CN115523103A - Fluctuation detection method and fluctuation detection device for wind power generating set operation data - Google Patents

Fluctuation detection method and fluctuation detection device for wind power generating set operation data Download PDF

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CN115523103A
CN115523103A CN202110702820.5A CN202110702820A CN115523103A CN 115523103 A CN115523103 A CN 115523103A CN 202110702820 A CN202110702820 A CN 202110702820A CN 115523103 A CN115523103 A CN 115523103A
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generating set
detection period
data
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wind generating
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马磊
霍钧
赵静菊
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Goldwind Science and Technology Co Ltd
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Xinjiang Goldwind Science and Technology Co Ltd
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03DWIND MOTORS
    • F03D17/00Monitoring or testing of wind motors, e.g. diagnostics
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/72Wind turbines with rotation axis in wind direction

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Abstract

公开了风力发电机组运行数据的波动检测方法和波动检测装置,所述波动检测方法包括:连续地对风力发电机组运行数据进行采样;计算预设检测周期内的风力发电机组运行数据的平均值;基于所述预设检测周期内的风力发电机组运行数据以及所述平均值,确定所述预设检测周期内的风力发电机组运行数据是否出现等间隔分布现象;响应于确定所述预设检测周期内的风力发电机组运行数据出现等间隔分布现象,输出指示所述预设检测周期内的风力发电机组运行数据发生正弦波动的信息。

Figure 202110702820

Disclosed are a fluctuation detection method and a fluctuation detection device for operating data of a wind power generating set. The fluctuation detecting method includes: continuously sampling the operating data of the wind generating set; calculating the average value of the operating data of the wind generating set within a preset detection period; Based on the wind power generating set operating data in the preset detection period and the average value, determine whether the wind power generating set operating data in the preset detection period is distributed at equal intervals; in response to determining the preset detection period The operating data of the wind power generating set within a period is distributed at equal intervals, and information indicating that the operating data of the wind generating set within the preset detection period occurs sinusoidal fluctuations is output.

Figure 202110702820

Description

风力发电机组运行数据的波动检测方法和波动检测装置Fluctuation detection method and fluctuation detection device for wind power generating set operation data

技术领域technical field

本公开总体说来涉及风力发电技术领域,更具体地讲,涉及风力发电机组运行数据的波动检测方法和波动检测装置。The present disclosure generally relates to the technical field of wind power generation, and more specifically, relates to a fluctuation detection method and a fluctuation detection device for operating data of a wind power generating set.

背景技术Background technique

随着风力发电机组容量的大型化,变桨距控制、变速恒频等先进风电技术已经成为当前风力发电机组的主流控制方式。发电机是风力发电机组中将风能转化为电能的重要装置,它不仅直接影响输出电能的质量和效率,也影响整个风力发电机组的性能和结构的复杂性。With the increasing capacity of wind turbines, advanced wind power technologies such as variable pitch control and variable speed constant frequency have become the mainstream control methods for wind turbines. The generator is an important device for converting wind energy into electrical energy in a wind turbine. It not only directly affects the quality and efficiency of the output electric energy, but also affects the performance and structure complexity of the entire wind turbine.

风力发电机组是一套比较复杂的系统。目前,MW级永磁风力发电机高度集成空气动力学、结构力学、电机学、材料科学、电力电子技术、电力系统分析、继电保护技术、自动控制技术和现代通讯等综合学科,成为一套复杂的能量转换系统,因此同一故障的发生,可能是不同的原因导致的。以变桨系统的“三轴角度不一致”故障为例,导致该故障的原因,可能是变桨系统的编码器本身故障,可能是编码器电源故障,可能是变桨系统卡桨,还可能是变桨驱动器故障。A wind turbine is a relatively complex system. At present, MW-level permanent magnet wind turbines are highly integrated with comprehensive disciplines such as aerodynamics, structural mechanics, electrical machinery, material science, power electronics technology, power system analysis, relay protection technology, automatic control technology and modern communications, and become a set of comprehensive disciplines. Complex energy conversion system, so the occurrence of the same failure may be caused by different reasons. Taking the "three-axis angle inconsistency" fault of the pitch system as an example, the cause of the fault may be the fault of the encoder itself of the pitch system, the fault of the encoder power supply, the jamming of the pitch system, or the Pitch drive failure.

目前,风力发电机组的故障检测通常采用单纯的数值比较,例如,当转速大于一定值时,或后备电源的电压低于一定值时触发故障。然而,风力发电机组的部件发生故障后,除了数值升高或降低外,在很多情况下还会发生频繁的数据波动并且风力发电机组停机后又会恢复正常。其结果是,一方面,故障上报后,运维人员不易查找故障的原因。另一方面,控制器中的定时器的特点是条件接通后,定时器开始计时,条件断开后,定时器停止工作并复位,而数据的频繁波动也会导致定时器频繁启动、复位,而无法到达定时时间,导致故障不能正常触发,给排查故障造成困难,甚至,如果故障不能正常触发,还会危害机组安全。At present, the fault detection of wind turbines usually uses pure numerical comparison, for example, when the speed is greater than a certain value, or the voltage of the backup power source is lower than a certain value, a fault is triggered. However, after a component failure of the wind turbine, in addition to the increase or decrease of the value, frequent data fluctuations will occur in many cases and the wind turbine will return to normal after the shutdown. As a result, on the one hand, after the fault is reported, it is difficult for the operation and maintenance personnel to find the cause of the fault. On the other hand, the characteristic of the timer in the controller is that when the condition is turned on, the timer starts counting, and when the condition is turned off, the timer stops working and resets, and the frequent fluctuation of data will also cause the timer to start and reset frequently, However, the timing cannot be reached, causing the failure to be triggered normally, causing difficulties in troubleshooting, and even, if the failure cannot be triggered normally, it will also endanger the safety of the unit.

