完成一个周期的风电机组工作模式重构后，将周期性地重复步骤1-3即完成风电机组工作模式的动态重构及对风机的动态分布式管理与控制。4. After completing a period of reconfiguration of the working mode of the wind turbine, the steps 1-3 will be repeated periodically to complete the dynamic reconfiguration of the working mode of the wind turbine and the dynamic distributed management and control of the wind turbines.
附图说明Description of drawings
图1为本发明的方法流程图。Fig. 1 is a flow chart of the method of the present invention.
具体实施方式detailed description
由本发明的方法流程图1可知，本发明技术方案的具体步骤为：As can be seen from method flowchart 1 of the present invention, the concrete steps of technical solution of the present invention are:
1、在各风机周围布置传感器以及其对风速和风向进行分布式数据收集并分别作相应处理。1. Arrange sensors around each wind turbine and collect distributed data on wind speed and wind direction and process them accordingly.
考虑到风电场内各风机风速和风向的随机不确定性，需要对各风机分别计算其的风速及风向信息，在各风机周围分别布置M个风速传感器和N个风向传感器，各传感器在一个周期T内采集K次数据，并将数据上传给区域汇聚节点；传感器分别对风速和风向信息进行分布式数据收集，并分别作相应处理，具体过程如下：Considering the random uncertainty of wind speed and wind direction of each wind turbine in the wind farm, it is necessary to calculate the wind speed and wind direction information of each wind turbine separately, and arrange M wind speed sensors and N wind direction sensors around each wind turbine. K times of data are collected within T, and the data is uploaded to the regional aggregation node; the sensors carry out distributed data collection on wind speed and wind direction information, and perform corresponding processing respectively. The specific process is as follows:
1)计算节点ni与风机kj的相关度D为传感器之间的最远距离，dij为传感器与风机之间的距离；1) Calculate the correlation between node n i and fan k j D is the farthest distance between the sensors, d ij is the distance between the sensor and the fan;
2)节点按相关度分为3个区域：当RT＜R(ni,kj)≤1，为有效感知区域；当RT/4≤R(ni,kj)≤1，为模糊感知区域；当0≤R(ni,kj)≤RT/4，为无效感知区域；且0＜RT＜1；2) The nodes are divided into three areas according to the degree of correlation: when R T <R(n i ,k j )≤1, it is the effective sensing area; when R T /4≤R(n i ,k j )≤1, it is Fuzzy perception area; when 0≤R(n i , k j )≤R T /4, it is an invalid perception area; and 0<R T <1;
3)设置有效感知区域内的节点采样周期和频率，触发方式为同步触发，每周期上传一次数据至风机服务器；3) Set the sampling cycle and frequency of nodes in the effective sensing area, the trigger mode is synchronous triggering, and upload data to the fan server once per cycle;
4)对模糊感知区域节点按随机方式分成m组，不同分组在一个周期T内依次采集数据，每组采集周期为为T/m，采集次数为 4) The nodes in the fuzzy perception area are randomly divided into m groups, and different groups collect data sequentially within a period T. The collection period of each group is T/m, and the number of collections is
5)无效感知区域内的节点，不参与对应范围内的风机数据采集；5) Nodes in the invalid sensing area do not participate in the fan data collection within the corresponding range;
6)将采集到的数据上传至区域汇聚节点。6) Upload the collected data to the regional aggregation node.
2、首先，设置区域汇聚节点；然后，区域汇聚节点分析处理风向和风速数据，并将处理后的数据上传至风机服务器存储，具体步骤如下：2. First, set the regional aggregation node; then, the regional aggregation node analyzes and processes the wind direction and wind speed data, and uploads the processed data to the wind turbine server for storage. The specific steps are as follows:
1)对有效感知区域和模糊感知区域的节点以大小相同的正六边形形成分簇，采用随机方式选取簇头并作为区域汇聚节点；1) The nodes in the effective sensing area and the fuzzy sensing area are clustered with regular hexagons of the same size, and the cluster heads are randomly selected as the regional convergence nodes;
2)区域汇聚节点计算并对比当前周期采集数据与前一周期风向方位和风速权值D(v)的差值：对于风向，若差值大于或等于门限则上传数据，否则丢弃数据；对于风速，若差值大于或等于门限θT，则上传数据，否则丢弃数据；2) The regional convergence node calculates and compares the data collected in the current cycle with the wind direction and direction of the previous cycle The difference with the wind speed weight D(v): For the wind direction, if the difference is greater than or equal to the threshold Then upload the data, otherwise discard the data; for wind speed, if the difference is greater than or equal to the threshold θ T , then upload the data, otherwise discard the data;
3)区域汇聚节点将处理后的数据上传至风机服务器进行存储。3) The regional aggregation node uploads the processed data to the fan server for storage.
