CN113971344A - Wind power enterprise fault screening method, system, equipment and medium - Google Patents
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Abstract
The invention discloses a method, a system, equipment and a medium for screening faults of a wind power enterprise, wherein the method comprises the following steps: analyzing original fault code information of all wind turbine generators to obtain real-time fault information of all the wind turbine generators; counting the failure times of the fan components according to the category of the wind power plant and the category of the brand of the fan, obtaining the failure times of the fan components in each wind power plant and the failure times of the fan components in each brand of the fan in a preset counting time period, and calculating the failure rate of the fan sets in each wind power plant and the failure rate of the fan sets in each brand of the fan; acquiring a wind power field, a fan brand and fan components influencing the wind power enterprise fault by using a big data analysis method to obtain a wind power enterprise fault screening result; the method can not only master the detailed fault conditions of all parts of the fan in real time, but also gradually find out the root causes influencing the high fault rate of the wind power enterprise, and provide technical support for the wind power enterprise to make a fault management plan.
Description
Technical Field
The invention belongs to the technical field of wind power, and particularly relates to a method, a system, equipment and a medium for screening faults of a wind power enterprise.
Background
Wind energy is a renewable clean energy source, and particularly under the background of global energy crisis and environmental protection, wind power generation is greatly concerned; at present, when each wind power enterprise carries out fault analysis and evaluation, the original fault information of the wind turbine generator is mostly monitored manually, and then each type of fault information is counted; however, wind power plants and wind turbine generators belonging to wind power enterprises are large in number and numerous in fan brands, a large amount of manpower and time are consumed through a manual monitoring method, and the fault conditions of the wind power plants and the wind turbine generators of the brands cannot be analyzed in real time.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a method, a system, equipment and a medium for screening faults of a wind power enterprise, and aims to solve the technical problems that a large amount of manpower and time are consumed and analysis and evaluation results cannot be obtained in real time in the existing fault analysis and evaluation process of the wind power enterprise by adopting manual monitoring.
In order to achieve the purpose, the invention adopts the technical scheme that:
the invention provides a wind power enterprise fault screening method which comprises the following steps:
acquiring original fault code information of all wind turbine generators in a wind turbine enterprise to be evaluated;
analyzing original fault code information of all wind turbine generators to obtain real-time fault information of all the wind turbine generators;
according to the real-time fault information of all wind turbines, counting the fault times of the fan components according to the wind power plant type and the fan brand type to obtain the fault times of the fan components in each wind power plant and the fault times of the fan components in each fan brand in a preset counting time period;
calculating the failure rate of the wind turbine generator set in each wind power plant and the failure rate of the wind turbine generator set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand;
and acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
Further, the original fault code information of the wind turbine generator is collected through a design specification of the wind turbine generator.
Further, the process of analyzing the original fault code information of all the wind turbine generators to obtain the real-time fault information of all the wind turbine generators is as follows:
classifying the acquired original fault codes of all the wind turbines according to the types of the fan components to obtain original fault codes of the fan components in different types;
analyzing original fault codes of fan components of different types according to a preset analysis rule by combining the fault real-time signal point type of the wind turbine generator to obtain real-time fault information of all the wind turbine generators.
Further, the original fault codes of the different types of fan components comprise original fault codes of a remote control system, original fault codes of a communication system, original fault codes of a wind wheel, original fault codes of a variable pitch regulation system, original fault codes of a transmission speed change system, original fault codes of a generator system, original fault codes of a hydraulic system, original fault codes of a yaw system, original fault codes of a cabin and a tower, original fault codes of an electrical control system, original fault codes of a frequency conversion system and original fault codes of a power supply system.