为了分析风力发电机组故障原因,需要对风力发电机组运行数据进行分析和诊断,这种分析和诊断可在故障发生时刻,自动、准确、及时地记录故障前、后过程的各种电气量的变化情况,通过这些电气量的分析、比较,对分析处理事故、判断保护是否正确动作以及风力发电机组的安全可靠运行起着十分重要的作用。在这种分析和诊断中,判断运行数据是否发生了周期性、正弦性的波动,是监测风力发电机组运行是否稳定、正常的关键要素。例如,如果变桨速度发生了正弦式、频繁的波动,则会导致变桨电机因调桨、换向过于频繁,进而使变桨电机温度升高过快;如果发电机转速发生了正弦式、频繁的波动,则说明发电机控制不稳定、发电机失调,这会影响风力发电机组的载荷和振动。In order to analyze the cause of the failure of the wind turbine, it is necessary to analyze and diagnose the operating data of the wind turbine. This analysis and diagnosis can automatically, accurately and timely record the changes of various electrical quantities in the process before and after the failure at the time of the failure. Through the analysis and comparison of these electrical quantities, it plays a very important role in analyzing and handling accidents, judging whether the protection is operating correctly, and the safe and reliable operation of the wind turbine. In this kind of analysis and diagnosis, judging whether the operation data has periodic and sinusoidal fluctuations is the key element to monitor whether the operation of the wind turbine is stable and normal. For example, if the pitching speed fluctuates sinusoidally and frequently, it will cause the pitch motor to be adjusted too frequently due to pitch adjustment and commutation, which will cause the temperature of the pitch motor to rise too fast; if the generator speed fluctuates sinusoidally, Frequent fluctuations indicate unstable generator control and generator imbalance, which will affect the load and vibration of the wind turbine.

发明内容Contents of the invention

本公开的实施例提供一种风力发电机组运行数据的波动检测方法和波动检测装置,可以直接应用于多种运行数据的检测,不需要设置检测周期、检测幅值等参数,具有广泛的适用性。The embodiments of the present disclosure provide a fluctuation detection method and a fluctuation detection device for wind power generator operating data, which can be directly applied to the detection of various operating data without setting parameters such as detection period and detection amplitude, and have wide applicability .

在一个总的方面,提供一种风力发电机组运行数据的波动检测方法,所述波动检测方法包括:连续地对风力发电机组运行数据进行采样;计算预设检测周期内的风力发电机组运行数据的平均值;基于所述预设检测周期内的风力发电机组运行数据以及所述平均值,确定所述预设检测周期内的风力发电机组运行数据是否出现等间隔分布现象;响应于确定所述预设检测周期内的风力发电机组运行数据出现等间隔分布现象,输出指示所述预设检测周期内的风力发电机组运行数据发生正弦波动的信息。In a general aspect, a method for detecting fluctuations in the operating data of a wind power generating set is provided. The fluctuation detecting method includes: continuously sampling the operating data of a wind generating set; Average value; based on the wind power generating set operating data in the preset detection period and the average value, determine whether the wind power generating set operating data in the preset detection period has an equidistant distribution phenomenon; in response to determining the predetermined Assuming that the operating data of the wind power generating set within the detection period is distributed at equal intervals, output information indicating that the operating data of the wind generating set within the preset detection period has sinusoidal fluctuations.

在另一总的方面,提供一种风力发电机组运行数据的波动检测装置,所述波动检测装置包括:采样单元,被配置为:连续地对风力发电机组运行数据进行采样;计算单元,被配置为:计算预设检测周期内的风力发电机组运行数据的平均值;确定单元,被配置为:基于所述预设检测周期内的风力发电机组运行数据以及所述平均值,确定所述预设检测周期内的风力发电机组运行数据是否出现等间隔分布现象;输出单元,被配置为:响应于确定所述预设检测周期内的风力发电机组运行数据出现等间隔分布现象,输出指示所述预设检测周期内的风力发电机组运行数据发生正弦波动的信息。In another general aspect, a device for detecting fluctuations in the operation data of a wind power generating set is provided. The device for detecting fluctuations includes: a sampling unit configured to: continuously sample the operating data of a wind power generating set; a computing unit configured to is: calculating the average value of the wind power generating set operating data within the preset detection period; the determination unit is configured to: determine the preset Whether the operation data of the wind power generating set within the detection period is distributed at equal intervals; the output unit is configured to: in response to determining that the operation data of the wind power generating set within the preset detection cycle has an equal interval distribution, output an indication of the predetermined It is assumed that the wind turbine operating data within the detection period has sinusoidal fluctuation information.

在另一总的方面,提供一种存储有计算机程序的计算机可读存储介质,当所述计算机程序被处理器执行时,实现如上所述的风力发电机组运行数据的波动检测方法。In another general aspect, a computer-readable storage medium storing a computer program is provided, and when the computer program is executed by a processor, the above-mentioned method for detecting fluctuations in operating data of a wind power generating set is realized.

在另一总的方面,提供一种控制器,所述控制器包括:处理器;和存储器,存储有计算机程序,当所述计算机程序被处理器执行时,实现如上所述的风力发电机组运行数据的波动检测方法。In another general aspect, there is provided a controller comprising: a processor; and a memory storing a computer program, which, when executed by the processor, implements the operation of the wind turbine as described above Data fluctuation detection method.

本公开实施例中的风力发电机组运行数据的波动检测方法和波动检测装置,既能够解决由于数据波动周期较长导致统计值较小而无法检测真实的数据波动的情况,又能够自动滤除短时、偶然的数据跳变和干扰,保证波动检测的可靠性。另一方面,根据本公开实施例中的风力发电机组运行数据的波动检测方法和波动检测装置,检测准确度不受检测周期的影响,也不受数据波动幅值的影响,因此可以直接适用于多种类型数据的波动检测。The fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure can not only solve the situation that the statistical value is small due to the long data fluctuation period and cannot detect the real data fluctuation, but also can automatically filter out short Timely and occasional data jumps and interferences ensure the reliability of fluctuation detection. On the other hand, according to the fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure, the detection accuracy is not affected by the detection period, nor is it affected by the data fluctuation amplitude, so it can be directly applied to Fluctuation detection for many types of data.

此外,本公开实施例中的风力发电机组运行数据的波动检测方法和波动检测装置,对运行数据的检测阈值设置没有任何要求,因此不需要频繁进行针对风力发电机组的参数调整。同时,所述波动检测方法计算简便且效率高,可以直接在PLC控制器中实现,并且可以保证检测的准确度。In addition, the fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure do not require any detection threshold setting of the operating data, so frequent parameter adjustments for the wind generating set are not required. At the same time, the fluctuation detection method is simple and efficient in calculation, can be directly implemented in a PLC controller, and can ensure detection accuracy.

将在接下来的描述中部分阐述本公开总体构思另外的方面和/或优点,还有一部分通过描述将是清楚的,或者可以经过本公开总体构思的实施而得知。Additional aspects and/or advantages of the general inventive concept of the present disclosure will be partially set forth in the following description, and some of them will be clear from the description, or can be learned through implementation of the general inventive concept of the present disclosure.

