CN111287912A - Fault Diagnosis Method for Wind Turbine Pitch System - Google Patents

Fault Diagnosis Method for Wind Turbine Pitch System Download PDF

Info

Publication number
CN111287912A
CN111287912A CN202010100372.7A CN202010100372A CN111287912A CN 111287912 A CN111287912 A CN 111287912A CN 202010100372 A CN202010100372 A CN 202010100372A CN 111287912 A CN111287912 A CN 111287912A
Authority
CN
China
Prior art keywords
pitch
fault
deviation
model
speed
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.)
Granted
Application number
CN202010100372.7A
Other languages
Chinese (zh)
Other versions
CN111287912B (en
Inventor
叶伟文
李茂东
李录平
杨波
刘瑞
王志刚
伍振凌
钟志强
张志达
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Special Equipment Testing And Research Institute Guangzhou Special Equipment Accident Investigation Technology Center Guangzhou Elevator Safety Operation Monitoring Center
Original Assignee
Guangzhou Special Pressure Equipment Inspection and Research Institute
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Guangzhou Special Pressure Equipment Inspection and Research Institute filed Critical Guangzhou Special Pressure Equipment Inspection and Research Institute
Priority to CN202010100372.7A priority Critical patent/CN111287912B/en
Publication of CN111287912A publication Critical patent/CN111287912A/en
Application granted granted Critical
Publication of CN111287912B publication Critical patent/CN111287912B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/80—Diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/84—Modelling or simulation
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Public Health (AREA)
  • Quality & Reliability (AREA)
  • Sustainable Development (AREA)
  • Sustainable Energy (AREA)
  • Chemical & Material Sciences (AREA)
  • Operations Research (AREA)
  • Combustion & Propulsion (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Wind Motors (AREA)

Abstract

本发明提供一种风力发电机变桨系统故障诊断方法，其包括如下步骤：步骤1：获取风力发电机的SCADA系统的历史运行数据；步骤2：利用所述SCADA系统的历史运行数据构建故障特征模型；步骤3：利用所述故障特征模型构建故障诊断模型；步骤4：获取风力发电机的SCADA系统的实时故障告警数据，并输入至故障特征模型，计算获取实时故障特征；步骤5：将步骤4中得到的实时故障特征输入所述故障诊断模型，获取故障诊断结果。本发明的风力发电机变桨系统故障诊断方法，通过利用SCADA测量的风力发电机运行数据提取故障特征并输入故障模型，实现了对风力发电机故障原因与故障位置的实时诊断。

Figure 202010100372

The present invention provides a fault diagnosis method for a pitch system of a wind turbine, comprising the following steps: Step 1: obtaining historical operation data of a SCADA system of a wind turbine; Step 2: constructing a fault feature model by using the historical operation data of the SCADA system ; Step 3: use the fault characteristic model to construct a fault diagnosis model; Step 4: obtain the real-time fault alarm data of the SCADA system of the wind turbine, and input it into the fault characteristic model, and calculate and obtain the real-time fault characteristics; Step 5: use step 4 The real-time fault features obtained in the system are input into the fault diagnosis model to obtain fault diagnosis results. The fault diagnosis method of the wind turbine pitch system of the present invention realizes the real-time diagnosis of the fault cause and the fault location of the wind turbine by extracting the fault features and inputting the fault model by using the wind turbine operation data measured by SCADA.

Figure 202010100372

Description

风力发电机变桨系统故障诊断方法Fault Diagnosis Method for Wind Turbine Pitch System

技术领域technical field

本发明涉及风力发电机故障诊断领域，特别是涉及一种风力发电机变桨系统故障诊断方法。The invention relates to the field of fault diagnosis of wind generators, in particular to a fault diagnosis method of a pitch system of wind generators.

背景技术Background technique

现有技术中，风力发电机是一种理想的能源采集设备，由于风力发电系统安装环境为偏远荒芜的无人区或海上，这导致了风力发电机工作环境十分恶劣，其受力情况复杂多变。请参阅图1和图2，所述风力发电机的主要结构包括：机舱1、塔身2、桨叶3，所述塔身2里在地面上，所述机舱1安装在所述塔身2上并于所述塔身垂直，所述桨叶3包括地至第三桨叶，安装于所述机舱1上。工作时，所述桨叶3受外界风力推动转动，将风能转化为桨叶的动能，并将动能传递入风力发电机内完成风能采集。其中，所述机舱1的舱体内设置有变桨系统4和轮毂5，所述轮毂5安装于所述机舱1的舱体前端内侧，同时所述轮毂5与所述桨叶3通过变桨轴承连接，所述变桨系统4设置于所述轮毂5内，且所述变桨系统4包括第一至第三变桨系统，其分别通过一变桨轴承与第一至第三桨叶连接，其中，每个变桨系统均包括变桨机械部件、变桨电源系统和变桨控制系统，所述变桨控制系统通过驱动所述变桨电源系统进而调节所述变桨机械部件，实现调节相应的桨叶的角度。所述变桨机械部件主要包括齿轮箱6与变桨轴承，所述齿轮箱受所述变桨电源系统驱动，且其通过主轴(即低速轴)与所述变桨轴承连接，能够通过所述变桨轴承调节相应的桨叶的角度。In the prior art, a wind turbine is an ideal energy collection device. Since the installation environment of the wind power system is a remote and barren uninhabited area or the sea, the working environment of the wind turbine is very harsh, and its stress situation is complicated. Change. Please refer to FIG. 1 and FIG. 2. The main structure of the wind turbine includes: a nacelle 1, a tower body 2, and blades 3. The tower body 2 is on the ground, and the nacelle 1 is installed on the tower body 2. Above and perpendicular to the tower body, the paddles 3 include ground to third paddles, and are installed on the nacelle 1 . During operation, the blade 3 is driven and rotated by the external wind, converts the wind energy into the kinetic energy of the blade, and transfers the kinetic energy into the wind generator to complete the wind energy collection. Wherein, the cabin of the nacelle 1 is provided with a pitch system 4 and a hub 5, the hub 5 is installed on the inner side of the front end of the cabin of the nacelle 1, and the hub 5 and the blade 3 pass through the pitch bearing The pitch system 4 is arranged in the hub 5, and the pitch system 4 includes first to third pitch systems, which are respectively connected with the first to third blades through a pitch bearing, Wherein, each pitch system includes a pitch mechanical part, a pitch power supply system and a pitch control system, and the pitch control system adjusts the pitch mechanical part by driving the pitch power system, so as to adjust the corresponding pitch control system. angle of the paddle. The pitch mechanical components mainly include a gear box 6 and a pitch bearing. The gear box is driven by the pitch power supply system, and is connected to the pitch bearing through the main shaft (ie, the low-speed shaft), and can pass through the pitch bearing. The pitch bearing adjusts the angle of the corresponding blade.

实际运行中，由于风速和风向的随机性和不确定性，所述变桨系统随着风速的频繁变化而进行变桨动作时容易损坏。同时，风机其余部件的故障问题往往也需要变桨系统进行刹车制动来保护机组安全，这也会引起变桨系统故障实际运行中变桨系统的故障率始终居高不下，易造成巨大的安全事故，影响风力发电厂的安全生产和经济效益。因此，对变桨系统进行实时监测与故障预警十分必要。In actual operation, due to the randomness and uncertainty of the wind speed and wind direction, the pitch system is easily damaged when the pitch action is performed with the frequent changes of the wind speed. At the same time, the failure of other components of the wind turbine often requires the pitch system to brake to protect the safety of the unit, which will also cause the pitch system to fail. Accidents affect the safe production and economic benefits of wind power plants. Therefore, it is necessary to carry out real-time monitoring and fault early warning for the pitch system.

现有技术采用SCADA(Supervisory Control and Data Acquisition)系统来对风力发电设备进行监控和控制，并可以采集多种风机运行数据、调节风力发电系统运行参数和实现故障报警。具体地，所述SCADA系统可测量机舱外平均风速、风向信号、桨距角(桨距角测量数据包括三个旋转编码器，也称作A编码器，和三个冗余编码器，也称作B编码器，测量的桨距角，所述A编码器分别设置于风力发电机的三个变桨电机的顶端，用于根据电机转动信息测量各桨叶的桨距角，所述B编码器分别设置于轮毂上，并分别通过一齿轮与所述变桨轴承啮合，其能够根据低速轴的转动信息测量各桨叶的桨距角)、低速轴转速、发电机有功功率、变桨电机的电压和电流、变桨电机温度、桨叶变流器的温度、变桨电池的电压、变桨轴承油脂泵出口油压和变桨齿轮油脂泵出口油压。然而，虽然所述SCADA系统能够获取上述各种采集信息，但该系统提供的故障报警通常包含大量混杂的信息，且不能让使用者明确真正的故障原因并定位故障位置，导致不能及时准确地进行故障检修。In the prior art, a SCADA (Supervisory Control and Data Acquisition) system is used to monitor and control the wind power generation equipment, and can collect various fan operation data, adjust the operation parameters of the wind power generation system and realize fault alarms. Specifically, the SCADA system can measure the average wind speed outside the cabin, the wind direction signal, the pitch angle (the pitch angle measurement data includes three rotary encoders, also called A encoders, and three redundant encoders, also called Make B encoder, the pitch angle of measurement, described A encoder is respectively arranged on the top of three pitch motors of wind turbine, for measuring the pitch angle of each blade according to motor rotation information, described B encoder The gears are respectively arranged on the hub and meshed with the pitch bearing through a gear, which can measure the pitch angle of each blade according to the rotation information of the low-speed shaft), low-speed shaft rotational speed, generator active power, pitch motor voltage and current, pitch motor temperature, blade converter temperature, pitch battery voltage, pitch bearing grease pump outlet oil pressure and pitch gear grease pump outlet oil pressure. However, although the SCADA system can obtain the above-mentioned various collection information, the fault alarm provided by the system usually contains a large amount of mixed information, and cannot allow the user to identify the real cause of the fault and locate the fault location, resulting in inability to timely and accurately carry out Troubleshooting.

发明内容SUMMARY OF THE INVENTION

基于此，本发明的目的在于，提供一种风力发电机变桨系统故障诊断方法，其能够诊断风机故障的原因并定位故障位置。Based on this, the purpose of the present invention is to provide a fault diagnosis method for a pitch system of a wind turbine, which can diagnose the cause of a fan fault and locate the fault location.

一种风力发电机变桨系统故障诊断方法，其包括如下步骤：步骤1：获取风力发电机的SCADA系统的历史运行数据；步骤2：利用所述SCADA系统的历史运行数据构建故障特征模型；步骤3：利用所述故障特征模型构建故障诊断模型；步骤4：获取风力发电机的SCADA系统的实时故障告警数据，并输入至故障特征模型，计算获取实时故障特征；步骤5：将步骤4中得到的实时故障特征输入所述故障诊断模型，获取故障诊断结果。A fault diagnosis method for a wind turbine pitch system, comprising the following steps: Step 1: obtaining historical operation data of a SCADA system of a wind turbine; Step 2: building a fault feature model by using the historical operation data of the SCADA system; Step 3 : use the fault characteristic model to construct a fault diagnosis model; Step 4: obtain the real-time fault alarm data of the SCADA system of the wind turbine, and input it into the fault characteristic model, and calculate and obtain the real-time fault characteristics; Real-time fault features are input into the fault diagnosis model to obtain fault diagnosis results.

本发明所述的风力发电机变桨系统故障诊断方法，通过利用SCADA系统测量的风力发电机运行数据提取故障特征并输入故障模型完成故障诊断，实现了对风力发电机故障原因与故障位置的实时诊断。The fault diagnosis method of the wind turbine pitch system of the present invention extracts the fault features by using the wind turbine operation data measured by the SCADA system and inputs the fault model to complete the fault diagnosis, and realizes the real-time diagnosis of the fault cause and the fault location of the wind turbine. .

进一步地，步骤1中，所述SCADA系统的历史运行数据包括：风力发电机机舱外平均风速、风向、风力发电机第一桨叶至第三桨叶的桨距角、低速轴转速、发电机有功功率、变桨电机的电压和电流、变桨电机温度、桨叶变流器温度、变桨电池电压、变桨轴承油脂泵出口油压和变桨齿轮油脂泵出口油压中的一种或多种。Further, in step 1, the historical operation data of the SCADA system includes: the average wind speed outside the wind turbine cabin, the wind direction, the pitch angle of the first blade to the third blade of the wind turbine, the low-speed shaft rotational speed, the generator Active power, pitch motor voltage and current, pitch motor temperature, blade converter temperature, pitch battery voltage, pitch bearing grease pump outlet oil pressure and pitch gear grease pump outlet oil pressure or variety.