Further, the method analyzes original fault codes of different types of fan components according to a preset analysis rule by combining fault real-time signal point types of the wind turbine generator, and obtains real-time fault information of all the wind turbine generators, specifically as follows:
if the fault real-time signal points of the wind turbine generator are analog measuring points and the number of the analog measuring points of the fans of different brands is different, automatically matching original fault codes of fan components according to real-time data of the analog measuring points of the wind turbine generator, and obtaining real-time fault information of the wind turbine generator according to the matched original information codes of the fan components;
if the fault real-time signal point of the wind turbine generator is a switch measuring point, the switch measuring points of the wind turbine generator correspond to original fault codes of fan components one by one, and the switch measuring values of the switch measuring points of the wind turbine generator are read in sequence to obtain real-time fault information of the wind turbine generator;
if the fault real-time signal point of the wind turbine generator is an analog measurement point, and the analog measurement signal needs to be analyzed according to bits, binary conversion is carried out on the real-time data of the analog measurement point of the wind turbine generator according to different types of wind power plants and different quantities of the analog measurement points, and fault information is obtained from original fault codes of fan components according to the converted binary data, so that the real-time fault information of the wind turbine generator is obtained.
Further, according to the failure frequency of the fan components in each wind power plant and the failure frequency of each fan component in each fan brand, calculating the failure rate of the fan sets in each wind power plant and the failure rate of the fan sets in each fan brand according to a failure rate formula; the fault rate formula is specifically as follows:
further, according to the fault rate of the wind turbine generator in each wind power plant and the fault rate of the wind turbine generator in each fan brand, a big data analysis method is used for obtaining the wind power plant, the fan brand and the fan components which affect the fault of the wind power enterprise, and the process of obtaining the fault screening result of the wind power enterprise is specifically as follows:
and carrying out big data operation on the fault rate of the wind turbine generator in each wind power plant and the fault rate of the wind turbine generator in each fan brand, sequentially screening and analyzing step by step according to the wind power plant, the fan brand and the fan parts, sequencing the wind turbine generators and the fan parts which influence the occurrence of the faults of the sub-electric field and the fan brand, acquiring the wind turbine generators and the fan parts which are sequenced in front, obtaining the wind power plant, the fan brand and the fan parts which influence the faults of the wind power enterprise, and obtaining the fault screening result of the wind power enterprise.
The invention also provides a wind power enterprise fault screening system, which comprises:
the system comprises an information acquisition module, a fault analysis module and a fault analysis module, wherein the information acquisition module is used for acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated;
the analysis module is used for analyzing the original fault code information of all the wind turbine generators to obtain the real-time fault information of all the wind turbine generators;
the counting module is used for counting the failure times of the fan components according to the type of the wind power plant and the type of the brand of the fan according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of each fan component in each brand of the fan in a preset counting time period;
the failure rate calculation module is used for calculating the failure rate of the wind turbine set in each wind power plant and the failure rate of the wind turbine set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand;
and the big data analysis module is used for acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
The invention also provides a wind power enterprise fault screening device, which comprises:
a memory for storing a computer program;
and the processor is used for realizing the steps of the wind power enterprise fault screening method when the computer program is executed.
The invention also provides a computer-readable storage medium, which is characterized in that the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the wind power enterprise fault screening method.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a wind power enterprise fault screening method, which is characterized in that fault information of each component of a fan is monitored in real time, and fault comparison analysis is carried out on each wind power plant and each brand of fan by combining the fault rate of the fan, so that the fault conditions of each component of the fan can be mastered in real time, the root cause and common problem of equipment fault can be integrally found, and the utilization rate of equipment of a wind power enterprise is improved; the method can be used for mastering the detailed fault conditions of all parts of the fan in real time, and can be used for finding out the root cause influencing the high fault rate of the wind power enterprise step by step, so that technical support is provided for the wind power enterprise to make a fault management plan.
Drawings
FIG. 1 is a flow chart of a wind power enterprise fault screening method according to the present invention;
FIG. 2 is a block diagram of a wind power enterprise fault evaluation system according to the present invention;
FIG. 3 is a block diagram of a wind power enterprise fault evaluation apparatus according to the present invention;
FIG. 4 is a table of real-time fault information for wind turbines and failure occurrence times of wind turbine components in a wind farm in an embodiment;
FIG. 5 shows wind turbine generators and wind turbine components with higher failure rate ranking and failure frequency of each wind farm within a preset statistical time period in the embodiment;
fig. 6 is a diagram illustrating a result of fault screening for a wind power enterprise according to an embodiment.