附图说明Description of drawings

通过下面结合示出实施例的附图进行的描述,本公开的实施例的上述和其他目的和特点将会变得更加清楚,其中:The above and other objects and features of the embodiments of the present disclosure will become more apparent through the following description in conjunction with the accompanying drawings showing the embodiments, wherein:

图1是示出作为波动检测的对象的发电机转速的曲线图;FIG. 1 is a graph showing a rotational speed of a generator as an object of fluctuation detection;

图2是示出作为波动检测的对象的变桨给定速度和变桨实际速度的曲线图;FIG. 2 is a graph showing a pitch given speed and a pitch actual speed as objects of fluctuation detection;

图3是示出现有的数据波动检测方法的示例的示图;3 is a diagram illustrating an example of an existing data fluctuation detection method;

图4是示出根据本公开的实施例的风力发电机组运行数据的波动检测方法的构思的示图;FIG. 4 is a diagram illustrating the concept of a method for detecting fluctuations in operating data of a wind power generating set according to an embodiment of the present disclosure;

图5是示出根据本公开的实施例的风力发电机组运行数据的波动检测方法的流程图;Fig. 5 is a flow chart showing a method for detecting fluctuations in wind power generating set operating data according to an embodiment of the present disclosure;

图6是示出根据本公开的示例性实施例的风力发电机组运行数据的波动检测装置的框图;Fig. 6 is a block diagram showing a fluctuation detection device for wind power generating set operating data according to an exemplary embodiment of the present disclosure;

图7是示出根据本公开的实施例中的控制器的框图。FIG. 7 is a block diagram illustrating a controller in an embodiment according to the present disclosure.

具体实施方式detailed description

提供下面的具体实施方式以帮助读者获得对在此描述的方法、设备和/或系统的全面理解。然而,在理解本申请的公开之后,在此描述的方法、设备和/或系统的各种改变、修改和等同物将是清楚的。例如,在此描述的操作的顺序仅是示例,并且不限于在此阐述的那些顺序,而是除了必须以特定的顺序发生的操作之外,可如在理解本申请的公开之后将是清楚的那样被改变。此外,为了更加清楚和简明,本领域已知的特征的描述可被省略。The following detailed description is provided to assist the reader in gaining an overall understanding of the methods, devices and/or systems described herein. However, various changes, modifications and equivalents of the methods, apparatus and/or systems described herein will be apparent after understanding the disclosure of the present application. For example, the order of operations described herein are examples only, and are not limited to those orders set forth herein, but, except for operations that must occur in a particular order, may occur as will become apparent after understanding the disclosure of this application. That's changed. Also, descriptions of features that are known in the art may be omitted for increased clarity and conciseness.

目前,检测数据发生周期性波动的方法主要有以下几种。At present, there are mainly the following methods for detecting periodic fluctuations in data.

一种方法是,判断数据值单次或多次大于一定值时,就认为数据发生波动。然而,这种方法不能对数据跳变的时序进行判断。例如,数据波动是指数据短时间内发生反复跳变,而只记录跳变,很可能因为短时的干扰而引起误判。另外,对于检测数据值大小,阈值不容易确定。例如,超级电容的电压正常值是85V,电压值大于91V为异常,但数据跳变的最大值可能为90V,这样就会造成漏检。更重要的是,当数据发生周期性正弦性波动时,数据不一定出现跳变,在这种情况下,该方法不能检测数据的周期性正弦性波动。One method is to judge that the data fluctuates when it is judged that the data value is greater than a certain value for one time or multiple times. However, this method cannot judge the timing of data transitions. For example, data fluctuation means that the data jumps repeatedly in a short period of time, and only the jumps are recorded, which may cause misjudgment due to short-term interference. In addition, it is not easy to determine the threshold value for the detection data value. For example, the normal value of the voltage of the supercapacitor is 85V, and the voltage value greater than 91V is abnormal, but the maximum value of the data jump may be 90V, which will cause missed detection. More importantly, when the data has periodic sinusoidal fluctuations, the data does not necessarily appear to jump. In this case, the method cannot detect the periodic sinusoidal fluctuations of the data.

另一种方法是,通过判断数据变化斜率来确定数据是否发生波动。这种方法在一定程度上可以检测数据是正常上升还是波动跳变,但是它不能准确地反映出数据的波动幅度,尤其不能反映出数据的波动周期。例如,数据从85V变化到88V与数据从85V变化到91V,其斜率几乎相等。另一方面,当数据的波动周期较长时,检测时间的不同,又会导致计算偏差。如果检测周期过短,则检测到的只是单次变化的斜率,不能体现出数据的整体变化情况;如果检测周期过长,则有可能跳过峰值,从而导致检测错误。Another method is to determine whether the data fluctuates by judging the slope of the data change. This method can detect whether the data is rising normally or fluctuating to a certain extent, but it cannot accurately reflect the fluctuation range of the data, especially the fluctuation period of the data. For example, the slope of data changing from 85V to 88V is almost the same as that of data changing from 85V to 91V. On the other hand, when the fluctuation period of the data is long, the difference in detection time will lead to calculation deviation. If the detection period is too short, only the slope of a single change is detected, which cannot reflect the overall change of the data; if the detection period is too long, the peak may be skipped, resulting in detection errors.

还有一种方法是方差法,这种方法的检测结果也取决于检测周期,另一方面,这种方法只能检测数据是否偏离了正常值,而无法检测数据的波动趋势。此外,方差无法滤除偶然跳变导致的误检。Another method is the variance method. The detection result of this method also depends on the detection period. On the other hand, this method can only detect whether the data deviates from the normal value, but cannot detect the fluctuation trend of the data. Furthermore, variance cannot filter out false detections caused by occasional transitions.

图1是示出作为波动检测的对象的发电机转速的曲线图。在图1中,横坐标表示时刻值,纵坐标表示转速值。从图1可以看出,发电机转速(也是叶轮转速)发生了正弦形式的波动,这属于风力发电机组运行数据的异常波动现象。此外,在发电机转速的周期性波动过程中,发电机转速总体呈逐渐下降趋势。因此,如果只检测转速值的最大值和最小值,则由于发电机转速会随着风速的变化而变化,因此其参考值不固定,因此采用阈值方法很难有效地检测出波动。FIG. 1 is a graph showing a generator rotation speed as an object of fluctuation detection. In FIG. 1, the abscissa represents the time value, and the ordinate represents the rotational speed value. It can be seen from Figure 1 that the generator speed (also the impeller speed) fluctuates in a sinusoidal form, which belongs to the abnormal fluctuation phenomenon of the wind turbine operating data. In addition, during the periodic fluctuation of the generator speed, the generator speed generally shows a gradual downward trend. Therefore, if only the maximum and minimum values of the rotational speed are detected, the reference value of the generator rotational speed is not fixed because the rotational speed of the generator changes with the change of the wind speed, so it is difficult to effectively detect fluctuations by using the threshold method.