进一步地，所述提取故障特征的方法包括：Further, the method for extracting fault features includes:

所述构建故障特征模型的方法包括：The method for constructing a fault feature model includes:

1)构建桨距角理论偏差故障特征模型：1) Construct the fault characteristic model of the theoretical deviation of pitch angle:

所述风力发电机桨叶的桨距角数据包括A编码器测量得到的桨距角数据

Figure BDA0002386678470000021
和B编码器测量得到的编码器数据
Figure BDA0002386678470000022
The pitch angle data of the wind turbine blade includes the pitch angle data measured by the A encoder
Figure BDA0002386678470000021
and the encoder data measured by the B encoder
Figure BDA0002386678470000022

获取风机历史运行中的一组平均风速数据，将其中每个平均风速数据

Figure BDA0002386678470000023
分别带入下方公式组，由此求解得到一组对应的桨距角β：Obtain a set of average wind speed data in the historical operation of the fan, and convert each of the average wind speed data
Figure BDA0002386678470000023
Bring them into the following formulas respectively, and solve them to obtain a set of corresponding pitch angles β:

Figure BDA0002386678470000024
Figure BDA0002386678470000024

Figure BDA0002386678470000025
其中，ρ为空气密度，η为传动效率，R为风轮直径，P为风力发电机的额定功率，c1-c8为风轮特性系数，λi为第i片桨叶的叶尖速，λ为风轮尖速比；
Figure BDA0002386678470000025
Among them, ρ is the air density, η is the transmission efficiency, R is the diameter of the rotor, P is the rated power of the wind turbine, c 1 -c 8 is the characteristic coefficient of the rotor, and λ i is the tip speed of the ith blade , λ is the rotor tip speed ratio;

以该组平均风速

Figure BDA0002386678470000031
为自变量，并以计算得到的该组桨距角β为因变量，通过最小二乘法多项式进行拟合，获得桨距角理论值βth的计算公式：with the average wind speed of the group
Figure BDA0002386678470000031
is the independent variable, and the calculated set of pitch angles β is used as the dependent variable, and is fitted by the least squares polynomial to obtain the calculation formula of the theoretical value of the pitch angle β th :

Figure BDA0002386678470000032
Figure BDA0002386678470000032

其中，

Figure BDA0002386678470000033
为风力发电机的额定风速；in,
Figure BDA0002386678470000033
is the rated wind speed of the wind turbine;

利用桨距角理论值βth构建桨距角理论偏差值

Figure BDA0002386678470000034
的计算模型，以该模型作为所述桨距角理论偏差故障特征模型：
Figure BDA0002386678470000035
其中，Δβ为桨距角绝对偏差限值，
Figure BDA0002386678470000036
是第i个A编码器测量得到的桨距角数据；Using the theoretical value of the pitch angle β th to construct the theoretical deviation value of the pitch angle
Figure BDA0002386678470000034
The calculation model of , and this model is used as the fault characteristic model of the theoretical deviation of the pitch angle:
Figure BDA0002386678470000035
Among them, Δβ is the absolute deviation limit of pitch angle,
Figure BDA0002386678470000036
is the pitch angle data measured by the i-th A encoder;

同理，

Figure BDA0002386678470000037
其中，Δβ为桨距角绝对偏差限值，
Figure BDA0002386678470000038
是第i个B编码器测量得到的桨距角数据；Similarly,
Figure BDA0002386678470000037
Among them, Δβ is the absolute deviation limit of pitch angle,
Figure BDA0002386678470000038
is the pitch angle data measured by the i-th B encoder;

和/或and / or

2)构建低速轴转速故障特征模型：2) Build a low-speed shaft rotational speed fault feature model:

所述低速轴转速故障特征模型包括风轮转速相对偏差计算模型和风轮转速相对波动计算模型；The low-speed shaft rotational speed fault characteristic model includes a calculation model for the relative deviation of the rotational speed of the wind rotor and a calculation model for the relative fluctuation of the rotational speed of the wind rotor;

构建所述风轮转速相对偏差计算模型：Build the calculation model of the relative deviation of the rotor speed:

Figure BDA0002386678470000039
Figure BDA0002386678470000039

其中，δnls为风轮转速相对偏差，nls为所述低速轴转速数据，

Figure BDA00023866784700000310
为低速轴转速理论值，且满足如下公式：Among them, δn ls is the relative deviation of the rotor speed, n ls is the low-speed shaft speed data,
Figure BDA00023866784700000310
is the theoretical value of the low-speed shaft speed and satisfies the following formula:

Figure BDA00023866784700000311
Figure BDA00023866784700000311

其中，n0为低速轴的额定转速，

Figure BDA00023866784700000312
为风力发电机的切入风速，
Figure BDA00023866784700000313
为风力发电机的切出风速；Among them, n 0 is the rated speed of the low-speed shaft,
Figure BDA00023866784700000312
is the cut-in wind speed of the wind turbine,
Figure BDA00023866784700000313
is the cut-out wind speed of the wind turbine;

构建风轮转速相对波动计算模型：Build a calculation model for the relative fluctuation of the rotor speed:

Figure BDA00023866784700000314
其中，δΔnms为风轮转速相对波动值，
Figure BDA00023866784700000315
为数秒内所述低速轴转速数据中的最大值，
Figure BDA0002386678470000041
为数秒内所述低速轴转速数据中的最小值；优选地，
Figure BDA0002386678470000042
为60秒内所述低速轴转速数据中的最大值，
Figure BDA0002386678470000043
为60内所述低速轴转速数据中的最小值；和/或
Figure BDA00023866784700000314
Among them, δΔn ms is the relative fluctuation value of the rotor speed,
Figure BDA00023866784700000315
is the maximum value of the low-speed shaft speed data in seconds,
Figure BDA0002386678470000041
is the minimum value in the low-speed shaft rotational speed data in several seconds; preferably,
Figure BDA0002386678470000042
is the maximum value of the low-speed shaft rotational speed data within 60 seconds,
Figure BDA0002386678470000043
is the minimum value of said low-speed shaft speed data within 60; and/or

3)构建桨距角偏差故障特征模型：3) Build the pitch angle deviation fault characteristic model:

所述风力发电机桨叶的桨距角数据还包括三个B编码器测得的桨距角数据

Figure BDA0002386678470000044
所述桨距角偏差故障特征模型包括桨距角相对偏差计算模型和桨距角绝对偏差计算模型；The pitch angle data of the wind turbine blade also includes the pitch angle data measured by the three B encoders
Figure BDA0002386678470000044
The pitch angle deviation fault characteristic model includes a pitch angle relative deviation calculation model and a pitch angle absolute deviation calculation model;

构建风力发电机第一桨叶至第三桨叶的桨距角相对偏差计算模型：Build a calculation model for the relative deviation of the pitch angle from the first blade to the third blade of the wind turbine:

Figure BDA0002386678470000045
Figure BDA0002386678470000045

Figure BDA0002386678470000046
Figure BDA0002386678470000046

其中，

Figure BDA0002386678470000047
为三个A编码器测量数据的平均桨距角，
Figure BDA0002386678470000048
为三个B编码器测量数据的平均桨距角，
Figure BDA0002386678470000049
为由A编码器测量数据得到的桨距角相对偏差，
Figure BDA00023866784700000410
为由B编码器测量数据得到的桨距角相对偏差；in,
Figure BDA0002386678470000047
The average pitch angle of the measured data for the three A encoders,
Figure BDA0002386678470000048
The average pitch angle of the measured data for the three B encoders,
Figure BDA0002386678470000049
is the relative deviation of the pitch angle obtained from the measurement data of the A encoder,
Figure BDA00023866784700000410
is the relative deviation of the pitch angle obtained from the measurement data of the B encoder;

构建风力发电机第一桨叶至第三桨叶的桨距角绝对偏差计算模型：Build a calculation model for the absolute deviation of the pitch angle from the first blade to the third blade of the wind turbine:

Figure BDA00023866784700000411
Figure BDA00023866784700000411

和/或and / or

4)构建变桨电池组故障特征模型：4) Build the fault feature model of the pitch battery pack:

Figure BDA0002386678470000051
Figure BDA0002386678470000051

其中，Vbp0为第一至第三变桨电池组的正常电压，Among them, V bp0 is the normal voltage of the first to third pitch battery packs,

Vbp1-Vbp3为所述变桨电池电压数中包含的第一至第三变桨电池的电压数据，δVbp1-δVbp3为第一至第三变桨电池组的电压相对偏差；和/或V bp1 -V bp3 are the voltage data of the first to third pitch batteries included in the pitch battery voltage number, and δV bp1 - δV bp3 are the voltage relative deviations of the first to third pitch battery packs; and/ or

5)构建变桨电机的变流器温度故障特征模型δtf cbi：5) Construct the converter temperature fault characteristic model δt f cbi of the pitch motor:

Figure BDA0002386678470000052
Figure BDA0002386678470000052

其中，tf cb0为所述变流器的温度上限值，tf cbi为所述变流器温度数据，δtf cbi为第i个变桨电机的变流器温度偏差；和/或Wherein, t f cb0 is the upper temperature limit of the converter, t f cbi is the temperature data of the converter, and δt f cbi is the converter temperature deviation of the ith pitch motor; and/or

6)构建变桨电机温度偏差故障特征模型：6) Construct the temperature deviation fault characteristic model of the pitch motor:

所述变桨电机温度数据包括第一至第三变桨电机的温度数据tm1-tm3；The pitch motor temperature data includes temperature data t m1 -t m3 of the first to third pitch motors;

所述变桨电机温度偏差故障特征模型为：The temperature deviation fault characteristic model of the pitch motor is:

Figure BDA0002386678470000053
Figure BDA0002386678470000053

其中，tm0为变桨电机的温度偏差限值，δtm1-δtm3为第一至第三变桨电机的温度偏差；和/或where t m0 is the temperature deviation limit of the pitch motor, and δt m1 - δt m3 are the temperature deviations of the first to third pitch motors; and/or

7)构建变桨电机电流故障特征模型：7) Build the current fault characteristic model of the pitch motor:

所述变桨电机电流数据包括第一至第三变桨电机的电流数据Im1-Im3；The pitch motor current data includes current data I m1 -I m3 of the first to third pitch motors;

所述变桨电机电流故障特征模型为：The current fault characteristic model of the pitch motor is:

Figure BDA0002386678470000061
Figure BDA0002386678470000061

其中，Im0为变桨电机的电流偏差限值，δIm1-δIm3为第一至第三变桨电机的电流偏差；和/或Wherein, I m0 is the current deviation limit of the pitch motor, and δI m1 - δI m3 are the current deviations of the first to third pitch motors; and/or

8)构建变桨电机转速特征模型：8) Construct the rotational speed characteristic model of the pitch motor:

所述变桨电机转速数据包括第一至第三变桨电机的转速数据nm1-nm3；The rotational speed data of the pitch motors includes rotational speed data n m1 -n m3 of the first to third pitch motors;

所述变桨电机转速特征模型为：The rotational speed characteristic model of the pitch motor is:

Figure BDA0002386678470000062
Figure BDA0002386678470000062

其中，nm0为变桨电机的平均转速，且满足

Figure BDA0002386678470000063
δnm1-δnm3为第一至第三变桨电机的转速偏差；Among them, n m0 is the average speed of the pitch motor, and it satisfies
Figure BDA0002386678470000063
δn m1 -δn m3 are the rotational speed deviations of the first to third pitch motors;

和/或and / or

9)构建变桨轴承油脂泵故障特征模型：9) Build the fault characteristic model of the pitch bearing grease pump:

Figure BDA0002386678470000064
Figure BDA0002386678470000064

其中，

Figure BDA0002386678470000065
分别为第i个变桨轴承油脂泵出口压力上限值和下限值，pbgpi为第i个变桨轴承油脂泵出口压力数据，
Figure BDA0002386678470000066
和
Figure BDA0002386678470000067
为第i个变桨轴油脂泵压力上限偏差与压力下限偏差；in,
Figure BDA0002386678470000065
are the upper limit and lower limit of the outlet pressure of the ith pitch bearing grease pump, respectively, p bgpi is the outlet pressure data of the ith pitch bearing grease pump,
Figure BDA0002386678470000066
and
Figure BDA0002386678470000067
is the pressure upper limit deviation and the pressure lower limit deviation of the ith pitch shaft grease pump;

和/或and / or

10)构建变桨齿轮油脂泵出口压力故障特征模型：10) Construct the characteristic model of the outlet pressure fault of the pitch gear grease pump:

Figure BDA0002386678470000071
Figure BDA0002386678470000071

其中，

Figure BDA0002386678470000072
分别为第i个变桨齿轮油脂泵出口压力上限值和下限值，pggpi为第i个变桨齿轮油脂泵出口压力数据，
Figure BDA0002386678470000073
和
Figure BDA0002386678470000074
第i个变桨齿轮油脂泵压力上限偏差与压力下限偏差。in,
Figure BDA0002386678470000072
are the upper limit and lower limit of the outlet pressure of the ith pitch gear grease pump respectively, p ggpi is the outlet pressure data of the ith pitch gear grease pump,
Figure BDA0002386678470000073
and
Figure BDA0002386678470000074
The deviation of the pressure upper limit and the pressure lower limit of the i-th pitch gear grease pump.