Detailed Description
In order to make the technical problems, technical solutions and advantageous effects of the present invention more apparent, the following embodiments further describe the present invention in detail. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in the attached figure 1, the invention provides a wind power enterprise fault screening method, which comprises the following steps:
step 1, acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated; the original fault code information of the wind turbine generator is collected through a design specification of the wind turbine generator.
And 2, analyzing the original fault code information of all the wind turbines to obtain real-time fault information of all the wind turbines.
The analysis process is as follows:
step 21, classifying the obtained original fault codes of all the wind turbines according to the types of the fan components to obtain original fault codes of the fan components in different types; the original fault codes of the different types of fan components comprise original fault codes of a remote control system, original fault codes of a communication system, original fault codes of a wind wheel, original fault codes of a variable pitch regulation system, original fault codes of a transmission speed change system, original fault codes of a generator system, original fault codes of a hydraulic system, original fault codes of a yaw system, original fault codes of a cabin and a tower, original fault codes of an electrical control system, original fault codes of a frequency conversion system and original fault codes of a power supply system.
Step 22, analyzing original fault codes of fan components of different types according to a preset analysis rule by combining fault real-time signal point types of the wind turbine generator to obtain real-time fault information of all the wind turbine generators; specifically, the method comprises the following steps:
if the fault real-time signal point of the wind turbine generator is an analog measurement point and the analog measurement points of the fans of different brands are different in number, automatically matching original fault codes of fan components according to real-time data of the analog measurement point of the wind turbine generator, and obtaining real-time fault information of the wind turbine generator according to the matched original information codes of the fan components.
And if the fault real-time signal point of the wind turbine generator is a switch measuring point, the switch measuring points of the wind turbine generator correspond to original fault codes of fan components one by one, and the switch measuring values of the switch measuring points of the wind turbine generator are read in sequence to obtain real-time fault information of the wind turbine generator.
If the fault real-time signal point of the wind turbine generator is an analog measurement point, and the analog measurement signal needs to be analyzed according to bits, binary conversion is carried out on the real-time data of the analog measurement point of the wind turbine generator according to different types of wind power plants and different quantities of the analog measurement points, and fault information is obtained from original fault codes of fan components according to the converted binary data, so that the real-time fault information of the wind turbine generator is obtained.
And 3, counting the failure times of the fan components according to the type of the wind power plant and the type of the fan brand according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of the fan components in each fan brand in a preset counting time period.
And 4, calculating the failure rate of the wind turbine generator in each wind power plant and the failure rate of the wind turbine generator in each wind power plant brand according to the failure rate formula and the failure times of the wind turbine components in each wind power plant brand.
The fault rate formula is specifically as follows:
and 5, acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, and obtaining a wind power enterprise fault screening result.
The big data analysis process is as follows:
and carrying out big data operation on the fault rate of the wind turbine generator in each wind power plant and the fault rate of the wind turbine generator in each fan brand, sequentially screening and analyzing step by step according to the wind power plant, the fan brand and the fan parts, sequencing the wind turbine generators and the fan parts which influence the occurrence of the faults of the sub-electric field and the fan brand, acquiring the wind turbine generators and the fan parts which are sequenced in front, obtaining the wind power plant, the fan brand and the fan parts which influence the faults of the wind power enterprise, and obtaining the fault screening result of the wind power enterprise.
As shown in fig. 2, the invention further provides a wind power enterprise fault screening system, which comprises an information acquisition module, an analysis module, a statistical module, a fault rate calculation module and a big data analysis module.
