CN113610250A - Method for online calculating number of wind field fault outages exceeding 24 hours based on defect list - Google Patents
Method for online calculating number of wind field fault outages exceeding 24 hours based on defect list Download PDFInfo
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- CN113610250A CN113610250A CN202110929155.3A CN202110929155A CN113610250A CN 113610250 A CN113610250 A CN 113610250A CN 202110929155 A CN202110929155 A CN 202110929155A CN 113610250 A CN113610250 A CN 113610250A
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- 230000007547 defect Effects 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 title claims abstract description 16
- 238000004519 manufacturing process Methods 0.000 claims abstract description 16
- 230000008030 elimination Effects 0.000 claims abstract description 4
- 238000003379 elimination reaction Methods 0.000 claims abstract description 4
- 238000003860 storage Methods 0.000 claims abstract description 4
- 238000001914 filtration Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 description 10
- 230000002354 daily effect Effects 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000003203 everyday effect Effects 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 230000002950 deficient Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- 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
Abstract
The invention discloses a method for calculating the number of wind field fault shutdown for more than 24 hours on line based on a defect list, which comprises the following steps: 1) collecting statistical day T from wind farm production management system0The current day is in an unclosed state and the creation time of the defect list is earlier than T0Defect list of the day, forming set A0(ii) a 2) From set A0The medium elimination state is a defect list of temporary storage, cancellation and invalidation states to form a set A; 3) collecting T from wind farm production management system0Close the day and create defect list time tcreateForm a set B, where tcreate24 hours prior to the off time; 4) merging the set A and the set B to form a defect list set C0(ii) a 5) From the set C0Removing the defect list with the reduced force eliminated to form a set C; 6) root of herbaceous plantInquiring in the defect set C according to the fan ID, if the same fan has a plurality of defect lists, only keeping 1 fan, and finally obtaining a defect set M; 7) the number n of defects in the set M is the statistic day T of the wind field0Is down for over 24 hours. The invention provides a scientific and rapid index acquisition method for production management departments.
Description
Technical Field
The invention belongs to the technical field of wind power operation and maintenance, and particularly relates to a method for calculating the number of wind field fault outages exceeding 24 hours on line based on a defect list.
Background
The number of the wind field fault shutdown exceeding 24 hours is the number of the fans which have started fault shutdown before the statistical day and have the fault shutdown duration longer than 24 hours at 24 points on the statistical day. The method is an important index for supervising and evaluating the long-stop condition of the wind field fan fault by a management department, is an important content for long-stop fan management, and is characterized in that a wind field operator manually judges by looking up wind field fan operation and maintenance historical data, but as the number of fans is large, the time span is large, the analysis workload is heavy, the operator can hardly make accurate judgment, the whole process can not be effectively supervised and controlled, so that the management department can not master the real long-stop information of the wind field fault, and the production decision is influenced. In addition, the index cannot be calculated based on real-time data of the fan, the fan fault state from the fan PLC only represents the state of the fan in the period of time of failure reporting and then stopping, and during the failure stopping period, the fan state may be switched among the states of failure, maintenance, standby, offline and the like, so that a judgment logic capable of effectively identifying the failure stopping starting time and the failure stopping recovery time cannot be found, and the application of the method is influenced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for calculating the number of wind field fault outages exceeding 24 hours on line based on a defect list, which can be used for calculating the number of wind field fault outages exceeding 24 hours on line by integrating parameters such as the state of the defect list, the ID of a defect fan, defect phenomena, defect creation time and the like, so as to provide a scientific and quick index acquisition method for production management departments and change the traditional manual judgment and filling mode.
In order to achieve the purpose, the invention adopts the following technical scheme:
the method for calculating the number of the wind field fault outages exceeding 24 hours on line based on the defect list comprises the following steps:
1) collecting statistical day T from wind farm production management system0The day is in an unclosed state and the creation time of the defect list is earlier than the statistic day T0Defect list of the day, forming set A0；
2) From set A0The medium elimination state is a defect list of temporary storage, cancellation and invalidation states to form a set A;
3) collecting statistical day T from wind farm production management system0Close the day and create defect list time tcreateForm a set B, where tcreateBefore the closing time tclose24 hours;
4) merging the set A and the set B to form a defect list set C0；
5) From the set C0Removing the defect list with the reduced force eliminated, namely filtering out the defect list with the reduced force as a defect phenomenon to form a set C;
6) inquiring in the defect set C according to the fan ID, if the same fan has a plurality of defect lists, only keeping 1 fan to ensure that each fan is counted once in a counting day, and finally obtaining a defect set M;
7) the number n of defects in the set M is the statistic day T of the wind field0Is down for over 24 hours.
Compared with the prior art, the invention has the following advantages:
the method can synthesize the state of the defect list, the ID of the defect fan, the defect phenomenon, the defect creating time and other parameters, calculate the number of the wind field fault stops for more than 24 hours on line, automatically provide the fault long-stop fan information of the subordinate wind field for the production management personnel of the enterprise at regular time, and point out a definite direction for promoting the purchase of spare parts and strengthening the overhaul supervision of the enterprise. The method has clear logic and scientific and accurate calculation, can flexibly calculate the number of the fans in over 24 hours, over 168 hours and over 720 hours according to the needs, can effectively promote the zero concept of the long-stop fan while releasing human resources, and promotes defect prevention control, sufficient guarantee of spare parts and timely elimination of defects. Meanwhile, under the large background of new energy resource base management, benchmarking between wind farms and benchmarking between time dimensions can be achieved, the influence of human factors in the calculation process is reduced, the operation and maintenance analysis and decision level of enterprises is improved, and scientific and standard efficient operation and maintenance is achieved.