3、风电机组控制中心均值处化理所有风机服务器上传的风速和风向信息，根据风速权值D(v)判断整个风电机组的工作模式是否一致，如果不一致则执行风电机组工作模式重构，最后将重构信息传至各风机服务器，各风机服务器根据风向方位动态调整风电机组的迎风方向，完成工作模式的调整，即实现了风机的分布式管理与控制，具体步骤如下：3. The average value processing of the wind turbine control center processes the wind speed and wind direction information uploaded by all wind turbine servers, and judges whether the working mode of the entire wind turbine is consistent according to the wind speed weight D(v). The reconstruction information is transmitted to each wind turbine server, and each wind turbine server Dynamically adjust the windward direction of the wind turbine and complete the adjustment of the working mode, that is, realize the distributed management and control of the wind turbine. The specific steps are as follows:
1)风电机组控制中心处理所有风机服务器上传的数据：1) The wind turbine control center processes the data uploaded by all wind turbine servers:
a)分别均值化处理风向数据γ(t)和风速数据v(t)；a) Meaning processing of wind direction data γ(t) and wind speed data v(t) respectively;
b)将均值化处理后的风向数据γ(t)转换为风向方位其中γ(t)∈[0°,360°)具体如下：b) Convert the mean valued wind direction data γ(t) into wind direction and azimuth Where γ(t)∈[0°,360°) is as follows:
将360°的风向均匀分成l等份，设定正北方向为0°的风向，按顺时针方向计算角度，则可将风向数据γ(t)表示为风向方位 Divide the 360° wind direction into l equal parts, set the north direction as the wind direction of 0°, and calculate the angle according to the clockwise direction, then the wind direction data γ(t) can be expressed as the wind direction azimuth
则的取值为{0,1,2,...,l-1}；but The value of is {0,1,2,...,l-1};
本发明中当l＝10时，可表示如下：When l=10 in the present invention, can Expressed as follows:
则的取值为{0,1,2,3,4,5,6,7,8,9}；but The value of is {0,1,2,3,4,5,6,7,8,9};
c)将均值化处理后的风速数据v(t)转换为风速权值D(v)，转换公式如下：c) Convert the mean valued wind speed data v(t) into wind speed weight D(v), the conversion formula is as follows:
VT为风电机组额定功率上限值。V T is the upper limit value of the wind turbine rated power.
本发明中当VT＝15时，D(v)可表示如下：When V T =15 in the present invention, D(v) can be expressed as follows:
则D(v)的取值为{1,2,3,4}；Then the value of D(v) is {1,2,3,4};
2)风电机组控制中心根据风速权值D(v)匹配风电机组的工作模式，并执行风电机组工作模式的匹配重构：2) The wind turbine control center matches the working mode of the wind turbine according to the wind speed weight D(v), and performs the matching reconstruction of the working mode of the wind turbine:
a)预先设定风电机组的工作模式：风电机组控制中心根据风速权值设定风电机组的四种工作模式：当风速权值分别为1、2、3和4时，工作模式分别为无风模式、弱风模式、中风模式和强风模式；a) Preset the working mode of the wind turbine: the wind turbine control center sets four working modes of the wind turbine according to the wind speed weights: when the wind speed weights are 1, 2, 3 and 4 respectively, the working modes are no wind mode, weak wind mode, medium wind mode and strong wind mode;
b)风电机组控制中心根据风速权值D(v)判断风电机组的工作模式是否一致，如果不一致则重构风电机组工作模式，否则保持原有工作模式；b) The wind turbine control center judges whether the working modes of the wind turbines are consistent according to the wind speed weight D(v), and if not, reconstructs the working modes of the wind turbines, otherwise maintains the original working mode;
3)若风电机组控制中心重构风电机组工作模式，则将重构信息传至各风机服务器，并完成各风机的工作模式调整，从而实现一个周期内风电机组对风机的分布式管理与控制：3) If the wind turbine control center reconfigures the working mode of the wind turbine, it will transmit the reconstruction information to each wind turbine server, and complete the adjustment of the working mode of each wind turbine, so as to realize the distributed management and control of the wind turbine by the wind turbine within a cycle:
a)根据风向方位调整风机的迎风方向；a) according to the wind direction Adjust the windward direction of the fan;
b)调整风机的工作模式。b) Adjust the working mode of the fan.
4、完成一个周期的风电机组工作模式重构后，将周期性的重复步骤1-3即完成风电机组工作模式的动态重构及对风机的动态分布式管理与控制。4. After completing a period of reconfiguration of the working mode of the wind turbine, periodically repeat steps 1-3 to complete the dynamic reconfiguration of the working mode of the wind turbine and the dynamic distributed management and control of the wind turbine.
综上所述，基于分布式数据收集的风电机组动态重构方法不仅能够克服风速和风向对风电机组造成不稳定输出电能的缺陷，还能保证风电机组在最大程度上利用风能发电，稳定电能输出。In summary, the dynamic reconfiguration method of wind turbines based on distributed data collection can not only overcome the defects of wind speed and direction that cause unstable output power of wind turbines, but also ensure that wind turbines can use wind energy to generate electricity to the greatest extent and stabilize power output. .