The system comprises an information acquisition module, a fault analysis module and a fault analysis module, wherein the information acquisition module is used for acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated;
the analysis module is used for analyzing the original fault code information of all the wind turbine generators to obtain the real-time fault information of all the wind turbine generators;
the counting module is used for counting the failure times of the fan components according to the type of the wind power plant and the type of the brand of the fan according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of each fan component in each brand of the fan in a preset counting time period;
the failure rate calculation module is used for calculating the failure rate of the wind turbine set in each wind power plant and the failure rate of the wind turbine set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand;
and the big data analysis module is used for acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
As shown in fig. 3, the present invention further provides a wind power enterprise fault screening device, which includes: a memory for storing a computer program; the processor is used for realizing the steps of the wind power enterprise fault screening method when the computer program is executed; the communication interface shown in fig. 3 is used to access an external device to obtain data.
When the processor executes the computer program, the steps of the wind power enterprise fault screening method are realized, for example: acquiring original fault code information of all wind turbine generators in a wind turbine enterprise to be evaluated; analyzing original fault code information of all wind turbine generators to obtain real-time fault information of all the wind turbine generators; according to the real-time fault information of all wind turbines, counting the fault times of the fan components according to the wind power plant type and the fan brand type to obtain the fault times of the fan components in each wind power plant and the fault times of the fan components in each fan brand in a preset counting time period; calculating the failure rate of the wind turbine generator set in each wind power plant and the failure rate of the wind turbine generator set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand; and acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
Alternatively, the processor implements the functions of the modules in the system when executing the computer program, for example: the system comprises an information acquisition module, a fault analysis module and a fault analysis module, wherein the information acquisition module is used for acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated; the analysis module is used for analyzing the original fault code information of all the wind turbine generators to obtain the real-time fault information of all the wind turbine generators; the counting module is used for counting the failure times of the fan components according to the type of the wind power plant and the type of the brand of the fan according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of each fan component in each brand of the fan in a preset counting time period; the failure rate calculation module is used for calculating the failure rate of the wind turbine set in each wind power plant and the failure rate of the wind turbine set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand; and the big data analysis module is used for acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used for describing the execution process of the computer program in the wind power enterprise fault screening device. For example, the computer program may be divided into an information acquisition module, an analysis module, a statistics module, a failure rate calculation module, and a big data analysis module, and the specific functions of each module are as follows: the system comprises an information acquisition module, a fault analysis module and a fault analysis module, wherein the information acquisition module is used for acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated; the analysis module is used for analyzing the original fault code information of all the wind turbine generators to obtain the real-time fault information of all the wind turbine generators; the counting module is used for counting the failure times of the fan components according to the type of the wind power plant and the type of the brand of the fan according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of each fan component in each brand of the fan in a preset counting time period; the failure rate calculation module is used for calculating the failure rate of the wind turbine set in each wind power plant and the failure rate of the wind turbine set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand; and the big data analysis module is used for acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
The wind power enterprise fault screening device can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The wind power enterprise fault screening equipment can comprise, but is not limited to, a processor and a memory. Those skilled in the art will understand that fig. 3 is only an example of a wind power enterprise fault screening device, and does not constitute a limitation to the wind power enterprise fault screening device, and may include more components than those shown in the drawings, or combine some components, or different components, for example, the wind power enterprise fault screening device may further include an input and output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the wind power enterprise fault screening equipment, and various interfaces and lines are utilized to connect all parts of the whole wind power enterprise fault screening equipment.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the wind power enterprise fault screening equipment by running or executing the computer program and/or the module stored in the memory and calling data stored in the memory.
The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), at least one disk storage device, a flash memory device, or other volatile solid state storage device.
The invention also provides a computer readable storage medium, which stores a computer program, and the computer program realizes the steps of the wind power enterprise fault screening method when being executed by a processor.
If the integrated module/unit of the wind power enterprise fault screening system is realized in the form of a software functional unit and is sold or used as an independent product, the integrated module/unit can be stored in a computer readable storage medium.
Based on such understanding, all or part of the processes in the wind power enterprise fault screening method can be realized by the present invention, and can also be completed by instructing related hardware through a computer program, where the computer program can be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the wind power enterprise fault screening method can be realized. Wherein the computer program comprises computer program code, which may be in source code form, object code form, executable file or preset intermediate form, etc.