进一步地，步骤3中，所述构建故障诊断模型的方法包括构建变桨电源系统故障诊断模型：Further, in step 3, the method for constructing a fault diagnosis model includes constructing a fault diagnosis model for a pitch power supply system:

1)构建变桨齿轮箱故障诊断模型：1) Build the fault diagnosis model of the pitch gearbox:

当所述第i个变桨电机的电流偏差和第i个变桨电机的温度偏差满足：When the current deviation of the i-th pitch motor and the temperature deviation of the i-th pitch motor satisfy:

Figure BDA0002386678470000075
可判断第i个变桨齿轮箱故障；
Figure BDA0002386678470000075
The fault of the i-th pitch gearbox can be judged;

若同时第i个变桨齿轮油脂泵上限偏差与下限偏差还满足：

Figure BDA0002386678470000076
或
Figure BDA0002386678470000077
At the same time, if the upper limit deviation and lower limit deviation of the i-th pitch gear grease pump also satisfy:
Figure BDA0002386678470000076
or
Figure BDA0002386678470000077

Figure BDA0002386678470000078
可进一步判断出：第i个变桨齿轮箱故障；和/或
Figure BDA0002386678470000078
It can be further determined that: the i-th pitch gearbox is faulty; and/or

2)构建变桨轴承故障诊断模型：2) Build the pitch bearing fault diagnosis model:

当下式成立：The present formula is established:

第一至第三变桨电机的电流偏差满足

Figure BDA0002386678470000079
The current deviation of the first to third pitch motors satisfies
Figure BDA0002386678470000079

且所述第一至第三变桨电机的温度偏差满足

Figure BDA00023866784700000710
And the temperature deviation of the first to third pitch motors satisfies
Figure BDA00023866784700000710

且所第一至第三个变桨轴油脂泵上限偏差与下限偏差满足条件：

Figure BDA00023866784700000711
或
Figure BDA00023866784700000712
即可判断变桨轴承出现故障。And the upper limit deviation and lower limit deviation of the first to third pitch shaft grease pumps meet the conditions:
Figure BDA00023866784700000711
or
Figure BDA00023866784700000712
It can be judged that the pitch bearing is faulty.

进一步地，步骤3中，所述构建故障诊断模型的方法还包括构建变桨系统机械部件故障诊断模型：Further, in step 3, the method for constructing a fault diagnosis model further includes constructing a fault diagnosis model for the mechanical components of the pitch system:

1)构建变桨齿轮箱故障诊断模型：1) Build the fault diagnosis model of the pitch gearbox:

当所述第i个变桨电机的电流偏差和第i个变桨电机的温度偏差满足：When the current deviation of the i-th pitch motor and the temperature deviation of the i-th pitch motor satisfy:

Figure BDA00023866784700000713
可判断第i个变桨齿轮箱故障；
Figure BDA00023866784700000713
The fault of the i-th pitch gearbox can be judged;

若同时第i个变桨齿轮油脂泵上限偏差与下限偏差还满足：

Figure BDA0002386678470000081
或
Figure BDA0002386678470000082
At the same time, if the upper limit deviation and lower limit deviation of the i-th pitch gear grease pump also satisfy:
Figure BDA0002386678470000081
or
Figure BDA0002386678470000082

Figure BDA0002386678470000083
可进一步判断出：第i个变桨齿轮箱故障；和/或
Figure BDA0002386678470000083
It can be further determined that: the i-th pitch gearbox is faulty; and/or

2)构建变桨轴承故障诊断模型：2) Build the pitch bearing fault diagnosis model:

当下式成立：The present formula is established:

第一至第三变桨电机的电流偏差满足

Figure BDA0002386678470000084
The current deviation of the first to third pitch motors satisfies
Figure BDA0002386678470000084

且所述第一至第三变桨电机的温度偏差满足

Figure BDA0002386678470000085
And the temperature deviation of the first to third pitch motors satisfies
Figure BDA0002386678470000085

且所第一至第三个变桨轴油脂泵上限偏差与下限偏差满足条件：

Figure BDA0002386678470000086
或
Figure BDA0002386678470000087
即可判断变桨轴承出现故障。And the upper limit deviation and lower limit deviation of the first to third pitch shaft grease pumps meet the conditions:
Figure BDA0002386678470000086
or
Figure BDA0002386678470000087
It can be judged that the pitch bearing is faulty.

进一步地，步骤3中，所述构建故障诊断模型的方法还包括构建变桨系统的控制系统故障诊断模型：Further, in step 3, the method for constructing a fault diagnosis model further includes constructing a control system fault diagnosis model of the pitch system:

1)构建变桨角度差异故障诊断模型：1) Build the pitch angle difference fault diagnosis model:

当桨距角相对偏差和变桨角度偏差Δβbi满足下式：When the relative pitch angle deviation and pitch angle deviation Δβ bi satisfy the following formula:

Figure BDA0002386678470000088
Figure BDA0002386678470000088

且

Figure BDA0002386678470000089
则判断第i个桨叶的B编码器故障；and
Figure BDA0002386678470000089
Then it is judged that the B encoder of the i-th blade is faulty;

当桨距角相对偏差和桨距角绝对偏差Δβbi满足下式：When the relative pitch angle deviation and the pitch angle absolute deviation Δβ bi satisfy the following formula:

Figure BDA00023866784700000810
Figure BDA00023866784700000810

且

Figure BDA00023866784700000811
则判断第i个桨叶的A编码器故障，其中，a为变桨角度许用误差；and
Figure BDA00023866784700000811
Then it is judged that the encoder A of the i-th blade is faulty, where a is the allowable error of the pitch angle;

和/或and / or

2)构建桨叶限位开关触发偏早故障诊断模型：2) Build a fault diagnosis model for the early failure of the blade limit switch trigger:

如果桨距角相对偏差和桨叶角度满足下式：If the relative deviation of the pitch angle and the blade angle satisfy the following formula:

Figure BDA00023866784700000812
Figure BDA00023866784700000812

Figure BDA00023866784700000813
即可诊断故障原因为桨叶限位开关触发偏早；和/或
Figure BDA00023866784700000813
The cause of the fault can be diagnosed as premature activation of the blade limit switch; and/or

3)构建变桨电机高温故障诊断模型：3) Build the high temperature fault diagnosis model of the pitch motor:

如果：变桨电机温度偏差δtmi≥10.0％，可判断出第i桨叶出现了变桨电机高温故障；If: the pitch motor temperature deviation δt mi ≥ 10.0%, it can be judged that the pitch motor high temperature fault occurs on the i-th blade;

如果变桨电机温度偏差与变桨电机电流偏差满足：

Figure BDA0002386678470000091
可判断出第i桨叶出现了变桨电机高温故障，且该故障多由变桨齿轮箱故障引起；和/或If the pitch motor temperature deviation and the pitch motor current deviation satisfy:
Figure BDA0002386678470000091
It can be determined that the pitch motor high temperature fault has occurred on the i-th blade, and the fault is mostly caused by the fault of the pitch gearbox; and/or

4)构建变桨电机转速过高故障诊断模型：4) Build a fault diagnosis model for the high speed of the pitch motor:

如果以下两式成立：If the following two equations hold:

所述变桨电机的转速偏差满足

Figure BDA0002386678470000092
The speed deviation of the pitch motor satisfies the
Figure BDA0002386678470000092

且变桨电机转速满足

Figure BDA0002386678470000093
And the speed of the pitch motor satisfies
Figure BDA0002386678470000093

可判断出现了变桨电机转速过高故障，且此故障多由A编码器故障引起,其中,a为风力发电机许用转速偏差；和/或It can be judged that there is a fault that the speed of the pitch motor is too high, and this fault is mostly caused by the fault of the A encoder, where a is the allowable speed deviation of the wind turbine; and/or

5)构建变桨失效故障诊断模型：5) Build a fault diagnosis model for pitch failure:

当下式成立：The present formula is established:

A编码器测得的桨距角相对偏差满足

Figure BDA0002386678470000094
The relative deviation of the pitch angle measured by the A encoder satisfies the
Figure BDA0002386678470000094

且B编码器测得的桨距角相对偏差满足

Figure BDA0002386678470000095
And the relative deviation of pitch angle measured by B encoder satisfies
Figure BDA0002386678470000095

且第一至第三桨叶的桨距角理论偏差满足

Figure BDA0002386678470000096
And the theoretical deviation of the pitch angle of the first to third blades satisfies
Figure BDA0002386678470000096

则可判断出现了变桨失效故障；和/或then it can be judged that a pitch failure has occurred; and/or

6)构建变桨系统信号传输故障诊断模型：6) Build a fault diagnosis model for the signal transmission of the pitch system:

当下式成立：The present formula is established:

低速轴转速相对偏差满足δnls≥10.0％且变桨电机的转速偏差满足δΔnms≥10.0％，则可判断出现了变桨控制信号传输故障。If the relative deviation of the low-speed shaft speed satisfies δn ls ≥ 10.0% and the speed deviation of the pitch motor satisfies δΔn ms ≥ 10.0%, it can be judged that there is a pitch control signal transmission failure.

进一步地，步骤5中，当所述实时故障特征输入所述故障诊断模型时，首先将所述实时故障特征输入所述变桨电源系统故障诊断模型和所述变桨系统机械部件故障诊断模型，判断该实时故障特征的故障类型；若判断该实时故障特征不属于所述两类故障模型，再将所述实时故障特征输入所述变桨系统的控制系统故障诊断模型进行诊断。Further, in step 5, when the real-time fault characteristics are input into the fault diagnosis model, firstly, the real-time fault characteristics are input into the pitch power system fault diagnosis model and the pitch system mechanical component fault diagnosis model, The fault type of the real-time fault feature is judged; if it is judged that the real-time fault feature does not belong to the two types of fault models, the real-time fault feature is input into the control system fault diagnosis model of the pitch system for diagnosis.

进一步地，步骤5中，所述故障诊断结果包括故障原因与故障位置。Further, in step 5, the fault diagnosis result includes a fault cause and a fault location.

进一步地，步骤2中，所述历史运行数据先经过前置处理再进行故障特征提取；所述前置处理包括对历史运行数据的电信号进行除噪、滤波、平移和放大，得到幅值范围在0-5V内的标准运行数据。Further, in step 2, the historical operation data is subjected to preprocessing before fault feature extraction is performed; the preprocessing includes denoising, filtering, shifting and amplifying the electrical signal of the historical operation data to obtain an amplitude range. Standard operating data within 0-5V.

进一步地，步骤1-5中，采用数据采集器获取所有SCADA系统测量的风力发电机运行数据，并采用计算机存储并完成数据存储、故障诊断和结果显示。Further, in steps 1-5, a data collector is used to acquire all the wind turbine operation data measured by the SCADA system, and a computer is used to store and complete data storage, fault diagnosis and result display.

为了更好地理解和实施，下面结合附图详细说明本发明。For better understanding and implementation, the present invention is described in detail below with reference to the accompanying drawings.

附图说明Description of drawings

图1为风力发电机结构示意图；Fig. 1 is a schematic diagram of the structure of a wind turbine;

图2为风力发电机变桨系统结构示意图；Figure 2 is a schematic structural diagram of a wind turbine pitch system;

图3为本发明风力发电机变桨系统故障诊断方法的流程图。FIG. 3 is a flow chart of the fault diagnosis method for the pitch system of the wind turbine according to the present invention.

具体实施方式Detailed ways

本发明的风力发电机变桨系统故障诊断方法通过利用所述SCADA系统采集的风力发电机各种历史运行数据，来提取故障特征并构建变桨系统各部位的故障诊断模型，能够在风力发电机实时运行过程中准确判断风力发电机的变桨系统的具体故障原因与故障位置。The fault diagnosis method of the pitch system of the wind turbine of the present invention extracts the fault features and constructs the fault diagnosis model of each part of the pitch system by using various historical operation data of the wind turbine collected by the SCADA system, which can realize the real-time monitoring of the wind turbine. Accurately determine the specific fault cause and fault location of the pitch system of the wind turbine during operation.

具体地，所述风力发电机变桨系统故障诊断方法包括如下步骤：Specifically, the fault diagnosis method of the wind turbine pitch system includes the following steps:

步骤1：获取风力发电机的SCADA系统的历史运行数据，包括风力发电机机舱外平均风速、风向、风力发电机桨叶的桨距角、变桨系统低速轴的转速、发电机有功功率、变桨电机的电压和电流、变桨电机温度、桨叶变流器温度、变桨电池电压、变桨轴承油脂泵出口油压和变桨齿轮油脂泵出口油压；所述风力发电机桨叶的桨距角数据包括A编码器测量得到的桨距角数据于B编码器测量得到的编码器数据。Step 1: Obtain the historical operation data of the SCADA system of the wind turbine, including the average wind speed and direction outside the wind turbine cabin, the pitch angle of the wind turbine blades, the rotational speed of the low-speed shaft of the pitch system, the active power of the generator, the variable The voltage and current of the propeller motor, the temperature of the pitch motor, the temperature of the blade converter, the voltage of the pitch battery, the oil pressure at the outlet of the pitch bearing grease pump and the oil pressure at the outlet of the pitch gear grease pump; The pitch angle data includes the pitch angle data measured by the A encoder and the encoder data measured by the B encoder.