Claims (3)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510063090.3A CN104763585B (en) | 2015-02-06 | 2015-02-06 | Wind turbines dynamic reconfiguration method based on distributed data collection |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510063090.3A CN104763585B (en) | 2015-02-06 | 2015-02-06 | Wind turbines dynamic reconfiguration method based on distributed data collection |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104763585A CN104763585A (en) | 2015-07-08 |
| CN104763585B true CN104763585B (en) | 2017-12-22 |
Family
ID=53645614
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510063090.3A Expired - Fee Related CN104763585B (en) | 2015-02-06 | 2015-02-06 | Wind turbines dynamic reconfiguration method based on distributed data collection |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104763585B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105041571B (en) * | 2015-08-20 | 2018-02-23 | 沈阳华创风能有限公司 | The intelligence control system and its control method of prediction of wind speed wind direction |
| CN105649878B (en) * | 2015-12-31 | 2018-11-13 | 北京金风科创风电设备有限公司 | Wind energy capture method, device and wind power generating set for wind power generating set |
| CN107420269B (en) * | 2016-05-23 | 2019-12-13 | 远景能源（江苏）有限公司 | method for identifying a wind distribution pattern on a rotor plane and wind turbine implementing the method |
| CN107829863B (en) * | 2017-09-05 | 2019-07-16 | 昆明理工大学 | A natural water flow multi-point distributed power generation system and method |
| CN110094297B (en) | 2018-01-31 | 2020-04-14 | 北京金风科创风电设备有限公司 | Control method and control system for wind turbine based on sector |
| CN109697317B (en) * | 2018-12-21 | 2020-12-01 | 天津同阳科技发展有限公司 | Device and method for calculating wind direction |
| CN111866786B (en) * | 2020-07-09 | 2021-08-06 | 中国地质科学院地质力学研究所 | Monitoring system and method for karst pipeline flow |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102792240A (en) * | 2009-11-16 | 2012-11-21 | Nrg系统股份有限公司 | Data acquisition system for condition-based maintenance |
| CN103161669A (en) * | 2013-02-26 | 2013-06-19 | 上海电机学院 | System and method for monitoring operation of wind power plant |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7013203B2 (en) * | 2003-10-22 | 2006-03-14 | General Electric Company | Wind turbine system control |
| US8178986B2 (en) * | 2009-03-18 | 2012-05-15 | General Electric Company | Wind turbine operation system and method |
-
2015
- 2015-02-06 CN CN201510063090.3A patent/CN104763585B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102792240A (en) * | 2009-11-16 | 2012-11-21 | Nrg系统股份有限公司 | Data acquisition system for condition-based maintenance |
| CN103161669A (en) * | 2013-02-26 | 2013-06-19 | 上海电机学院 | System and method for monitoring operation of wind power plant |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104763585A (en) | 2015-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104763585B (en) | Wind turbines dynamic reconfiguration method based on distributed data collection | |
| CN103808509A (en) | Fan gear box fault diagnosis method based on artificial intelligence algorithm | |
| CN109492315B (en) | A Copula Function-Based Simulation Method for Space-Time Related Scenery Sequences | |
| CN107846014B (en) | Power grid probability vulnerability assessment method based on random features and series calculation | |
| CN106548256B (en) | A method and system for modeling the spatial-temporal dynamic correlation of wind farms | |
| CN109086527B (en) | Practical equivalent modeling method based on running state of wind turbine generator | |
| WO2019056753A1 (en) | Dynamic equivalent modeling method for distributed photovoltaic power station cluster | |
| CN104133143B (en) | A kind of Guangdong power system diagnostic system and method calculating platform based on Hadoop cloud | |
| CN106451567A (en) | Distributed power supply cluster dynamic partitioning method and system | |
| CN104600713A (en) | Device and method for generating day-ahead reactive power dispatch of power distribution network containing wind/photovoltaic power generation | |
| CN106684905B (en) | A kind of wind power plant Dynamic Equivalence considering wind-powered electricity generation uncertainty in traffic | |
| CN105590144A (en) | Wind speed prediction method and apparatus based on NARX neural network | |
| CN104036073A (en) | Double-fed wind power plant dynamic equivalence modeling method suitable for active power characteristic analysis | |
| CN104112167B (en) | Can power generating wind resource distribution acquisition methods | |
| CN102945318B (en) | A kind of ultra-short term wind speed dynamic prediction method based on cascade blower fan | |
| CN109409575A (en) | Wind power plant group of planes division methods based on Gap Statistic | |
| CN105825002B (en) | A kind of wind power plant dynamic equivalent modeling method based on dynamic Gray Association Analysis | |
| CN103440405A (en) | Method and system for steady-state modeling of wind power plant based on measured data | |
| Zhai et al. | Medium and long-term wind power prediction based on artificial fish swarm algorithm combined with extreme learning machine | |
| CN118283553A (en) | Wind farm multidimensional monitoring method and system based on wireless communication | |
| CN112287531A (en) | Offshore wind farm multi-state reliability obtaining method considering typhoon influence | |
| CN105743093A (en) | Power distribution network power flow calculation method considering wind power and photovoltaic power output uncertainty | |
| CN112101393B (en) | Wind farm fan clustering method and device | |
| Lin et al. | Clustering wind turbines for a large wind farm using spectral clustering approach based on diffusion mapping theory | |
| Wang et al. | Wind power curtailment evaluation based on EOF and hierarchical clustering method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| EXSB | Decision made by sipo to initiate substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20171222 |