The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, Read-only memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc.
It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
Examples
Taking a certain wind power enterprise as an example, the embodiment provides a wind power enterprise fault screening method, which includes the following steps:
step 1, acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated; the original fault code information of the wind turbine generator is collected through a design specification of the wind turbine generator.
And 2, analyzing the original fault code information of all the wind turbines to obtain real-time fault information of all the wind turbines.
The analysis process is as follows:
step 21, classifying the obtained original fault codes of all the wind turbines according to the types of the fan components to obtain original fault codes of the fan components in different types; the original fault codes of the different types of fan components comprise original fault codes of a remote control system, original fault codes of a communication system, original fault codes of a wind wheel, original fault codes of a variable pitch regulation system, original fault codes of a transmission speed change system, original fault codes of a generator system, original fault codes of a hydraulic system, original fault codes of a yaw system, original fault codes of a cabin and a tower, original fault codes of an electrical control system, original fault codes of a frequency conversion system and original fault codes of a power supply system.
Step 22, analyzing original fault codes of fan components of different types according to a preset analysis rule by combining fault real-time signal point types of the wind turbine generator to obtain real-time fault information of all the wind turbine generators; specifically, the method comprises the following steps:
if the fault real-time signal points of the wind turbine generator are analog measuring points and the number of the analog measuring points of the fans of different brands is different, automatically matching original fault codes of fan components according to real-time data of the analog measuring points of the wind turbine generator, and obtaining real-time fault information of the wind turbine generator according to the matched original information codes of the fan components; for example: when the analog measurement points of a certain wind turbine generator set are acquired as 8 measurement points, automatically matching which fault belongs to according to the real-time data of each analog measurement point; if the real-time data of the first analog measurement point is 103001, the fault code corresponding to the real-time data 103001 is found from the original fault codes of the fan component, and the fault code is analyzed, that is, the fault information displayed by the first analog measurement point.
If the fault real-time signal point of the wind turbine generator is a switch measuring point, the switch measuring points of the wind turbine generator correspond to original fault codes of fan components one by one, and the switch measuring values of the switch measuring points of the wind turbine generator are read in sequence to obtain real-time fault information of the wind turbine generator; for example, 500 switch measurement points of a certain wind turbine generator set are 500 corresponding original fault codes, and when the switch value of a certain switch measurement point is 1 in sequence, the corresponding fault occurs; when the switching value at a certain switching value point is 0, it means that the corresponding failure does not occur.
If the fault real-time signal point of the wind turbine generator is an analog measurement point, and the analog measurement signal needs to be analyzed according to bits, binary conversion is carried out on the real-time data of the analog measurement point of the wind turbine generator according to different types of wind power plants and different quantities of the analog measurement points, and fault information is obtained from original fault codes of fan components according to the converted binary data, so that the real-time fault information of the wind turbine generator is obtained; for example, if a certain wind turbine generator set has 10 analog quantity data measuring points, and the real-time data of the first analog quantity data measuring point is 23, binary conversion is performed on the real-time data 23 to obtain 00000000000000000000000000010111, which represents 1 st, 2 nd, 3 rd and 5 th fault triggers, and then fault information of 1 st, 2 nd, 3 rd and 5 th faults are automatically analyzed from original fault codes of the wind turbine components, wherein the fault information is respectively all normal, normal error repetition, grid error repetition and long yaw pressure release time.
And 3, counting the failure times of the fan components according to the type of the wind power plant and the type of the fan brand according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of the fan components in each fan brand in a preset counting time period.
And 4, calculating the failure rate of the wind turbine generator in each wind power plant and the failure rate of the wind turbine generator in each wind power plant brand according to the failure rate formula and the failure times of the wind turbine components in each wind power plant brand.
The fault rate formula is specifically as follows:
and 5, acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, and obtaining a wind power enterprise fault screening result.