其中，所述风力发电机桨叶的桨距角数据包括A编码器测量得到的桨距角数据

Figure BDA0002386678470000101
和B编码器测量得到的编码器数据
Figure BDA0002386678470000102
所述变桨电池电压数包括第一至第三变桨电池的电压数据Vbp1-Vbp3；所述桨叶变流器温度数据包括第一至第三变桨电机的变流器温度数据tf cb1-tf cb3；所述变桨电机温度数据包括第一至第三变桨电机的温度数据tm1-tm3；所述变桨电机电流数据包括第一至第三变桨电机的电流数据Im1-Im3；所述变桨电机转速数据包括第一至第三变桨电机的转速数据nm1-nm3；所述变桨轴承油脂泵出口油压包括Wherein, the pitch angle data of the wind turbine blade includes the pitch angle data measured by the A encoder
Figure BDA0002386678470000101
and the encoder data measured by the B encoder
Figure BDA0002386678470000102
The pitch battery voltage numbers include the voltage data V bp1 -V bp3 of the first to third pitch batteries; the blade converter temperature data includes the converter temperature data t of the first to third pitch motors f cb1 -t f cb3 ; the pitch motor temperature data includes temperature data t m1 -t m3 of the first to third pitch motors; the pitch motor current data includes the currents of the first to third pitch motors Data I m1 -I m3 ; the rotational speed data of the pitch motor includes the rotational speed data n m1 -n m3 of the first to third pitch motors; the outlet oil pressure of the pitch bearing grease pump includes

步骤2：首先将所述历史运行数据进行前置处理：对每种历史运行数据的测量信号进行除噪、滤波、平移和放大，得到幅值范围在0-5V内的标准运行数据；而后利用所述标准运行数据构建风力发电机变桨系统各位置的故障特征模型。所述构建故障特征模型的方法具体如下：Step 2: First perform preprocessing on the historical operating data: de-noise, filter, translate and amplify the measurement signal of each historical operating data to obtain standard operating data with an amplitude range of 0-5V; The standard operating data is used to construct a fault characteristic model of each position of the pitch system of the wind turbine. The method for constructing the fault feature model is specifically as follows:

1)构建桨距角理论偏差故障特征模型：1) Construct the fault characteristic model of the theoretical deviation of pitch angle:

获取风机历史运行中的一组平均风速数据，将其中每个平均风速数据

Figure BDA0002386678470000111
分别带入下方公式组，由此求解得到一组对应的桨距角β：Obtain a set of average wind speed data in the historical operation of the fan, and convert each of the average wind speed data
Figure BDA0002386678470000111
Bring them into the following formulas respectively, and solve them to obtain a set of corresponding pitch angles β:

Figure BDA0002386678470000112
Figure BDA0002386678470000112

Figure BDA0002386678470000113
其中，ρ为空气密度，η为传动效率，R为风轮直径，P为风力发电机的额定功率，c1-c8为风轮特性系数，λi为第i片桨叶的叶尖速，λ为风轮尖速比；
Figure BDA0002386678470000113
Among them, ρ is the air density, η is the transmission efficiency, R is the diameter of the rotor, P is the rated power of the wind turbine, c 1 -c 8 is the characteristic coefficient of the rotor, and λ i is the tip speed of the ith blade , λ is the rotor tip speed ratio;

以该组平均风速

Figure BDA0002386678470000114
为自变量，并以计算得到的该组桨距角β为因变量，通过最小二乘法多项式进行拟合，得到所述桨距角理论值βth的表达式：with the average wind speed of the group
Figure BDA0002386678470000114
is an independent variable, and the calculated set of pitch angles β is used as a dependent variable, and the least squares polynomial is used for fitting to obtain the expression of the theoretical value of the pitch angle β th :

Figure BDA0002386678470000115
Figure BDA0002386678470000115

其中，

Figure BDA0002386678470000116
为风力发电机的额定风速；in,
Figure BDA0002386678470000116
is the rated wind speed of the wind turbine;

利用桨距角理论值βth构建桨距角理论偏差值

Figure BDA0002386678470000117
的计算模型，以该模型作为所述桨距角理论偏差故障特征模型：
Figure BDA0002386678470000118
其中，Δβ为桨距角绝对偏差限值，
Figure BDA0002386678470000119
是第i个A编码器测量得到的桨距角数据；
Figure BDA00023866784700001110
Figure BDA00023866784700001111
其中，Δβ为桨距角绝对偏差限值，
Figure BDA00023866784700001112
是第i个B编码器测量得到的桨距角数据。叶片桨距角正常时，桨距角理论偏差值
Figure BDA00023866784700001113
桨距角理论偏差值
Figure BDA00023866784700001114
是变桨角度出现异常的情况之一。Using the theoretical value of the pitch angle β th to construct the theoretical deviation value of the pitch angle
Figure BDA0002386678470000117
The calculation model of , and this model is used as the fault characteristic model of the theoretical deviation of the pitch angle:
Figure BDA0002386678470000118
Among them, Δβ is the absolute deviation limit of pitch angle,
Figure BDA0002386678470000119
is the pitch angle data measured by the i-th A encoder;
Figure BDA00023866784700001110
Figure BDA00023866784700001111
Among them, Δβ is the absolute deviation limit of pitch angle,
Figure BDA00023866784700001112
is the pitch angle data measured by the i-th B encoder. When the blade pitch angle is normal, the theoretical deviation value of the pitch angle
Figure BDA00023866784700001113
Theoretical deviation of pitch angle
Figure BDA00023866784700001114
It is one of the cases where the pitch angle is abnormal.

2)构建低速轴转速故障特征模型：2) Build a low-speed shaft rotational speed fault feature model:

所述低速轴转速故障特征模型包括风轮转速相对偏差计算模型和风轮转速相对波动计算模型；The low-speed shaft rotational speed fault characteristic model includes a calculation model for the relative deviation of the rotational speed of the wind rotor and a calculation model for the relative fluctuation of the rotational speed of the wind rotor;

构建所述风轮转速相对偏差计算模型：Build the calculation model of the relative deviation of the rotor speed:

Figure BDA00023866784700001115
Figure BDA00023866784700001115

其中，δnls为风轮转速相对偏差，nls为所述低速轴转速数据，

Figure BDA00023866784700001116
为低速轴转速理论值，且满足如下公式：Among them, δn ls is the relative deviation of the rotor speed, n ls is the low-speed shaft speed data,
Figure BDA00023866784700001116
is the theoretical value of the low-speed shaft speed and satisfies the following formula:

Figure BDA0002386678470000121
Figure BDA0002386678470000121

其中，n0为低速轴的额定转速，

Figure BDA0002386678470000122
为风力发电机的切入风速，
Figure BDA0002386678470000123
为风力发电机的切出风速；Among them, n 0 is the rated speed of the low-speed shaft,
Figure BDA0002386678470000122
is the cut-in wind speed of the wind turbine,
Figure BDA0002386678470000123
is the cut-out wind speed of the wind turbine;

构建风轮转速相对波动计算模型：Build a calculation model for the relative fluctuation of the rotor speed:

Figure BDA0002386678470000124
Figure BDA0002386678470000124

其中，δΔnms为风轮转速相对波动值，

Figure BDA0002386678470000125
为n秒内所述低速轴转速数据中的最大值，
Figure BDA0002386678470000126
为n秒内所述低速轴转速数据中的最小值。优选地，
Figure BDA0002386678470000127
为60秒内所述低速轴转速数据中的最大值，
Figure BDA0002386678470000128
为60秒内所述低速轴转速数据中的最小值。Among them, δΔn ms is the relative fluctuation value of the rotor speed,
Figure BDA0002386678470000125
is the maximum value in the low-speed shaft speed data in n seconds,
Figure BDA0002386678470000126
is the minimum value in the low-speed shaft rotational speed data in n seconds. Preferably,
Figure BDA0002386678470000127
is the maximum value of the low-speed shaft rotational speed data within 60 seconds,
Figure BDA0002386678470000128
is the minimum value of the low-speed shaft rotational speed data within 60 seconds.

3)构建桨距角偏差故障特征模型：3) Build the pitch angle deviation fault characteristic model:

所述桨距角偏差故障特征模型包括桨距角相对偏差计算模型和桨距角绝对偏差计算模型；The pitch angle deviation fault characteristic model includes a pitch angle relative deviation calculation model and a pitch angle absolute deviation calculation model;

构建风力发电机第一桨叶至第三桨叶的桨距角相对偏差计算模型：Build a calculation model for the relative deviation of the pitch angle from the first blade to the third blade of the wind turbine:

Figure BDA0002386678470000129
Figure BDA0002386678470000129

Figure BDA00023866784700001210
Figure BDA00023866784700001210

其中，

Figure BDA00023866784700001211
为三个A编码器测量数据的平均桨距角，
Figure BDA00023866784700001212
Figure BDA00023866784700001213
为三个B编码器测量数据的平均桨距角，
Figure BDA00023866784700001214
Figure BDA00023866784700001215
为由A编码器测量数据得到的桨距角相对偏差，
Figure BDA0002386678470000131
为由B编码器测量数据得到的桨距角相对偏差；in,
Figure BDA00023866784700001211
The average pitch angle of the measured data for the three A encoders,
Figure BDA00023866784700001212
Figure BDA00023866784700001213
The average pitch angle of the measured data for the three B encoders,
Figure BDA00023866784700001214
Figure BDA00023866784700001215
is the relative deviation of the pitch angle obtained from the measurement data of the A encoder,
Figure BDA0002386678470000131
is the relative deviation of the pitch angle obtained from the measurement data of the B encoder;

构建风力发电机第一桨叶至第三桨叶的桨距角绝对偏差计算模型：Build a calculation model for the absolute deviation of the pitch angle from the first blade to the third blade of the wind turbine:

Figure BDA0002386678470000132
Figure BDA0002386678470000132

4)构建变桨电池组故障特征模型：4) Build the fault feature model of the pitch battery pack:

所述变桨电池电压数包括第一至第三变桨电池的电压数据Vbp1-Vbp3，The pitch battery voltage number includes the voltage data V bp1 -V bp3 of the first to third pitch batteries,

所述变桨电池组故障特征模型为：The fault characteristic model of the pitch battery pack is:

Figure BDA0002386678470000133
Figure BDA0002386678470000133

其中，Vbp0为第一至第三变桨电池组的正常电压，Among them, V bp0 is the normal voltage of the first to third pitch battery packs,

δVbp1-δVbp3为第一至第三变桨电池组的电压相对偏差。δV bp1 -δV bp3 are the relative voltage deviations of the first to third pitch battery packs.

5)构建变桨电机的变流器温度故障特征模型δtf cbi：5) Construct the converter temperature fault characteristic model δt f cbi of the pitch motor:

Figure BDA0002386678470000134
Figure BDA0002386678470000134

其中，tf cb0为所述变流器的温度上限值，tf cbi为第i个变桨电机的变流器温度数据，δtf cbi为第i个变桨电机的变流器温度偏差。Among them, t f cb0 is the upper temperature limit of the converter, t f cbi is the converter temperature data of the i-th pitch motor, and δt f cbi is the converter temperature deviation of the i-th pitch motor .

6)构建变桨电机温度偏差故障特征模型：6) Construct the temperature deviation fault characteristic model of the pitch motor:

所述变桨电机温度数据包括第一至第三变桨电机的温度数据tm1-tm3；The pitch motor temperature data includes temperature data t m1 -t m3 of the first to third pitch motors;

所述变桨电机温度偏差故障特征模型为：The temperature deviation fault characteristic model of the pitch motor is:

Figure BDA0002386678470000141
Figure BDA0002386678470000141

其中，tm0为变桨电机的温度偏差限值，δtm1-δtm3为第一至第三变桨电机的温度偏差。Among them, t m0 is the temperature deviation limit of the pitch motor, and δ tm1 -δ tm3 are the temperature deviations of the first to third pitch motors.

7)构建变桨电机电流故障特征模型：7) Build the current fault characteristic model of the pitch motor:

所述变桨电机电流数据包括第一至第三变桨电机的电流数据Im1-Im3；The pitch motor current data includes current data I m1 -I m3 of the first to third pitch motors;

所述变桨电机电流故障特征模型为：The current fault characteristic model of the pitch motor is:

Figure BDA0002386678470000142
Figure BDA0002386678470000142

其中，Im0为变桨电机的电流偏差限值，δIm1-δIm3为第一至第三变桨电机的电流偏差。Wherein, I m0 is the current deviation limit of the pitch motor, and δI m1 -δI m3 are the current deviations of the first to third pitch motors.