图2是示出作为波动检测的对象的变桨给定速度和变桨实际速度的曲线图。图3是示出现有的数据波动检测方法示例的图示。FIG. 2 is a graph showing a pitch given speed and a pitch actual speed that are objects of fluctuation detection. FIG. 3 is a diagram showing an example of an existing data fluctuation detection method.

在图2和图3中,横坐标表示时刻值,纵坐标表示速度值。从图2中可以看出,变桨给定速度201和变桨实际速度202均发生了类似正弦形式的波动。结合参照图3,如果采用数据变化斜率来进行数据波动检测,则只能检测一定范围内的变化斜率,且检测准确度取决于数据的波动周期。然而,数据的波动周期事先是未知的。如图3所示,在t1~t2区间,数据变化量很小,所以如果检测周期很短,则检测到的曲线变化斜率很小,无法实现数据波动检测;而如果检测周期过长,例如图3所示的t2~t3区间,两个时刻的纵坐标值相当,检测到的曲线变化斜率仍然很小,这意味着t2~t3区间没有发生数据波动,但实际上该区间内数据发生了向上波动变大的情况。因此,采用数据变化斜率来进行数据波动检测无法准确检测到数据波动趋势。In Fig. 2 and Fig. 3, the abscissa represents the time value, and the ordinate represents the speed value. It can be seen from FIG. 2 that both the given pitch speed 201 and the actual pitch speed 202 fluctuate in a sinusoidal form. With reference to FIG. 3 , if the data variation slope is used for data fluctuation detection, only the variation slope within a certain range can be detected, and the detection accuracy depends on the data fluctuation period. However, the fluctuation period of the data is unknown in advance. As shown in Figure 3, in the interval between t1 and t2, the amount of data change is very small, so if the detection period is very short, the slope of the detected curve change is very small, and the detection of data fluctuations cannot be realized; and if the detection period is too long, for example, as shown in Figure 3 In the interval from t2 to t3 shown in 3, the ordinate values at the two moments are equivalent, and the slope of the detected curve change is still very small, which means that there is no data fluctuation in the interval from t2 to t3, but in fact, the data in this interval has an upward trend. A case of increased volatility. Therefore, using the data change slope to detect data fluctuations cannot accurately detect data fluctuation trends.

另一方面,如果采用方差值来进行数据波动检测,则检测准确度也取决于检测周期,检测周期太短,计算的方差值会很小,而检测周期太大,计算的方差值又取决于数据波动的幅值。此外,如果数据发生了短时跳变(例如,t4时刻),则计算的方差值也会很大。因此,采用方差值来进行数据波动检测只能检测数据偏离正常值的程度,而无法检测出数据波动的趋势。On the other hand, if the variance value is used for data fluctuation detection, the detection accuracy also depends on the detection cycle. If the detection cycle is too short, the calculated variance value will be small, and if the detection cycle is too large, the calculated variance value will It also depends on the magnitude of data fluctuations. In addition, if the data has a short-term jump (for example, at time t4), the calculated variance value will also be large. Therefore, using the variance value to detect data fluctuations can only detect the degree to which the data deviates from the normal value, but cannot detect the trend of data fluctuations.

图4是示出根据本公开实施例的风力发电机组运行数据的波动检测方法的构思的示图。Fig. 4 is a diagram illustrating the concept of a method for detecting fluctuations in wind power generating set operating data according to an embodiment of the present disclosure.

参照图4,本公开实施例中的风力发电机组运行数据的波动检测方法按照一定检测周期求取运行数据的平均值,并根据各个采样时刻的运行数据大于平均值或者小于等于平均值进行量化处理,然后确定运行数据的分布曲线以及相应的连续运行数据的个数;如果运行数据的分布曲线呈现周期性变化,且相应的连续运行数据的个数较多,则认为运行数据发生了正弦波动。如图4所示,通过将各个采样时刻的运行数据与平均值相比,生成了高低相间的分布曲线。由于平均值的大小可以随着运行数据的变化而变化,而不受检测周期的影响,因此与现有的波动检测方法相比,这种基于数据分布曲线的波动检测方法更加可靠、准确。Referring to FIG. 4 , the method for detecting fluctuations in the operating data of wind power generators in the embodiments of the present disclosure calculates the average value of the operating data according to a certain detection period, and performs quantification processing according to whether the operating data at each sampling time is greater than the average value or less than or equal to the average value , and then determine the distribution curve of the operating data and the number of corresponding continuous operating data; if the distribution curve of the operating data shows periodic changes, and the number of corresponding continuous operating data is large, it is considered that the operating data has sinusoidal fluctuations. As shown in Figure 4, by comparing the operating data at each sampling time with the average value, a distribution curve with alternating high and low is generated. Because the size of the average value can change with the change of the operating data without being affected by the detection period, this method of fluctuation detection based on the data distribution curve is more reliable and accurate than the existing fluctuation detection methods.

图5示出了根据本公开的实施例的风力发电机组运行数据的波动检测方法的流程图。本公开实施例中的风力发电机组运行数据的波动检测方法可在风力发电机组的主控制器中实现,也可以在风力发电机组中的任何专用控制器中实现。Fig. 5 shows a flow chart of a method for detecting fluctuations in operating data of a wind power generating set according to an embodiment of the present disclosure. The method for detecting fluctuations in the operating data of the wind power generating set in the embodiments of the present disclosure may be implemented in the main controller of the wind generating set, or in any dedicated controller in the wind generating set.

参照图5,在步骤S501中,可连续地对风力发电机组运行数据进行采样。这里,风力发电机组运行数据可以是例如发电机转速、变桨速度、发电机转矩等。可以通过各种检测装置来获取风力发电机组运行数据,本公开对此不做任何限制。Referring to Fig. 5, in step S501, the operation data of the wind power generating set may be continuously sampled. Here, the operating data of the wind power generating set may be, for example, the rotational speed of the generator, the pitch speed, the torque of the generator, and the like. The operation data of the wind power generating set can be acquired through various detection devices, which is not limited in this disclosure.