The big data analysis process is as follows:
and carrying out big data operation on the fault rate of the wind turbine generator in each wind power plant and the fault rate of the wind turbine generator in each fan brand, sequentially screening and analyzing step by step according to the wind power plant, the fan brand and the fan parts, sequencing the wind turbine generators and the fan parts which influence the occurrence of the faults of the sub-electric field and the fan brand, acquiring the wind turbine generators and the fan parts which are sequenced in front, obtaining the wind power plant, the fan brand and the fan parts which influence the faults of the wind power enterprise, and obtaining the fault screening result of the wind power enterprise.
Fig. 4 is a table diagram showing real-time fault information of a wind turbine in a certain wind farm of the wind power enterprise and fault occurrence times of 12 fan components, and fig. 5 is a table diagram showing a wind turbine and a fan component with a large fault rate ranking and fault occurrence times of each wind farm in a preset statistical time period; as can be seen from the attached FIGS. 4-5, the landscape wind farm to which the wind power enterprise belongs has the highest failure rate, and the landscape wind farm is mainly due to the fact that the 69# wind turbine generator and the 70# wind turbine generator have the largest failure frequency; the parts with high faults and detailed fault information influencing the 69# wind turbine generator and the 70# wind turbine generator can be found out one by one, as shown in the attached figure 6; as can be seen from the attached figure 6, high-frequency faults can be comprehensively and pertinently processed, and the utilization rate of equipment of a wind power enterprise is improved.
For a description of a relevant part in the wind power enterprise fault screening system, the device and the computer-readable storage medium provided in this embodiment, reference may be made to a detailed description of a corresponding part in the wind power enterprise fault screening method described in this embodiment, which is not described herein again.
According to the method, the system, the equipment and the medium for screening the faults of the wind power enterprise, the wind power plants and the fan brands of the wind power enterprise are subjected to statistical analysis by utilizing the original fault codes of the fans and combining the fault rate of the fans, so that the wind power plants, the brands of fans and high-frequency fault fan parts with serious faults of the wind power enterprise are gradually found out.
The above-described embodiment is only one of the embodiments that can implement the technical solution of the present invention, and the scope of the present invention is not limited by the embodiment, but includes any variations, substitutions and other embodiments that can be easily conceived by those skilled in the art within the technical scope of the present invention disclosed.
Claims (10)
1. A wind power enterprise fault screening method is characterized by comprising the following steps:
acquiring original fault code information of all wind turbine generators in a wind turbine enterprise to be evaluated;
analyzing original fault code information of all wind turbine generators to obtain real-time fault information of all the wind turbine generators;
according to the real-time fault information of all wind turbines, counting the fault times of the fan components according to the wind power plant type and the fan brand type to obtain the fault times of the fan components in each wind power plant and the fault times of the fan components in each fan brand in a preset counting time period;
calculating the failure rate of the wind turbine generator set in each wind power plant and the failure rate of the wind turbine generator set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand;
and acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
2. The wind power enterprise fault screening method according to claim 1, wherein the original fault code information of the wind turbine generator is collected through a design specification of the wind turbine generator.
3. The method for screening the faults of the wind power enterprises according to claim 1, wherein the process of analyzing the original fault code information of all the wind power generation sets to obtain the real-time fault information of all the wind power generation sets comprises the following steps:
classifying the acquired original fault codes of all the wind turbines according to the types of the fan components to obtain original fault codes of the fan components in different types;
analyzing original fault codes of fan components of different types according to a preset analysis rule by combining the fault real-time signal point type of the wind turbine generator to obtain real-time fault information of all the wind turbine generators.
4. The wind power enterprise fault screening method according to claim 3, wherein the different types of fan component original fault codes include a remote control system original fault code, a communication system original fault code, a wind wheel original fault code, a pitch control system original fault code, a transmission speed change system original fault code, a generator system original fault code, a hydraulic system original fault code, a yaw system original fault code, a nacelle and tower original fault code, an electrical control system original fault code, a frequency conversion system original fault code, and a power supply system original fault code.