8)构建变桨电机转速特征模型：8) Construct the rotational speed characteristic model of the pitch motor:

所述变桨电机转速数据包括第一至第三变桨电机的转速数据nm1-nm3；The rotational speed data of the pitch motors includes rotational speed data n m1 -n m3 of the first to third pitch motors;

所述变桨电机转速特征模型为：The rotational speed characteristic model of the pitch motor is:

Figure BDA0002386678470000143
Figure BDA0002386678470000143

其中，nm0为变桨电机的平均转速，且满足

Figure BDA0002386678470000144
δnm1-δnm3为第一至第三变桨电机的转速偏差。Among them, n m0 is the average speed of the pitch motor, and it satisfies
Figure BDA0002386678470000144
δn m1 -δn m3 are the rotational speed deviations of the first to third pitch motors.

9)构建变桨轴承油脂泵故障特征模型：9) Build the fault characteristic model of the pitch bearing grease pump:

Figure BDA0002386678470000145
Figure BDA0002386678470000145

其中，

Figure BDA0002386678470000151
分别为第i个变桨轴承油脂泵出口压力上限值和下限值，pbgpi为第i个变桨轴承油脂泵出口压力数据，
Figure BDA0002386678470000152
和
Figure BDA0002386678470000153
为第i个变桨轴油脂泵压力上限偏差与压力下限偏差。in,
Figure BDA0002386678470000151
are the upper limit and lower limit of the outlet pressure of the ith pitch bearing grease pump, respectively, p bgpi is the outlet pressure data of the ith pitch bearing grease pump,
Figure BDA0002386678470000152
and
Figure BDA0002386678470000153
are the upper and lower pressure deviations of the grease pump of the i-th pitch shaft.

10)构建变桨齿轮油脂泵出口压力故障特征模型：10) Construct the characteristic model of the outlet pressure fault of the pitch gear grease pump:

Figure BDA0002386678470000154
Figure BDA0002386678470000154

其中，

Figure BDA0002386678470000155
分别为第i个变桨齿轮油脂泵出口压力上限值和下限值，pggpi为第i个变桨齿轮油脂泵出口压力数据，
Figure BDA0002386678470000156
和
Figure BDA0002386678470000157
第i个变桨齿轮油脂泵压力上限偏差与压力下限偏差。in,
Figure BDA0002386678470000155
are the upper limit and lower limit of the outlet pressure of the ith pitch gear grease pump respectively, p ggpi is the outlet pressure data of the ith pitch gear grease pump,
Figure BDA0002386678470000156
and
Figure BDA0002386678470000157
The deviation of the pressure upper limit and the pressure lower limit of the i-th pitch gear grease pump.

步骤3：利用所述故障特征构建故障诊断模型，具体如下：Step 3: Build a fault diagnosis model using the fault features, as follows:

1)构建变桨电源系统故障诊断模型：1) Build a fault diagnosis model for the pitch power system:

1.1构建变桨电池故障诊断模型：1.1 Build the pitch battery fault diagnosis model:

若对于第i个桨叶的电池组有：If the battery pack for the i-th blade has:

所述第一至第三变桨电池组的电压相对偏差满足δVbpi≥100％*(Vs-Vd)/Vs，则可判断第i个电池组发生故障，其中，Vs为电池组的额定电压，Vd为电池组的放电终止电压，当工作时电池组电压小于或等于放电终止电压则表示该电池组已经损坏。If the relative voltage deviations of the first to third pitch battery packs satisfy δV bpi ≥100%*(V s -V d )/V s , it can be determined that the ith battery pack is faulty, where V s is the battery pack The rated voltage of the battery pack, V d is the discharge termination voltage of the battery pack. When the battery pack voltage is less than or equal to the discharge termination voltage during operation, it means that the battery pack has been damaged.

1.2构建变桨电池充电器故障诊断模型：1.2 Build a fault diagnosis model for the pitch battery charger:

由于所述第一至第三变桨电池组独立工作，不存在相互影响，罕见同时出现故障的情况，因此，当所述第一至第三变桨电池组的电压相对偏差满足：Since the first to third pitch battery packs work independently, there is no mutual influence, and it is rare that faults occur at the same time. Therefore, when the voltage relative deviations of the first to third pitch battery packs satisfy:

Figure BDA0002386678470000158
则判断为变桨电池充电器故障。
Figure BDA0002386678470000158
It is judged that the pitch battery charger is faulty.

1.3构建变流器高温故障诊断模型：1.3 Build the high temperature fault diagnosis model of the converter:

由于变流器具有热过载保护，为防止热过载保护系统启动影响风力发电机正常运行，当变流器温度超过温度限值10％需要及时告警，因此，当第一至第三个变桨电机的变流器温度偏差满足：Since the converter has thermal overload protection, in order to prevent the thermal overload protection system from affecting the normal operation of the wind turbine, when the temperature of the converter exceeds the temperature limit by 10%, a timely alarm is required. Therefore, when the first to third pitch motors The converter temperature deviation satisfies:

δtf cbi≥10.0％，则可判断第i个变桨电机的变流器出现高温故障。If δt f cbi ≥ 10.0%, it can be judged that the converter of the i-th pitch motor has a high temperature fault.

2)构建变桨系统机械部件故障诊断模型：2) Build a fault diagnosis model for the mechanical components of the pitch system:

2.1构建变桨齿轮箱故障诊断模型：2.1 Build the fault diagnosis model of the pitch gearbox:

变桨电机所允许的最大电流一般为额定电流的110％，则当变桨电机电流大于额定电流10％，其温度将超出温度限值10％达到告警值，因此，此特征可用做故障判断指标。因此当所述第i个变桨电机的电流偏差和第i个变桨电机的温度偏差满足：The maximum current allowed by the pitch motor is generally 110% of the rated current. When the current of the pitch motor is greater than 10% of the rated current, its temperature will exceed the temperature limit by 10% and reach the alarm value. Therefore, this feature can be used as a fault judgment indicator. . Therefore, when the current deviation of the i-th pitch motor and the temperature deviation of the i-th pitch motor satisfy:

Figure BDA0002386678470000161
可判断第i个变桨齿轮箱故障。
Figure BDA0002386678470000161
It can be judged that the i-th pitch gearbox is faulty.

若同时第i个变桨齿轮油脂泵上限偏差与下限偏差还满足：

Figure BDA0002386678470000162
或
Figure BDA0002386678470000163
Figure BDA0002386678470000164
可进一步判断出：第i个变桨齿轮箱故障。At the same time, if the upper limit deviation and lower limit deviation of the i-th pitch gear grease pump also satisfy:
Figure BDA0002386678470000162
or
Figure BDA0002386678470000163
Figure BDA0002386678470000164
It can be further judged that the i-th pitch gearbox is faulty.

2.2构建变桨轴承故障诊断模型：2.2 Construct the fault diagnosis model of pitch bearing:

三个变桨轴承相互独立，互不影响，罕见三个变桨轴承同时出现故障的情况，因此，当当下式成立：The three pitch bearings are independent of each other and do not affect each other. It is rare for the three pitch bearings to fail at the same time. Therefore, when the following formula is established:

第一至第三变桨电机的电流偏差满足

Figure BDA0002386678470000165
The current deviation of the first to third pitch motors satisfies
Figure BDA0002386678470000165

且第一至第三变桨电机的温度偏差满足

Figure BDA0002386678470000166
And the temperature deviation of the first to third pitch motors satisfies
Figure BDA0002386678470000166

且第一至第三个变桨轴油脂泵上限偏差与下限偏差满足条件：

Figure BDA0002386678470000167
或
Figure BDA0002386678470000168
即可判断变桨轴承出现故障。And the upper limit deviation and lower limit deviation of the first to third pitch shaft grease pumps meet the conditions:
Figure BDA0002386678470000167
or
Figure BDA0002386678470000168
It can be judged that the pitch bearing is faulty.

3)构建变桨系统的控制系统故障诊断模型：3) Build the control system fault diagnosis model of the pitch system:

变桨系统出现故障时，排除1)中的变桨电源系统故障与2)中的变桨系统机械部件故障后，若变桨系统仍存在故障点，则考虑为变桨系统的控制系统故障。具体诊断方法如下：When the pitch system fails, after eliminating the pitch power system failure in 1) and the mechanical component failure of the pitch system in 2), if the pitch system still has a fault point, it is considered as a control system failure of the pitch system. The specific diagnosis methods are as follows:

3.1构建变桨角度差异故障诊断模型：3.1 Build a fault diagnosis model for pitch angle difference:

已知现有风力发电机组的变桨角度偏差Δβbi的数值上限值为2°，并设变桨角度许用偏差为a％，则将变桨角度偏差上限值限定取为2a％，当桨距角相对偏差和变桨角度偏差Δβbi满足下式：It is known that the numerical upper limit of the pitch angle deviation Δβbi of the existing wind turbine is 2°, and the allowable pitch angle deviation is set to a%, then the upper limit of the pitch angle deviation is limited to 2a%, When the relative pitch angle deviation and pitch angle deviation Δβ bi satisfy the following formula:

Figure BDA0002386678470000169
Figure BDA0002386678470000169

且

Figure BDA00023866784700001610
and
Figure BDA00023866784700001610

则判断第i个桨叶的B编码器故障；Then it is judged that the B encoder of the i-th blade is faulty;

当桨距角相对偏差和变桨角度偏差Δβbi满足下式：When the relative pitch angle deviation and pitch angle deviation Δβ bi satisfy the following formula:

Figure BDA00023866784700001611
Figure BDA00023866784700001611

且

Figure BDA0002386678470000171
则判断第i个桨叶的A编码器故障。and
Figure BDA0002386678470000171
Then it is judged that the A encoder of the i-th blade is faulty.

优选地，a的取值为5。Preferably, the value of a is 5.

3.2构建桨叶限位开关触发偏早故障诊断模型：3.2 Build a fault diagnosis model for the early failure of the blade limit switch trigger:

现有的风力发电系统中，桨叶角度的限位器设定值一般为91°，如果桨距角相对偏差和桨叶角度满足下式：In the existing wind power generation system, the set value of the limiter of the blade angle is generally 91°. If the relative deviation of the pitch angle and the blade angle satisfy the following formula:

Figure BDA0002386678470000172
Figure BDA0002386678470000172

Figure BDA0002386678470000173
Figure BDA0002386678470000173

即表示编码器正常工作，此时桨叶尚未达到正常的叶片限位开关的动作值，对此时出现的故障告警，诊断故障原因为桨叶限位开关触发偏早。It means that the encoder is working normally, and the blade has not yet reached the normal action value of the blade limit switch. For the fault alarm that occurs at this time, the cause of the diagnosis is that the blade limit switch is triggered too early.

3.3构建变桨电机高温故障诊断模型：3.3 Build a high temperature fault diagnosis model for the pitch motor:

如果：变桨电机温度偏差δtmi≥10.0％，即该变桨电机温度将超出温度限值10％，可判断出第i桨叶出现了变桨电机高温故障；If: the pitch motor temperature deviation δt mi ≥10.0%, that is, the pitch motor temperature will exceed the temperature limit by 10%, it can be judged that the pitch motor high temperature fault occurs on the i-th blade;

如果变桨电机温度偏差与变桨电机电流偏差满足：

Figure BDA0002386678470000174
可判断出第i桨叶出现了变桨电机高温故障，且该故障多由变桨齿轮箱故障引起。If the pitch motor temperature deviation and the pitch motor current deviation satisfy:
Figure BDA0002386678470000174
It can be judged that the pitch motor high temperature fault occurs in the i-th blade, and this fault is mostly caused by the fault of the pitch gearbox.

3.4构建变桨电机高转速故障诊断模型：3.4 Build a high-speed fault diagnosis model for the pitch motor:

一般地，现有风力发电机许用转速偏差同为所述a％，且限定变桨电机转速不超过31(deg/s)，如果以下两式成立：Generally, the allowable rotational speed deviation of the existing wind turbines is the same as the above a%, and the rotational speed of the pitch motor is limited to not exceed 31 (deg/s), if the following two equations are established:

变桨电机的转速偏差满足

Figure BDA0002386678470000175
The speed deviation of the pitch motor meets the
Figure BDA0002386678470000175

且变桨电机转速满足

Figure BDA0002386678470000176
And the speed of the pitch motor satisfies
Figure BDA0002386678470000176

可判断出现了变桨电机转速过高故障，且此故障多由A编码器故障引起。It can be judged that there is a fault that the speed of the pitch motor is too high, and this fault is mostly caused by the fault of the A encoder.