在步骤S502中,可计算预设检测周期内的风力发电机组运行数据的平均值。这里,预设检测周期可包括多个对风力发电机组运行数据进行采样的采样间隔。例如,采样间隔可以是20ms,预设检测周期可以是500ms。然而,以上数值仅是示例,采样间隔和预设检测周期的时长可以根据需要适当地调整。In step S502, the average value of the wind power generating set operating data within a preset detection period may be calculated. Here, the preset detection period may include a plurality of sampling intervals for sampling the operation data of the wind power generating set. For example, the sampling interval may be 20ms, and the preset detection period may be 500ms. However, the above numerical values are only examples, and the sampling interval and the duration of the preset detection period can be adjusted appropriately as required.

接下来,在步骤S503中,可基于预设检测周期内的风力发电机组运行数据以及计算的平均值,确定预设检测周期内风力发电机组运行数据是否出现等间隔分布现象。Next, in step S503, based on the operating data of the wind generating set within the preset detecting period and the calculated average value, it is determined whether the operating data of the wind generating set within the preset detecting period is distributed at equal intervals.

具体地讲,可将预设检测周期内每个采样时刻的风力发电机组运行数据与计算的平均值进行比较,并且基于比较结果确定预设检测周期内风力发电机组运行数据是否出现等间隔分布现象。在将预设检测周期内每个采样时刻的风力发电机组运行数据与计算的平均值进行比较时,如果该采样时刻的风力发电机组运行数据大于计算的平均值,则将值1存储到存储器中,如果该采样时刻的风力发电机组运行数据不大于计算的平均值,则将值0存储到存储器中。存储器可以是运行波动检测方法的控制器中的存储器,也可以是风力发电机组中设置的其他存储器。根据本公开的实施例,将预设检测周期内的每个采样时刻的风力发电机组运行数据与计算的平均值进行比较的意义在于:平均值可随着运行数据的上升下降趋势而自动调整,从而保证检测准确度。Specifically, the operating data of the wind power generating set at each sampling moment in the preset detection period can be compared with the calculated average value, and based on the comparison result, it can be determined whether there is an equidistant distribution phenomenon in the operating data of the wind power generating set in the preset detection period . When comparing the operating data of the wind power generating set at each sampling moment within the preset detection period with the calculated average value, if the operating data of the wind generating set at the sampling moment is greater than the calculated average value, store the value 1 into the memory , if the running data of the wind power generating set at the sampling moment is not greater than the calculated average value, store the value 0 into the memory. The memory may be a memory in the controller running the fluctuation detection method, or other memory provided in the wind power generating set. According to the embodiment of the present disclosure, the significance of comparing the operating data of the wind power generating set at each sampling moment within the preset detection period with the calculated average value is that the average value can be automatically adjusted along with the rising and falling trends of the operating data, Thereby ensuring the detection accuracy.

在将预设检测周期内的每个采样时刻的风力发电机组运行数据与计算的平均值进行比较之后,可设置计数器进行计数。其后,可基于计数器的计数值确定预设检测周期内的风力发电机组运行数据是否出现等间隔分布现象。这里,计数器可以是运行波动检测方法的控制器中的存储器,也可以是风力发电机组中设置的专用计数器。设置的计数器可按照以下规则进行计数:如果连续存储的值1的数量大于第一阈值,并且随后连续存储的值0的数量大于第一阈值,则将计数器的计数值增1;如果连续存储的值0的数量大于第一阈值,并且随后连续存储的值1的数量大于第一预定阈值,则将计数器的计数值增1。可选择地,如果计数器的计数值大于第二阈值,则可确定预设检测周期内风力发电机组运行数据出现等间隔分布现象。在本公开的实施例中,第一阈值可以为例如不小于5的整数,第二阈值可以为例如不小于2的整数。After comparing the operating data of the wind power generating set at each sampling moment within the preset detection period with the calculated average value, a counter can be set to count. Thereafter, based on the count value of the counter, it may be determined whether the operation data of the wind power generating set within the preset detection period is distributed at equal intervals. Here, the counter can be a memory in the controller running the fluctuation detection method, or it can be a dedicated counter set in the wind power generating set. The set counter can be counted according to the following rules: if the number of continuously stored value 1 is greater than the first threshold, and then the number of continuously stored value 0 is greater than the first threshold, the count value of the counter is increased by 1; if the continuously stored If the number of values 0 is greater than a first threshold and subsequently the number of consecutively stored values 1 is greater than a first predetermined threshold, the count value of the counter is incremented by 1. Optionally, if the count value of the counter is greater than the second threshold, it may be determined that the operating data of the wind power generating set is distributed at equal intervals within the preset detection period. In an embodiment of the present disclosure, the first threshold may be, for example, an integer not less than 5, and the second threshold may be, for example, an integer not less than 2.

此外,计数器还可按照以下规则进行计数:如果连续存储的值1的数量大于第一阈值,并且随后连续存储的值0的数量不大于第一阈值,则将计数器的计数值清零;如果连续存储的值0的数量大于第一阈值,并且随后连续存储的值1的数量不大于第一预定阈值,则将计数器的计数值清零。可选择地,根据上述规则可以确定,在计数器清零的情况下,如果连续存储的值1或值0的数量不大于第一阈值,则计数器的计数值不会增加。In addition, the counter can also count according to the following rules: if the number of continuously stored value 1 is greater than the first threshold, and the number of subsequently continuously stored value 0 is not greater than the first threshold, then the count value of the counter is cleared; If the number of stored values of 0 is greater than the first threshold, and the number of subsequently continuously stored values of 1 is not greater than the first predetermined threshold, the count value of the counter is cleared to zero. Optionally, it may be determined according to the above rule that, in the case of the counter being cleared, if the number of consecutively stored values of 1 or 0 is not greater than the first threshold, the count value of the counter will not increase.

如果确定预设检测周期内的风力发电机组运行数据出现等间隔分布现象,则在步骤S504中,可输出指示预设检测周期内的风力发电机组运行数据发生正弦波动的信息。根据本公开的实施例,可输出指示预设检测周期内的风力发电机组运行数据发生正弦波动的标志以及预设检测周期内的风力发电机组运行数据的最大值和最小值。这里,输出指示预设检测周期内的风力发电机组运行数据发生正弦波动的标志的一种具体实现方式是输出警报,以提示运维人员对运行数据进行分析并关注风力发电机组的运行情况。此外,预设检测周期内风力发电机组运行数据的最大值和最小值可用于风力发电机组的其他控制处理,本公开不再赘述。If it is determined that the operating data of the wind power generating set within the preset detection period is distributed at equal intervals, then in step S504, information indicating that the operating data of the wind power generating set within the preset detection period has sinusoidal fluctuations may be output. According to an embodiment of the present disclosure, a flag indicating sinusoidal fluctuation of the wind power generating set operating data within a preset detection period and the maximum and minimum values of the wind power generating set operating data within the preset detection period may be output. Here, a specific implementation manner of outputting a flag indicating sinusoidal fluctuations in the operating data of the wind power generating set within a preset detection period is to output an alarm to prompt the operation and maintenance personnel to analyze the operating data and pay attention to the operation of the wind generating set. In addition, the maximum and minimum values of the operating data of the wind power generating set within the preset detection period can be used for other control processing of the wind power generating set, which will not be repeated in this disclosure.