5. The method for screening the faults of the wind power enterprises according to claim 3, wherein the method is a process of analyzing original fault codes of fan components of different types according to a preset analysis rule by combining fault real-time signal point types of the wind power generation sets to obtain real-time fault information of all the wind power generation sets, and specifically comprises the following steps:
if the fault real-time signal points of the wind turbine generator are analog measuring points and the number of the analog measuring points of the fans of different brands is different, automatically matching original fault codes of fan components according to real-time data of the analog measuring points of the wind turbine generator, and obtaining real-time fault information of the wind turbine generator according to the matched original information codes of the fan components;
if the fault real-time signal point of the wind turbine generator is a switch measuring point, the switch measuring points of the wind turbine generator correspond to original fault codes of fan components one by one, and the switch measuring values of the switch measuring points of the wind turbine generator are read in sequence to obtain real-time fault information of the wind turbine generator;
if the fault real-time signal point of the wind turbine generator is an analog measurement point, and the analog measurement signal needs to be analyzed according to bits, binary conversion is carried out on the real-time data of the analog measurement point of the wind turbine generator according to different types of wind power plants and different quantities of the analog measurement points, and fault information is obtained from original fault codes of fan components according to the converted binary data, so that the real-time fault information of the wind turbine generator is obtained.
6. The wind power enterprise fault screening method according to claim 1, characterized by calculating the fault rate of the wind turbine generator in each wind farm and the fault rate of the wind turbine generator in each wind farm according to a fault rate formula according to the fault number of the wind turbine generator in each wind farm and the fault number of each wind turbine generator in each wind farm brand; the fault rate formula is specifically as follows:
7. the method for screening the faults of the wind power enterprises according to claim 1, wherein the wind power plants, the fan brands and the fan components influencing the faults of the wind power enterprises are obtained by a big data analysis method according to the fault rate of the fan sets in each wind power plant and the fault rate of the fan sets in each fan brand, so that the process of screening the faults of the wind power enterprises is obtained, and the process is as follows:
and carrying out big data operation on the fault rate of the wind turbine generator in each wind power plant and the fault rate of the wind turbine generator in each fan brand, sequentially screening and analyzing step by step according to the wind power plant, the fan brand and the fan parts, sequencing the wind turbine generators and the fan parts which influence the occurrence of the faults of the sub-electric field and the fan brand, acquiring the wind turbine generators and the fan parts which are sequenced in front, obtaining the wind power plant, the fan brand and the fan parts which influence the faults of the wind power enterprise, and obtaining the fault screening result of the wind power enterprise.
8. The utility model provides a wind-powered electricity generation enterprise trouble screening system which characterized in that includes:
the system comprises an information acquisition module, a fault analysis module and a fault analysis module, wherein the information acquisition module is used for acquiring original fault code information of all wind turbine generators in a wind power enterprise to be evaluated;
the analysis module is used for analyzing the original fault code information of all the wind turbine generators to obtain the real-time fault information of all the wind turbine generators;
the counting module is used for counting the failure times of the fan components according to the type of the wind power plant and the type of the brand of the fan according to the real-time failure information of all the wind generation sets to obtain the failure times of the fan components in each wind power plant and the failure times of each fan component in each brand of the fan in a preset counting time period;
the failure rate calculation module is used for calculating the failure rate of the wind turbine set in each wind power plant and the failure rate of the wind turbine set in each wind power plant brand according to the failure times of the wind turbine components in each wind power plant and the failure times of each wind turbine component in each wind power plant brand;
and the big data analysis module is used for acquiring the wind power plant, the fan brand and the fan component which influence the wind power enterprise fault by using a big data analysis method according to the fault rate of the fan set in each wind power plant and the fault rate of the fan set in each fan brand, so as to obtain a wind power enterprise fault screening result.
9. The utility model provides a wind-powered electricity generation enterprise trouble screening installation which characterized in that includes:
a memory for storing a computer program;
a processor for implementing the steps of the wind power enterprise fault screening method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the wind power enterprise fault screening method according to any one of claims 1-7.
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