3.5构建变桨失效故障诊断模型：3.5 Build a fault diagnosis model for pitch failure:

当编码器运行良好，而桨叶偏差超过限定值，即为变桨失效情况，即当下式成立：When the encoder runs well and the blade deviation exceeds the limit value, it is the pitch failure situation, that is, the following formula is established:

A编码器测得的桨距角相对偏差满足

Figure BDA0002386678470000177
The relative deviation of the pitch angle measured by the A encoder satisfies the
Figure BDA0002386678470000177

且B编码器测得的桨距角相对偏差满足

Figure BDA0002386678470000181
And the relative deviation of pitch angle measured by B encoder satisfies
Figure BDA0002386678470000181

且第一至第三桨叶的桨距角理论偏差满足

Figure BDA0002386678470000182
And the theoretical deviation of the pitch angle of the first to third blades satisfies
Figure BDA0002386678470000182

则可判断出现了变桨失效故障。It can be judged that there is a pitch failure failure.

3.6构建变桨系统信号传输故障诊断模型：3.6 Build a fault diagnosis model for the signal transmission of the pitch system:

当下式成立：The present formula is established:

低速轴转速相对偏差满足δnls≥10.0％且风轮转速相对波动满足δΔnms≥10.0％，则可判断出现了变桨控制信号传输故障。If the relative deviation of the low-speed shaft speed satisfies δn ls ≥ 10.0% and the relative fluctuation of the rotor speed satisfies δΔn ms ≥ 10.0%, it can be judged that there is a pitch control signal transmission failure.

步骤4：获取风力发电机的SCADA系统的实时故障告警数据，并输入至故障特征模型，计算获取实时故障特征。Step 4: Obtain the real-time fault alarm data of the SCADA system of the wind turbine, and input it into the fault feature model, and calculate and obtain the real-time fault feature.

步骤5：将步骤4中得到的实时故障特征输入所述故障诊断模型，获取故障诊断结果，所述故障诊断结果包括故障原因与故障位置。Step 5: Input the real-time fault feature obtained in Step 4 into the fault diagnosis model to obtain a fault diagnosis result, where the fault diagnosis result includes the fault cause and the fault location.

本实施例中，采用数据采集器获取SCADA系统测量的上述各类风力发电机运行数据，并采用计算机存储并完成数据存储、故障诊断和结果显示。In this embodiment, a data collector is used to obtain the above-mentioned various types of wind turbine operation data measured by the SCADA system, and a computer is used to store and complete data storage, fault diagnosis and result display.

相比于现有技术，本实施例的风力发电机变桨系统故障诊断方法从现有的SCADA系统中获取所需的风机运行数据，并从中提取变桨系统电源系统、变桨系统机械部件和变桨系统的控制系统中各个故障高发位置的故障特征，构建全面的故障特征模型，且能够诊断风力发电机组的实时故障位置于故障原因，提高检修效率。Compared with the prior art, the method for diagnosing the fault of the pitch system of the wind turbine in the present embodiment obtains the required operation data of the fan from the existing SCADA system, and extracts the power supply system of the pitch system, the mechanical parts of the pitch system and the variable pitch system therefrom. The fault characteristics of each fault-prone position in the control system of the propeller system are used to build a comprehensive fault characteristic model, which can diagnose the real-time fault location of the wind turbine and the cause of the fault, and improve the maintenance efficiency.

以上所述实施例仅表达了本发明的几种实施方式，其描述较为具体和详细，但并不能因此而理解为对发明专利范围的限制。应当指出的是，对于本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些都属于本发明的保护范围。The above-mentioned embodiments only represent several embodiments of the present invention, and the descriptions thereof are specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can also be made, which all belong to the protection scope of the present invention.

Claims (10)