另一方面,如果确定预设检测周期内的风力发电机组运行数据出现等间隔分布现象,则根据本公开实施例的风力发电机组运行数据的波动检测方法可返回步骤S501,继续进行运行数据波动检测。On the other hand, if it is determined that the operating data of the wind power generating set within the preset detection period is distributed at equal intervals, the method for detecting fluctuations in the operating data of the wind generating set according to the embodiment of the present disclosure may return to step S501 and continue to detect fluctuations in the operating data .

根据本公开实施例的风力发电机组运行数据的波动检测方法,既能够解决由于数据波动周期较长导致统计值较小而无法检测真实的数据波动情况,又能够自动滤除短时、偶然的数据跳变和干扰,保证波动检测的可靠性。另一方面,根据本公开实施例的风力发电机组运行数据的波动检测方法,检测准确度不受检测周期的影响,也不受数据波动幅值的影响,因此可以直接适用于多种类型数据的波动检测。此外,根据本公开实施例的风力发电机组运行数据的波动检测方法,对运行数据的检测阈值设置没有任何要求,因此不需要频繁进行针对风力发电机组的参数调整。同时,所述波动检测方法计算简便且效率高,可以直接在PLC控制器中实现,并且可以保证检测的准确度。According to the method for detecting fluctuations in the operating data of wind power generating sets in the embodiments of the present disclosure, it can not only solve the problem that the real data fluctuations cannot be detected due to the small statistical value due to the long data fluctuation period, but also automatically filter out short-term and accidental data Jump and interference, to ensure the reliability of fluctuation detection. On the other hand, according to the method for detecting fluctuations in the operating data of wind power generating sets in the embodiments of the present disclosure, the detection accuracy is not affected by the detection period, nor is it affected by the data fluctuation amplitude, so it can be directly applied to the detection of various types of data. Fluctuation detection. In addition, according to the method for detecting fluctuations in the operating data of the wind power generating set according to the embodiments of the present disclosure, there is no requirement for setting the detection threshold of the operating data, so frequent parameter adjustments for the wind generating set are not required. At the same time, the fluctuation detection method is simple and efficient in calculation, can be directly implemented in a PLC controller, and can ensure detection accuracy.

图6是示出根据本公开示例性实施例的风力发电机组运行数据的波动检测装置的框图。风力发电机组运行数据的波动检测装置600可实现在风力发电机组的主控制器中,也可以实现在风力发电机组中的任何专用控制器中。Fig. 6 is a block diagram illustrating an apparatus for detecting fluctuations in operating data of a wind power generating set according to an exemplary embodiment of the present disclosure. The fluctuation detection device 600 for the operation data of the wind power generating set can be implemented in the main controller of the wind generating set, or in any dedicated controller in the wind generating set.

参照图6,风力发电机组运行数据的波动检测装置600可包括采样单元610、计算单元620、确定单元630和输出单元640。Referring to FIG. 6 , an apparatus 600 for detecting fluctuations in operating data of a wind power generating set may include a sampling unit 610 , a calculation unit 620 , a determination unit 630 and an output unit 640 .

采样单元610可连续地对风力发电机组运行数据进行采样。如上所述,风力发电机组运行数据可以是例如发电机转速、变桨速度、发电机转矩等。The sampling unit 610 can continuously sample the operation data of the wind power generating set. As mentioned above, the wind turbine operating data may be, for example, generator speed, pitch speed, generator torque, and the like.

计算单元620可计算预设检测周期内风力发电机组运行数据的平均值。如上所述,预设检测周期可包括多个对风力发电机组运行数据进行采样的采样间隔。The calculation unit 620 can calculate the average value of the wind power generating set operating data within a preset detection period. As mentioned above, the preset detection period may include a plurality of sampling intervals for sampling the operation data of the wind power generating set.

确定单元630可基于预设检测周期内风力发电机组运行数据以及计算的平均值,确定预设检测周期内风力发电机组运行数据是否出现等间隔分布现象。The determining unit 630 may determine whether the wind power generating set operating data in the preset detecting period is distributed at equal intervals based on the operating data of the wind generating set in the preset detecting period and the calculated average value.

具体地讲,确定单元630可将预设检测周期内每个采样时刻的风力发电机组运行数据与计算的平均值进行比较,并且基于比较结果确定预设检测周期内风力发电机组运行数据是否出现等间隔分布现象。这里,针对预设检测周期内的任意一个采样时刻,确定单元630可响应于任意一个采样时刻的风力发电机组运行数据大于平均值,将值1存储到存储器中,并且可响应于任意一个采样时刻的风力发电机组运行数据不大于平均值,将值0存储到存储器中。其后,确定单元630可设置计数器进行计数,并且基于计数器的计数值确定预设检测周期内风力发电机组运行数据是否出现等间隔分布现象。计数器可按照以下规则进行计数:响应于连续存储的值1的数量大于第一阈值,并且随后连续存储的值0的数量大于第一阈值,将计数器的计数值增1;响应于连续存储的值0的数量大于第一阈值,并且随后连续存储的值1的数量大于第一预定阈值,将计数器的计数值增1。可选择地,响应于计数器的计数值大于第二阈值,确定单元630可确定预设检测周期内风力发电机组运行数据出现等间隔分布现象。根据本公开的实施例,第一阈值可以为例如不小于5的整数,第二阈值可以为例如不小于2的整数。Specifically, the determination unit 630 can compare the operating data of the wind power generating set at each sampling moment in the preset detection period with the calculated average value, and determine whether the operating data of the wind power generating set in the preset detection period occurs or not based on the comparison result. Interval distribution phenomenon. Here, for any sampling time within the preset detection period, the determination unit 630 may store the value 1 into the memory in response to the wind power generating set operating data at any sampling time being greater than the average value, and may respond to any sampling time The running data of the wind power generating set is not greater than the average value, and the value 0 is stored in the memory. Afterwards, the determination unit 630 may set a counter to count, and determine whether the operation data of the wind power generating set has an equidistant distribution phenomenon within a preset detection period based on the count value of the counter. The counter may count according to the following rules: in response to the number of consecutively stored values of 1 being greater than a first threshold, and subsequently the number of consecutively stored values of 0 being greater than the first threshold, the count value of the counter is increased by 1; in response to the continuously stored value The number of 0's is greater than the first threshold, and subsequently the number of consecutively stored 1's is greater than the first predetermined threshold, incrementing the count value of the counter by one. Optionally, in response to the count value of the counter being greater than the second threshold, the determining unit 630 may determine that the operating data of the wind power generating set has an equidistant distribution phenomenon within a preset detection period. According to an embodiment of the present disclosure, the first threshold may be, for example, an integer not less than 5, and the second threshold may be, for example, an integer not less than 2.