1.一种风力发电机变桨系统故障诊断方法，其特征在于，包括如下步骤：1. a method for diagnosing faults of a wind turbine pitch system, is characterized in that, comprises the steps: 步骤1：获取风力发电机的SCADA系统的历史运行数据；Step 1: Obtain the historical operation data of the SCADA system of the wind turbine; 步骤2：利用所述SCADA系统的历史运行数据构建故障特征模型；Step 2: use the historical operation data of the SCADA system to construct a fault feature model; 步骤3：利用所述故障特征模型构建故障诊断模型；Step 3: using the fault feature model to construct a fault diagnosis model; 步骤4：获取风力发电机的SCADA系统的实时故障告警数据，并输入至故障特征模型，计算获取实时故障特征；Step 4: Obtain the real-time fault alarm data of the SCADA system of the wind turbine, and input it into the fault characteristic model, and calculate and obtain the real-time fault characteristics; 步骤5：将步骤4中得到的实时故障特征输入所述故障诊断模型，获取故障诊断结果。Step 5: Input the real-time fault features obtained in Step 4 into the fault diagnosis model to obtain fault diagnosis results. 2.根据权利要求1所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤1中，所述SCADA系统的历史运行数据包括：风力发电机机舱外平均风速、风向、风力发电机第一桨叶至第三桨叶的桨距角、低速轴转速、发电机有功功率、变桨电机的电压和电流、变桨电机温度、桨叶变流器温度、变桨电池电压、变桨轴承油脂泵出口油压和变桨齿轮油脂泵出口油压中的一种或多种。2. The fault diagnosis method for wind turbine pitch system according to claim 1, it is characterized in that: in step 1, the historical operation data of described SCADA system comprises: average wind speed outside wind turbine cabin, wind direction, wind turbine No. Pitch angle from one blade to the third blade, low-speed shaft speed, generator active power, pitch motor voltage and current, pitch motor temperature, blade converter temperature, pitch battery voltage, pitch bearing One or more of grease pump outlet oil pressure and pitch gear grease pump outlet oil pressure. 3.根据权利要求2所述的风力发电机变桨系统故障诊断方法，其特征在于：3. The fault diagnosis method for a wind turbine pitch system according to claim 2, wherein: 所述构建故障特征模型的方法包括：The method for constructing a fault feature model includes: 1)构建桨距角理论偏差故障特征模型：1) Construct the fault characteristic model of the theoretical deviation of pitch angle: 所述风力发电机桨叶的桨距角数据包括A编码器测量得到的桨距角数据
Figure FDA0002386678460000011
和B编码器测量得到的编码器数据
Figure FDA0002386678460000012
获取风机历史运行中的一组平均风速数据，将其中每个平均风速数据
Figure FDA0002386678460000013
分别带入下方公式组，由此求解得到一组对应的桨距角β：The pitch angle data of the wind turbine blade includes the pitch angle data measured by the A encoder
Figure FDA0002386678460000011
and the encoder data measured by the B encoder
Figure FDA0002386678460000012
Obtain a set of average wind speed data in the historical operation of the fan, and convert each of the average wind speed data
Figure FDA0002386678460000013
Bring them into the following formulas respectively, and solve them to obtain a set of corresponding pitch angles β:
Figure FDA0002386678460000014
Figure FDA0002386678460000014
Figure FDA0002386678460000015
其中，ρ为空气密度，η为传动效率，R为风轮直径，P为风力发电机的额定功率，c1-c8为风轮特性系数，λi为第i片桨叶的叶尖速，λ为风轮尖速比；
Figure FDA0002386678460000015
Among them, ρ is the air density, η is the transmission efficiency, R is the diameter of the rotor, P is the rated power of the wind turbine, c 1 -c 8 is the characteristic coefficient of the rotor, and λ i is the tip speed of the ith blade , λ is the rotor tip speed ratio; 以该组平均风速
Figure FDA0002386678460000016
为自变量，并以计算得到的该组桨距角β为因变量，通过最小二乘法多项式进行拟合，获得桨距角理论值βth的计算公式：with the average wind speed of the group
Figure FDA0002386678460000016
is the independent variable, and the calculated set of pitch angles β is used as the dependent variable, and is fitted by the least squares polynomial to obtain the calculation formula of the theoretical value of the pitch angle β th :
Figure FDA0002386678460000017
Figure FDA0002386678460000017
 其中，
Figure FDA0002386678460000018
为风力发电机的额定风速；in,
Figure FDA0002386678460000018
is the rated wind speed of the wind turbine; 利用桨距角理论值βth构建桨距角理论偏差值
Figure FDA0002386678460000019
的计算模型，以该模型作为所述桨距角理论偏差故障特征模型：
Figure FDA0002386678460000021
其中，Δβ为桨距角绝对偏差限值，
Figure FDA0002386678460000022
是第i个A编码器测量得到的桨距角数据；Using the theoretical value of the pitch angle β th to construct the theoretical deviation value of the pitch angle
Figure FDA0002386678460000019
The calculation model of , and this model is used as the fault characteristic model of the theoretical deviation of the pitch angle:
Figure FDA0002386678460000021
Among them, Δβ is the absolute deviation limit of pitch angle,
Figure FDA0002386678460000022
is the pitch angle data measured by the i-th A encoder;
Figure FDA0002386678460000023
其中，Δβ为桨距角绝对偏差限值，
Figure FDA0002386678460000024
是第i个B编码器测量得到的桨距角数据；
Figure FDA0002386678460000023
Among them, Δβ is the absolute deviation limit of pitch angle,
Figure FDA0002386678460000024
is the pitch angle data measured by the i-th B encoder; 和/或and / or 2)构建低速轴转速故障特征模型：2) Build a low-speed shaft rotational speed fault feature model: 所述低速轴转速故障特征模型包括风轮转速相对偏差计算模型和风轮转速相对波动计算模型；The low-speed shaft rotational speed fault characteristic model includes a calculation model for the relative deviation of the rotational speed of the wind rotor and a calculation model for the relative fluctuation of the rotational speed of the wind rotor; 构建所述风轮转速相对偏差计算模型：Build the calculation model of the relative deviation of the rotor speed:
Figure FDA0002386678460000025
Figure FDA0002386678460000025
 其中，δnls为风轮转速相对偏差，nls为所述低速轴转速数据，
Figure FDA0002386678460000026
为低速轴转速理论值，且满足如下公式：Among them, δn ls is the relative deviation of the rotor speed, n ls is the low-speed shaft speed data,
Figure FDA0002386678460000026
is the theoretical value of the low-speed shaft speed and satisfies the following formula:
Figure FDA0002386678460000027
Figure FDA0002386678460000027
 其中，n0为低速轴的额定转速，
Figure FDA0002386678460000028
为风力发电机的切入风速，
Figure FDA0002386678460000029
为风力发电机的切出风速；Among them, n 0 is the rated speed of the low-speed shaft,
Figure FDA0002386678460000028
is the cut-in wind speed of the wind turbine,
Figure FDA0002386678460000029
is the cut-out wind speed of the wind turbine; 构建风轮转速相对波动计算模型：Build a calculation model for the relative fluctuation of the rotor speed:
Figure FDA00023866784600000210
其中，δΔnms为风轮转速相对波动值，
Figure FDA00023866784600000211
为数秒内所述低速轴转速数据中的最大值，
Figure FDA00023866784600000212
为数秒内所述低速轴转速数据中的最小值；
Figure FDA00023866784600000210
Among them, δΔn ms is the relative fluctuation value of the rotor speed,
Figure FDA00023866784600000211
is the maximum value of the low-speed shaft speed data in seconds,
Figure FDA00023866784600000212
is the minimum value of the low-speed shaft speed data in several seconds; 和/或and / or 3)构建桨距角偏差故障特征模型：3) Build the pitch angle deviation fault characteristic model: 所述桨距角偏差故障特征模型包括桨距角相对偏差计算模型和桨距角绝对偏差计算模型；The pitch angle deviation fault characteristic model includes a pitch angle relative deviation calculation model and a pitch angle absolute deviation calculation model; 构建风力发电机第一桨叶至第三桨叶的桨距角相对偏差计算模型：Build a calculation model for the relative deviation of the pitch angle from the first blade to the third blade of the wind turbine:
Figure FDA0002386678460000031
Figure FDA0002386678460000031
Figure FDA0002386678460000032
Figure FDA0002386678460000032
 其中，
Figure FDA0002386678460000033
为三个A编码器测量数据的平均桨距角，
Figure FDA0002386678460000034
为三个B编码器测量数据的平均桨距角，
Figure FDA0002386678460000035
为由A编码器测量数据得到的桨距角相对偏差，
Figure FDA0002386678460000036
为由B编码器测量数据得到的桨距角相对偏差；in,
Figure FDA0002386678460000033
The average pitch angle of the measured data for the three A encoders,
Figure FDA0002386678460000034
The average pitch angle of the measured data for the three B encoders,
Figure FDA0002386678460000035
is the relative deviation of the pitch angle obtained from the measurement data of the A encoder,
Figure FDA0002386678460000036
is the relative deviation of the pitch angle obtained from the measurement data of the B encoder; 构建风力发电机第一桨叶至第三桨叶的桨距角绝对偏差计算模型：Build a calculation model for the absolute deviation of the pitch angle from the first blade to the third blade of the wind turbine:
Figure FDA0002386678460000037
Figure FDA0002386678460000037
 和/或and / or 4)构建变桨电池组故障特征模型：4) Build the fault feature model of the pitch battery pack: 所述变桨电池电压数包括第一至第三变桨电池的电压数据Vbp1-Vbp3，The pitch battery voltage number includes the voltage data V bp1 -V bp3 of the first to third pitch batteries, 所述变桨电池组故障特征模型为：The fault characteristic model of the pitch battery pack is:
Figure FDA0002386678460000038
Figure FDA0002386678460000038
 其中，Vbp0为第一至第三变桨电池组的正常电压，Among them, V bp0 is the normal voltage of the first to third pitch battery packs, δVbp1-δVbp3为第一至第三变桨电池组的电压相对偏差；和/或δV bp1 - δV bp3 are the relative voltage deviations of the first to third pitch battery packs; and/or 5)构建变桨电机的变流器温度故障特征模型δtfcbi：5) Construct the converter temperature fault characteristic model δt fcbi of the pitch motor:
Figure FDA0002386678460000041
Figure FDA0002386678460000041
 其中，tfcb0为所述变流器的温度上限值，tfcbi为第i个变桨电机的变流器温度数据，δtfcbi为第i个变桨电机的变流器温度偏差；和/或where t fcb0 is the upper temperature limit of the converter, t fcbi is the converter temperature data of the ith pitch motor, and δt fcbi is the converter temperature deviation of the ith pitch motor; and/ or 6)构建变桨电机温度偏差故障特征模型：6) Construct the fault characteristic model of pitch motor temperature deviation: 所述变桨电机温度数据包括第一至第三变桨电机的温度数据tm1-tm3；The pitch motor temperature data includes temperature data t m1 -t m3 of the first to third pitch motors; 所述变桨电机温度偏差故障特征模型为：The temperature deviation fault characteristic model of the pitch motor is:
Figure FDA0002386678460000042
Figure FDA0002386678460000042
 其中，tm0为变桨电机的温度偏差限值，δtm1-δtm3为第一至第三变桨电机的温度偏差；和/或where t m0 is the temperature deviation limit of the pitch motor, and δt m1 - δt m3 are the temperature deviations of the first to third pitch motors; and/or 7)构建变桨电机电流故障特征模型：7) Build the current fault characteristic model of the pitch motor: 所述变桨电机电流数据包括第一至第三变桨电机的电流数据Im1-Im3；The pitch motor current data includes current data I m1 -I m3 of the first to third pitch motors; 所述变桨电机电流故障特征模型为：The current fault characteristic model of the pitch motor is:
Figure FDA0002386678460000043
Figure FDA0002386678460000043
 其中，Im0为变桨电机的电流偏差限值，δIm1-δIm3为第一至第三变桨电机的电流偏差；和/或Wherein, I m0 is the current deviation limit of the pitch motor, and δI m1 - δI m3 are the current deviations of the first to third pitch motors; and/or 8)构建变桨电机转速特征模型：8) Construct the rotational speed characteristic model of the pitch motor: 所述变桨电机转速数据包括第一至第三变桨电机的转速数据nm1-nm3；The rotational speed data of the pitch motors includes rotational speed data n m1 -n m3 of the first to third pitch motors; 所述变桨电机转速特征模型为：The rotational speed characteristic model of the pitch motor is:
Figure FDA0002386678460000051
Figure FDA0002386678460000051
 其中，nm0为变桨电机的平均转速，且满足
Figure FDA0002386678460000052
δnm1-δnm3为第一至第三变桨电机的转速偏差；Among them, n m0 is the average speed of the pitch motor, and it satisfies
Figure FDA0002386678460000052
δn m1 -δn m3 are the rotational speed deviations of the first to third pitch motors; 和/或and / or 9)构建变桨轴承油脂泵故障特征模型：9) Build the fault characteristic model of the pitch bearing grease pump:
Figure FDA0002386678460000053
Figure FDA0002386678460000053
 其中，
Figure FDA0002386678460000054
分别为第i个变桨轴承油脂泵出口压力上限值和下限值，pbgpi为第i个变桨轴承油脂泵出口压力数据，
Figure FDA0002386678460000055
和
Figure FDA0002386678460000056
为第i个变桨轴油脂泵压力上限偏差与压力下限偏差；in,
Figure FDA0002386678460000054
are the upper limit and lower limit of the outlet pressure of the ith pitch bearing grease pump, respectively, p bgpi is the outlet pressure data of the ith pitch bearing grease pump,
Figure FDA0002386678460000055
and
Figure FDA0002386678460000056
is the pressure upper limit deviation and the pressure lower limit deviation of the ith pitch shaft grease pump; 和/或and / or 10)构建变桨齿轮油脂泵出口压力故障特征模型：10) Construct the characteristic model of the outlet pressure fault of the pitch gear grease pump:
Figure FDA0002386678460000057
Figure FDA0002386678460000057
 其中，
Figure FDA0002386678460000058
分别为第i个变桨齿轮油脂泵出口压力上限值和下限值，pggpi为第i个变桨齿轮油脂泵出口压力数据，
Figure FDA0002386678460000059
和
Figure FDA00023866784600000510
第i个变桨齿轮油脂泵压力上限偏差与压力下限偏差。in,
Figure FDA0002386678460000058
are the upper limit and lower limit of the outlet pressure of the ith pitch gear grease pump respectively, p ggpi is the outlet pressure data of the ith pitch gear grease pump,
Figure FDA0002386678460000059
and
Figure FDA00023866784600000510
The deviation of the pressure upper limit and the pressure lower limit of the i-th pitch gear grease pump. 4.根据权利要求3所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤3中，4. The fault diagnosis method for a wind turbine pitch system according to claim 3, characterized in that: in step 3, 所述构建故障诊断模型的方法包括构建变桨电源系统故障诊断模型：The method for constructing a fault diagnosis model includes constructing a fault diagnosis model of a pitch power system: 1)构建变桨电池故障诊断模型：1) Build the pitch battery fault diagnosis model: 若对于第i个桨叶的电池组有：If the battery pack for the i-th blade has: 所述第一至第三变桨电池组的电压相对偏差满足δVbpi≥100％*(Vs-Vd)/Vs，The voltage relative deviations of the first to third pitch battery packs satisfy δV bpi ≥100%*(V s -V d )/V s , 则可判断第i个电池组发生故障，其中，Vs为电池组的额定电压，Vd为电池组的放电终止电压；和/或Then it can be judged that the i-th battery pack is faulty, wherein V s is the rated voltage of the battery pack, and V d is the discharge termination voltage of the battery pack; and/or 2)构建变桨电池充电器故障诊断模型：2) Build a fault diagnosis model for the pitch battery charger: 当所述当第一至第三变桨电池组的电压相对偏差满足：
Figure FDA0002386678460000061
则判断为变桨电池充电器故障；和/或When the voltage relative deviation of the first to third pitch battery packs satisfies:
Figure FDA0002386678460000061
then it is determined that the pitch battery charger is faulty; and/or 3)构建变流器高温故障诊断模型：3) Construct the high temperature fault diagnosis model of the converter: 当所述第i个变桨电机的变流器温度偏差满足：δtfcbi≥10.0％，则可判断第i个变桨电机的变流器出现高温故障。When the temperature deviation of the converter of the i-th pitch motor satisfies: δt fcbi ≥10.0 %, it can be determined that the converter of the i-th pitch motor has a high temperature fault. 5.根据权利要求4所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤3中，5. The fault diagnosis method for a wind turbine pitch system according to claim 4, characterized in that: in step 3, 所述构建故障诊断模型的方法还包括构建变桨系统机械部件故障诊断模型：The method for constructing a fault diagnosis model further includes constructing a fault diagnosis model for the mechanical components of the pitch system: 1)构建变桨齿轮箱故障诊断模型：1) Build the fault diagnosis model of the pitch gearbox: 当所述第i个变桨电机的电流偏差和第i个变桨电机的温度偏差满足：
Figure FDA0002386678460000062
可判断第i个变桨齿轮箱故障；When the current deviation of the i-th pitch motor and the temperature deviation of the i-th pitch motor satisfy:
Figure FDA0002386678460000062
The fault of the i-th pitch gearbox can be judged; 若同时第i个变桨齿轮油脂泵上限偏差与下限偏差还满足：
Figure FDA0002386678460000063
或
Figure FDA0002386678460000064
Figure FDA0002386678460000065
可进一步判断出：第i个变桨齿轮箱故障；和/或At the same time, if the upper limit deviation and lower limit deviation of the i-th pitch gear grease pump also satisfy:
Figure FDA0002386678460000063
or
Figure FDA0002386678460000064
Figure FDA0002386678460000065
It can be further determined that: the i-th pitch gearbox is faulty; and/or 2)构建变桨轴承故障诊断模型：2) Build the pitch bearing fault diagnosis model: 当下式成立：The present formula is established: 第一至第三变桨电机的电流偏差满足
Figure FDA0002386678460000066
The current deviation of the first to third pitch motors satisfies
Figure FDA0002386678460000066
 且所述第一至第三变桨电机的温度偏差满足
Figure FDA0002386678460000067
And the temperature deviation of the first to third pitch motors satisfies
Figure FDA0002386678460000067
 且所第一至第三个变桨轴油脂泵上限偏差与下限偏差满足条件：
Figure FDA0002386678460000068
或
Figure FDA0002386678460000069
即可判断变桨轴承出现故障。And the upper limit deviation and lower limit deviation of the first to third pitch shaft grease pumps meet the conditions:
Figure FDA0002386678460000068
or
Figure FDA0002386678460000069
It can be judged that the pitch bearing is faulty. 6.根据权利要求5所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤3中，6. The fault diagnosis method for a wind turbine pitch system according to claim 5, characterized in that: in step 3, 所述构建故障诊断模型的方法还包括构建变桨系统的控制系统故障诊断模型：The method for constructing a fault diagnosis model further includes constructing a control system fault diagnosis model of the pitch system: 1)构建变桨角度差异故障诊断模型：1) Build the pitch angle difference fault diagnosis model: 当桨距角相对偏差和变桨角度偏差Δβbi满足下式：When the relative pitch angle deviation and pitch angle deviation Δβ bi satisfy the following formula:
Figure FDA0002386678460000071
Figure FDA0002386678460000071
 且
Figure FDA0002386678460000072
则判断第i个桨叶的B编码器故障；and
Figure FDA0002386678460000072
Then it is judged that the B encoder of the i-th blade is faulty; 当桨距角相对偏差和桨距角绝对偏差Δβbi满足下式：When the relative pitch angle deviation and the pitch angle absolute deviation Δβ bi satisfy the following formula:
Figure FDA0002386678460000073
Figure FDA0002386678460000073
 且
Figure FDA0002386678460000074
则判断第i个桨叶的A编码器故障，其中，a为变桨角度许用误差；和/或and
Figure FDA0002386678460000074
Then it is judged that the A encoder of the i-th blade is faulty, where a is the allowable pitch angle error; and/or 2)构建桨叶限位开关触发偏早故障诊断模型：2) Build a fault diagnosis model for the early failure of the blade limit switch trigger: 如果桨距角相对偏差和桨叶角度满足下式：If the relative deviation of the pitch angle and the blade angle satisfy the following formula:
Figure FDA0002386678460000075
Figure FDA0002386678460000075
Figure FDA0002386678460000076
即可诊断故障原因为桨叶限位开关触发偏早；和/或
Figure FDA0002386678460000076
The cause of the fault can be diagnosed as premature activation of the blade limit switch; and/or 3)构建变桨电机高温故障诊断模型：3) Build the high temperature fault diagnosis model of the pitch motor: 如果：变桨电机温度偏差δtmi≥10.0％，可判断出第i桨叶出现了变桨电机高温故障；If: the pitch motor temperature deviation δt mi ≥ 10.0%, it can be judged that the pitch motor high temperature fault occurs on the i-th blade; 如果变桨电机温度偏差与变桨电机电流偏差满足：
Figure FDA0002386678460000077
可判断出第i桨叶出现了变桨电机高温故障，且该故障多由变桨齿轮箱故障引起；If the pitch motor temperature deviation and the pitch motor current deviation satisfy:
Figure FDA0002386678460000077
It can be judged that there is a high temperature fault of the pitch motor on the i-th blade, and the fault is mostly caused by the fault of the pitch gearbox; 4)构建变桨电机转速过高故障诊断模型：4) Build a fault diagnosis model for the high speed of the pitch motor: 如果以下两式成立：If the following two equations hold: 所述变桨电机的转速偏差满足
Figure FDA0002386678460000078
The speed deviation of the pitch motor satisfies the
Figure FDA0002386678460000078
 且变桨电机转速满足
Figure FDA0002386678460000079
And the speed of the pitch motor satisfies
Figure FDA0002386678460000079
 可判断出现了变桨电机转速过高故障，且此故障多由A编码器故障引起，其中，a为风力发电机许用转速偏差；和/或It can be judged that there is a fault that the speed of the pitch motor is too high, and this fault is mostly caused by the fault of the A encoder, where a is the allowable speed deviation of the wind turbine; and/or 5)构建变桨失效故障诊断模型：5) Build a fault diagnosis model for pitch failure: 当下式成立：The present formula is established: A编码器测得的桨距角相对偏差满足
Figure FDA00023866784600000710
The relative deviation of the pitch angle measured by the A encoder satisfies the
Figure FDA00023866784600000710
 且B编码器测得的桨距角相对偏差满足
Figure FDA0002386678460000081
And the relative deviation of pitch angle measured by B encoder satisfies
Figure FDA0002386678460000081
 且第一至第三桨叶的桨距角理论偏差满足
Figure FDA0002386678460000082
And the theoretical deviation of the pitch angle of the first to third blades satisfies
Figure FDA0002386678460000082
 则可判断出现了变桨失效故障；和/或then it can be judged that a pitch failure has occurred; and/or 6)构建变桨系统信号传输故障诊断模型：6) Build a fault diagnosis model for the signal transmission of the pitch system: 当下式成立：The present formula is established: 低速轴转速相对偏差满足δnls≥10.0％且风轮转速相对波动值满足δΔnms≥10.0％，则可判断出现了变桨控制信号传输故障。If the relative deviation of the low-speed shaft speed satisfies δn ls ≥ 10.0% and the relative fluctuation value of the rotor speed satisfies δΔn ms ≥ 10.0%, it can be judged that there is a pitch control signal transmission failure. 7.根据权利要求6所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤5中，当所述实时故障特征输入所述故障诊断模型时，首先将所述实时故障特征输入所述变桨电源系统故障诊断模型和所述变桨系统机械部件故障诊断模型，判断该实时故障特征的故障类型；若判断该实时故障特征不属于所述两类故障模型，再将所述实时故障特征输入所述变桨系统的控制系统故障诊断模型进行诊断。7. The fault diagnosis method for a wind turbine pitch system according to claim 6, characterized in that: in step 5, when the real-time fault characteristics are input into the fault diagnosis model, the real-time fault characteristics are first input into the The fault diagnosis model of the pitch power supply system and the fault diagnosis model of the mechanical parts of the pitch system are used to judge the fault type of the real-time fault feature; if it is judged that the real-time fault feature does not belong to the two types of fault models, the real-time fault feature Input the control system fault diagnosis model of the pitch system for diagnosis. 8.根据权利要求7所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤5中，所述故障诊断结果包括故障原因与故障位置。8 . The fault diagnosis method for a pitch system of a wind turbine according to claim 7 , wherein in step 5, the fault diagnosis result includes a fault cause and a fault location. 9 . 9.根据权利要求8所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤2中，所述历史运行数据先经过前置处理再进行故障特征提取；所述前置处理包括对历史运行数据的电信号进行除噪、滤波、平移和放大，得到幅值范围在0-5V内的标准运行数据。9 . The fault diagnosis method for a pitch system of a wind turbine according to claim 8 , wherein: in step 2, the historical operation data is subjected to preprocessing before fault feature extraction is performed; the preprocessing comprises The electrical signal of the operating data is de-noised, filtered, shifted and amplified to obtain standard operating data with an amplitude range of 0-5V. 10.根据权利要求9所述的风力发电机变桨系统故障诊断方法，其特征在于：步骤1-5中，采用数据采集器获取所有SCADA系统测量的风力发电机运行数据，并采用计算机存储并完成数据存储、故障诊断和结果显示。10. The fault diagnosis method for a wind turbine pitch system according to claim 9, wherein in steps 1-5, a data collector is used to obtain the wind turbine operation data measured by all SCADA systems, and a computer is used to store and complete the operation data of the wind turbine. Data storage, fault diagnosis and result display.
CN202010100372.7A 2020-02-18 2020-02-18 Fault Diagnosis Method for Wind Turbine Pitch System Active CN111287912B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010100372.7A CN111287912B (en) 2020-02-18 2020-02-18 Fault Diagnosis Method for Wind Turbine Pitch System

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010100372.7A CN111287912B (en) 2020-02-18 2020-02-18 Fault Diagnosis Method for Wind Turbine Pitch System

Publications (2)

Publication Number Publication Date
CN111287912A true CN111287912A (en) 2020-06-16
CN111287912B CN111287912B (en) 2021-06-08

Family

ID=71026784

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010100372.7A Active CN111287912B (en) 2020-02-18 2020-02-18 Fault Diagnosis Method for Wind Turbine Pitch System

Country Status (1)

Country Link
CN (1) CN111287912B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112594141A (en) * 2020-11-12 2021-04-02 北京金风慧能技术有限公司 Bearing fault monitoring method, system, device, controller and storage medium
CN113187674A (en) * 2021-06-22 2021-07-30 鲁能集团有限公司 Fault determination method and system for wind turbine generator pitch system
CN113447813A (en) * 2020-09-03 2021-09-28 鲁能集团有限公司 Fault diagnosis method and equipment for offshore wind generating set
CN114658612A (en) * 2020-12-23 2022-06-24 北京金风科创风电设备有限公司 Method and device for abnormal detection of pitch bearing of wind turbine
CN119262317A (en) * 2024-12-11 2025-01-07 四川沃飞长空科技发展有限公司 Fault identification method and related device for variable pitch propeller

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101871422A (en) * 2009-04-22 2010-10-27 湘潭大学 Low-voltage ride-through coordinated control method for wind turbines
CA2712158A1 (en) * 2009-07-24 2011-01-24 Raj Mohan Bharadwaj Wind turbine generator fault diagnostic and prognostic device and method
WO2012097819A1 (en) * 2011-01-20 2012-07-26 Vestas Wind Systems A/S A method for diagnostic monitoring of a wind turbine generator system
CN102889172A (en) * 2012-10-16 2013-01-23 国电联合动力技术有限公司 System and method for sequentially recording faults of wind generating set
CN104329222A (en) * 2014-10-09 2015-02-04 国电南瑞科技股份有限公司 On-line state monitoring and fault diagnosis method integrated into master control system for wind turbines
CN105134510A (en) * 2015-09-18 2015-12-09 北京中恒博瑞数字电力科技有限公司 State monitoring and failure diagnosis method for wind generating set variable pitch system
CN105760617A (en) * 2016-03-07 2016-07-13 华北电力大学（保定） Calculation method applied to multi-parameter fault prediction and judgment indexes of wind generating set
CN107609574A (en) * 2017-08-18 2018-01-19 上海电力学院 Wind turbines fault early warning method based on data mining
CN107728059A (en) * 2017-10-20 2018-02-23 郭莹莹 A kind of pitch-controlled system state evaluating method
CN107797063A (en) * 2017-09-19 2018-03-13 北京瑞盈同创智能技术研究院有限公司 Running of wind generating set state estimation and method for diagnosing faults based on SCADA
CN108953071A (en) * 2017-05-25 2018-12-07 中国电力科学研究院 A kind of fault early warning method and system of paddle change system of wind turbines
CN109543210A (en) * 2018-09-28 2019-03-29 国电电力宁夏新能源开发有限公司 A kind of Wind turbines failure prediction system based on machine learning algorithm platform

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101871422A (en) * 2009-04-22 2010-10-27 湘潭大学 Low-voltage ride-through coordinated control method for wind turbines
CA2712158A1 (en) * 2009-07-24 2011-01-24 Raj Mohan Bharadwaj Wind turbine generator fault diagnostic and prognostic device and method
WO2012097819A1 (en) * 2011-01-20 2012-07-26 Vestas Wind Systems A/S A method for diagnostic monitoring of a wind turbine generator system
CN102889172A (en) * 2012-10-16 2013-01-23 国电联合动力技术有限公司 System and method for sequentially recording faults of wind generating set
CN104329222A (en) * 2014-10-09 2015-02-04 国电南瑞科技股份有限公司 On-line state monitoring and fault diagnosis method integrated into master control system for wind turbines
CN105134510A (en) * 2015-09-18 2015-12-09 北京中恒博瑞数字电力科技有限公司 State monitoring and failure diagnosis method for wind generating set variable pitch system
CN105760617A (en) * 2016-03-07 2016-07-13 华北电力大学（保定） Calculation method applied to multi-parameter fault prediction and judgment indexes of wind generating set
CN108953071A (en) * 2017-05-25 2018-12-07 中国电力科学研究院 A kind of fault early warning method and system of paddle change system of wind turbines
CN107609574A (en) * 2017-08-18 2018-01-19 上海电力学院 Wind turbines fault early warning method based on data mining
CN107797063A (en) * 2017-09-19 2018-03-13 北京瑞盈同创智能技术研究院有限公司 Running of wind generating set state estimation and method for diagnosing faults based on SCADA
CN107728059A (en) * 2017-10-20 2018-02-23 郭莹莹 A kind of pitch-controlled system state evaluating method
CN109543210A (en) * 2018-09-28 2019-03-29 国电电力宁夏新能源开发有限公司 A kind of Wind turbines failure prediction system based on machine learning algorithm platform

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113447813A (en) * 2020-09-03 2021-09-28 鲁能集团有限公司 Fault diagnosis method and equipment for offshore wind generating set
CN113447813B (en) * 2020-09-03 2022-09-13 中国绿发投资集团有限公司 Fault diagnosis method and equipment for offshore wind generating set
CN112594141A (en) * 2020-11-12 2021-04-02 北京金风慧能技术有限公司 Bearing fault monitoring method, system, device, controller and storage medium
CN114658612A (en) * 2020-12-23 2022-06-24 北京金风科创风电设备有限公司 Method and device for abnormal detection of pitch bearing of wind turbine
CN113187674A (en) * 2021-06-22 2021-07-30 鲁能集团有限公司 Fault determination method and system for wind turbine generator pitch system
CN119262317A (en) * 2024-12-11 2025-01-07 四川沃飞长空科技发展有限公司 Fault identification method and related device for variable pitch propeller
CN119262317B (en) * 2024-12-11 2025-03-28 四川沃飞长空科技发展有限公司 Fault identification method and related device for variable pitch propeller

Also Published As

Publication number Publication date
CN111287912B (en) 2021-06-08

Similar Documents

Publication Publication Date Title
CN111287912B (en) Fault Diagnosis Method for Wind Turbine Pitch System
CN104019000B (en) The loading spectrum of wind power generating set is determined and perspective maintenance system
JP6503419B2 (en) Data collection system for wind power generation facility, data collection method, and wind power generation facility
CN103717887B (en) The closing method of the wind turbine in wind energy turbine set
CN204099123U (en) Blade of wind-driven generator vibration monitoring and protective gear
CN104515677A (en) Failure diagnosing and condition monitoring system for blades of wind generating sets
EP2299346A1 (en) Systems, methods, and apparatus for monitoring and controlling a wind driven machine
CN103234753A (en) Gaussian process modeling based wind turbine shafting state monitoring method
CN101839806B (en) Wind generating set and fatigue load monitoring system thereof
CN110067708B (en) Method for identifying yaw wind disharmony by using power curve
CN103472406A (en) Wind turbine generator unit pitch system standby power supply online automatic detection method and device
CN118361361B (en) Wind driven generator running state monitoring system
CN109958577B (en) Systems and methods for protecting wind turbines during wind gusts
CN201448194U (en) A fan vibration monitoring device based on information fusion technology
CN205744278U (en) Large-scale Double-wind wheel wind power generator group
CN211174438U (en) A diagnostic device for hydroelectric power generating units
CN111241708B (en) Calculation method and type selection method of supercapacitor for large wind turbine pitch system
CN102778654A (en) A detection system and detection method for a pitch storage battery of a wind power generating set
CN117231435B (en) Wind turbine operation and maintenance debugging system based on dynamic control
EP4040626A1 (en) Systems and methods for operating a power generating asset
CN217384748U (en) Fault trend diagnosis system for wind power plant group
EP4056843A1 (en) System and method for detecting actual slip in a coupling of a rotary shaft
CN104989602B (en) A kind of wind-driven generator status remote monitoring device
Wilson et al. Aspects of the dynamic response of a small wind turbine blade in highly turbulent flow: part 1 measured blade response
CN211008956U (en) Yaw control system of wind generating set

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant
CP03 Change of name, title or address

Address after: 510663 No.9, Keke Road, Huangpu District, Guangzhou City, Guangdong Province

Patentee after: Guangzhou Special Equipment Testing and Research Institute (Guangzhou Special Equipment Accident Investigation Technology Center Guangzhou Elevator Safety Operation Monitoring Center)

Country or region after: China

Address before: 510663 No.9, Keke Road, Huangpu District, Guangzhou City, Guangdong Province

Patentee before: GUANGZHOU SPECIAL PRESSURE EQUIPMENT INSPECTION AND Research Institute

Country or region before: China

CP03 Change of name, title or address