此外,计数器还可按照以下规则进行计数:响应于连续存储的值1的数量大于第一阈值,并且随后连续存储的值0的数量不大于第一阈值,将计数器的计数值清零;响应于连续存储的值0的数量大于第一阈值,并且随后连续存储的值1的数量不大于第一预定阈值,将计数器的计数值清零。In addition, the counter can also count according to the following rules: in response to the number of continuously stored values of 1 being greater than the first threshold, and subsequently the number of continuously stored values of 0 being not greater than the first threshold, the count value of the counter is cleared; in response to The number of consecutively stored values of 0 is greater than the first threshold, and subsequently the number of consecutively stored values of 1 is not greater than the first predetermined threshold, and the count value of the counter is cleared to zero.

响应于确定预设检测周期内风力发电机组运行数据出现等间隔分布现象,输出单元640可输出指示预设检测周期内风力发电机组运行数据发生正弦波动的信息。例如,输出单元640可输出指示预设检测周期内风力发电机组运行数据发生正弦波动的标志以及预设检测周期内风力发电机组运行数据的最大值和最小值。In response to determining that the operating data of the wind power generating set has an equidistant distribution phenomenon within the preset detection period, the output unit 640 may output information indicating that the operating data of the wind power generating set has sinusoidal fluctuations within the preset detection period. For example, the output unit 640 may output a flag indicating sinusoidal fluctuations in the operating data of the wind power generating set within a preset detection period, and the maximum and minimum values of the operating data of the wind power generating set within the preset detection period.

图7是示出根据本公开的实施例中的控制器的框图。FIG. 7 is a block diagram illustrating a controller in an embodiment according to the present disclosure.

参照图7,本公开实施例中的控制器700可以是风力发电机组的主控制器,也可以是风力发电组中的任何专用控制器。根据本实施例公开的控制器700可包括处理器710和存储器720。处理器710可包括(但不限于)中央处理器(CPU)、数字信号处理器(DSP)、微型计算机、现场可编程门阵列(FPGA)、片上系统(SoC)、微处理器、专用集成电路(ASIC)等。存储器720存储将由处理器710执行的计算机程序。存储器720包括高速随机存取存储器和/或非易失性计算机可读存储介质。当处理器710执行存储器720中存储的计算机程序时,可实现如上所述的风力发电机组运行数据的波动检测方法。Referring to FIG. 7 , the controller 700 in the embodiment of the present disclosure may be the main controller of the wind power generating set, or any dedicated controller in the wind generating set. The controller 700 disclosed according to this embodiment may include a processor 710 and a memory 720 . The processor 710 may include, but is not limited to, a central processing unit (CPU), a digital signal processor (DSP), a microcomputer, a field programmable gate array (FPGA), a system on a chip (SoC), a microprocessor, an application specific integrated circuit (ASIC) and so on. The memory 720 stores computer programs to be executed by the processor 710 . Memory 720 includes high-speed random access memory and/or non-volatile computer-readable storage media. When the processor 710 executes the computer program stored in the memory 720, the method for detecting fluctuations in the operation data of the wind power generating set as described above can be realized.

可选择地,控制器700可以以有线/无线通信方式与风力发电机组中的其他各种组件进行通信,还可以有线/无线通信方式与风电场中的其他装置进行通信。此外,控制器700可以以有线/无线通信方式与风电场外部的装置进行通信。Optionally, the controller 700 may communicate with various other components in the wind power generating set in a wired/wireless communication manner, and may also communicate with other devices in the wind farm in a wired/wireless communication manner. In addition, the controller 700 may communicate with devices outside the wind farm in a wired/wireless communication manner.

本公开实施例中的风力发电机组运行数据的波动检测方法可被编写为计算机程序并被存储在计算机可读存储介质上。当所述计算机程序被处理器执行时,可实现如上所述的运行数据的波动检测方法。计算机可读存储介质的示例包括:只读存储器(ROM)、随机存取可编程只读存储器(PROM)、电可擦除可编程只读存储器(EEPROM)、随机存取存储器(RAM)、动态随机存取存储器(DRAM)、静态随机存取存储器(SRAM)、闪存、非易失性存储器、CD-ROM、CD-R、CD+R、CD-RW、CD+RW、DVD-ROM、DVD-R、DVD+R、DVD-RW、DVD+RW、DVD-RAM、BD-ROM、BD-R、BD-R LTH、BD-RE、蓝光或光盘存储器、硬盘驱动器(HDD)、固态硬盘(SSD)、卡式存储器(诸如,多媒体卡、安全数字(SD)卡或极速数字(XD)卡)、磁带、软盘、磁光数据存储装置、光学数据存储装置、硬盘、固态盘以及任何其他装置,所述任何其他装置被配置为以非暂时性方式存储计算机程序以及任何相关联的数据、数据文件和数据结构并将所述计算机程序以及任何相关联的数据、数据文件和数据结构提供给处理器或计算机使得处理器或计算机能执行所述计算机程序。在一个示例中,计算机程序以及任何相关联的数据、数据文件和数据结构分布在联网的计算机系统上,使得计算机程序以及任何相关联的数据、数据文件和数据结构通过一个或多个处理器或计算机以分布式方式存储、访问和执行。The method for detecting fluctuations in the operating data of a wind power generating set in the embodiments of the present disclosure can be written as a computer program and stored on a computer-readable storage medium. When the computer program is executed by the processor, the method for detecting fluctuations in operating data as described above can be realized. Examples of computer-readable storage media include: Read Only Memory (ROM), Random Access Programmable Read Only Memory (PROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Flash Memory, Nonvolatile Memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD -R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or Disc storage, Hard Disk Drive (HDD), Solid State Drive ( SSD), memory cards (such as Multimedia Cards, Secure Digital (SD) or Extreme Digital (XD) cards), magnetic tapes, floppy disks, magneto-optical data storage devices, optical data storage devices, hard disks, solid-state disks, and any other device , said any other means configured to store in a non-transitory manner a computer program and any associated data, data files and data structures and to provide said computer program and any associated data, data files and data structures to a processing A processor or computer enables a processor or computer to execute the computer program. In one example, the computer program and any associated data, data files and data structures are distributed over a networked computer system such that the computer program and any associated data, data files and data structures are processed by one or more processors or Computers store, access and execute in a distributed fashion.

本公开实施例中的风力发电机组运行数据的波动检测方法和波动检测装置,既能够解决由于数据波动周期较长导致统计值较小而无法检测真实的数据波动的情况,又能够自动滤除短时、偶然的数据跳变和干扰,保证波动检测的可靠性。另一方面,本公开实施例中的风力发电机组运行数据的波动检测方法和波动检测装置,检测准确度不受检测周期的影响,也不受数据波动幅值的影响,因此可以直接适用于多种类型数据的波动检测。The fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure can not only solve the situation that the statistical value is small due to the long data fluctuation period and cannot detect the real data fluctuation, but also can automatically filter out short Timely and occasional data jumps and interferences ensure the reliability of fluctuation detection. On the other hand, the detection accuracy of the fluctuation detection method and fluctuation detection device for the wind power generating set operating data in the embodiment of the present disclosure is not affected by the detection period, nor is it affected by the data fluctuation amplitude, so it can be directly applied to multiple Fluctuation detection for various types of data.

此外,本公开实施例中的风力发电机组运行数据的波动检测方法和波动检测装置,对运行数据的检测阈值设置没有任何要求,因此不需要频繁进行针对风力发电机组的参数调整。同时,所述波动检测方法计算简便且效率高,可以直接在PLC控制器中实现,并且可以保证检测的准确度。In addition, the fluctuation detection method and fluctuation detection device of the wind power generating set operating data in the embodiments of the present disclosure do not require any detection threshold setting of the operating data, so frequent parameter adjustments for the wind generating set are not required. At the same time, the fluctuation detection method is simple and efficient in calculation, can be directly implemented in a PLC controller, and can ensure detection accuracy.

虽然已表示和描述了本公开的一些实施例,但本领域技术人员应该理解,在不脱离由权利要求及其等同物限定其范围的本公开的原理和精神的情况下,可以对这些实施例进行修改。While certain embodiments of the present disclosure have been shown and described, it should be understood by those skilled in the art that modifications may be made to these embodiments without departing from the principles and spirit of the disclosure, the scope of which is defined by the claims and their equivalents. to modify.

Claims (12)

1. A method for detecting fluctuation of operating data of a wind generating set is characterized by comprising the following steps:
continuously sampling the operating data of the wind generating set;
calculating an average value of the running data of the wind generating set in a preset detection period;
determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value;
and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.
2. The method of claim 1, wherein the preset detection period comprises a plurality of sampling intervals at which wind turbine generator set operating data is sampled.
3. The method of claim 1, wherein the step of determining whether the wind generating set operation data in the preset detection period has the equal interval distribution phenomenon comprises the following steps:
and comparing the running data of the wind generating set at each sampling moment in the preset detection period with the average value, and determining whether the running data of the wind generating set in the preset detection period are distributed at equal intervals or not based on the comparison result.
4. The method of claim 3, wherein the step of comparing the wind turbine generator system operating data at each sampling instant within the preset detection period to the average value comprises:
for any sampling moment in the preset detection period, responding to the fact that the running data of the wind generating set at any sampling moment is larger than the average value, and storing a value 1 into a memory;
and responding to the condition that the wind generating set operation data at any one sampling moment is not larger than the average value, and storing a value 0 into a memory.
5. The method of claim 4, wherein the step of determining whether the wind generating set operation data in the preset detection period has the equispaced distribution phenomenon based on the comparison result comprises the steps of:
setting a counter to count, wherein the counter counts according to the following rules:
in response to the number of consecutively stored values 1 being greater than a first threshold and subsequently the number of consecutively stored values 0 being greater than the first threshold, incrementing the count value of the counter by 1;
in response to the number of consecutively stored values 0 being greater than the first threshold and subsequently the number of consecutively stored values 1 being greater than the first predetermined threshold, incrementing the count value of the counter by 1;
and determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the count value of the counter.
6. The method of claim 5, wherein the step of determining whether the wind generating set operation data in the preset detection period has the equispaced distribution phenomenon based on the count value of the counter further comprises the steps of:
and determining that the wind generating set operation data in the preset detection period are distributed at equal intervals in response to the fact that the count value of the counter is larger than a second threshold value.
7. The method of claim 5, wherein the counter further counts according to the following rule:
in response to the number of consecutively stored values 1 being greater than a first threshold and the number of subsequently consecutively stored values 0 being not greater than the first threshold, clearing the count value of the counter;
in response to the number of consecutively stored values 0 being greater than the first threshold and the number of subsequently consecutively stored values 1 being not greater than the first predetermined threshold, clearing the count value of the counter.
8. The method of claim 1, wherein the step of outputting information indicating that the wind generating set operating data within the preset detection period fluctuates sinusoidally comprises:
and outputting a mark indicating that the wind generating set operation data in the preset detection period generate sine fluctuation and the maximum value and the minimum value of the wind generating set operation data in the preset detection period.
9. A fluctuation detection device for wind generating set operation data, characterized in that the device comprises:
a sampling unit configured to: continuously sampling the operating data of the wind generating set;
a computing unit configured to: calculating an average value of the running data of the wind generating set in a preset detection period;
a determination unit configured to: determining whether the wind generating set operation data in the preset detection period are distributed at equal intervals or not based on the wind generating set operation data in the preset detection period and the average value;
an output unit configured to: and responding to the fact that the wind generating set operation data in the preset detection period are distributed at equal intervals, and outputting information indicating that the wind generating set operation data in the preset detection period are subjected to sine fluctuation.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method for fluctuation detection of wind park operational data according to any one of claims 1 to 8.
11. A controller, characterized in that the controller comprises:
a processor; and
a memory storing a computer program which, when executed by the processor, implements the method of fluctuation detection of wind turbine generator set operational data according to any one of claims 1 to 8.
12. A wind park according to claim 11, wherein the wind park comprises a controller.
CN202110702820.5A 2021-06-24 2021-06-24 Fluctuation detection method and fluctuation detection device for wind power generating set operation data Pending CN115523103A (en)

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