Drawings
Fig. 1 shows the number of fault-down units (daily report) exceeding 24 hours.
Fig. 2 is a comparison (daily report) of indexes such as the number of the fans in a failure shutdown state for more than 24 hours.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
The method for calculating the number of the wind farm fault outages in excess of 24 hours on line based on the defect list is tested on a wind power intelligent operation and maintenance project, realizes automatic calculation on an intelligent operation and maintenance platform, and becomes an important means for a production management department to supervise the wind farm operation and maintenance and carry out wind farm performance assessment. Taking a certain wind power company headquarters as an example, the implementation mode of the method is introduced as follows:
1) collecting and arranging field information of a certain wind field defect list in the wind field production management system, and extracting fields of the state of the defect list, the ID of a defective fan, defect phenomena, defect creation time and the like.
2) And the statistical program collects the information of the defect list of the previous day (statistical day) on line in zero morning every day to form an original defect list table.
3) And screening the defect list which is in an unclosed state on the day of statistics and has the defect list creation time before the day of statistics from the original defect list table, and removing the defect list in the states of temporary storage, cancellation, invalidation and the like.
4) And screening the defect list which is closed on the day and has the defect list existence time longer than 24 hours from the original defect list table.
5) Combining the defect lists obtained in the steps 3) and 4) and removing the defect list with the eliminated reduced force, namely filtering the defect list with the defect phenomenon as the reduced force to form an effective defect list table.
6) And inquiring in an effective defect list table according to the ID of the fan, if the same fan has a plurality of defect lists, only keeping 1 fan to ensure that each fan is counted once on a counting day.
7) And finally obtaining the number of the defect lists, namely the number of the fault shutdown exceeding 24 hours on the day of the wind field statistics.
Fig. 1 shows the number of wind farms with fault shutdown exceeding 24 hours (daily report), wind farm data of the previous day is automatically counted and calculated in zero morning every day, and the wind farm data is displayed in the production daily report of a production management department, so that a user can flexibly inquire the number of fault shutdown exceeding 24 hours between subordinate wind farms on any date.
Fig. 2 is a comparison (daily report) of indexes such as the number of the fans in a fault shutdown state for more than 24 hours, and the benchmarking between wind fields and between different indexes is realized.
In fig. 1 and 2, a user can randomly select a date, inquire the number of the subordinate wind power plants in the fault shutdown time exceeding 24 hours, and compare and display the number of the subordinate wind power plants with indexes such as the number of fault-free shutdown machines, the number of regular maintenance machines, the number of planned shutdown machines, the number of output-reduced operation machines and the like, so that a basis is provided for the user to master the production general view of each wind power plant, and an objective and effective means is provided for a production management department to evaluate the operation and maintenance level of the wind power plant and carry out benchmarking.
Claims (1)
1. The method for calculating the number of the wind field fault outages exceeding 24 hours on line based on the defect list is characterized by comprising the following steps of: the method comprises the following steps:
1) collecting statistical day T from wind farm production management system0The day is in an unclosed state and the creation time of the defect list is earlier than the statistic day T0Defect list of the day, forming set A0；
2) From the original defect list set A0The medium elimination state is a defect list of temporary storage, cancellation and invalidation states to form a set A;
3) collecting statistical day T from wind farm production management system0Close the day and create defect list time tcreateForm a set B, where tcreateBefore the closing time tclose24 hours;
4) merging the set A and the set B to form a defect list set C0；
5) From the set C0Removing the defect list with the reduced force eliminated, namely filtering out the defect list with the reduced force as a defect phenomenon to form a set C;
6) inquiring in the defect set C according to the fan ID, if the same fan has a plurality of defect lists, only keeping 1 fan to ensure that each fan is counted once in a counting day, and finally obtaining a defect set M;
7) the number n of defects in the set M is the statistic day T of the wind field0Is down for over 24 hours.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110929155.3A CN113610250A (en) | 2021-08-13 | 2021-08-13 | Method for online calculating number of wind field fault outages exceeding 24 hours based on defect list |
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| CN202110929155.3A CN113610250A (en) | 2021-08-13 | 2021-08-13 | Method for online calculating number of wind field fault outages exceeding 24 hours based on defect list |
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5841964A (en) * | 1995-06-28 | 1998-11-24 | Canon Kabushiki Kaisha | Operating state management system |
| CN101770612A (en) * | 2009-09-17 | 2010-07-07 | 宁波北电源兴电力工程有限公司 | Device defect management module of EAM (Enterprise Asset Management) system in power plant |
| CN104361419A (en) * | 2014-09-10 | 2015-02-18 | 国家电网公司 | Electric transmission and transformation equipment state monitoring defect management system and method based on regulation and control integration |
| CN105117830A (en) * | 2015-08-11 | 2015-12-02 | 中节能港建（甘肃）风力发电有限公司 | Wind farm production operation and maintenance information collection application system and method |
| CN106874525A (en) * | 2017-04-18 | 2017-06-20 | 重庆工商大学 | A kind of Wind turbines equipment fault examination, the method and apparatus of statistics |
| CN108074197A (en) * | 2016-11-11 | 2018-05-25 | 河北新天科创新能源技术有限公司 | The control method of fan trouble data analysis system |
| CN109472459A (en) * | 2018-10-17 | 2019-03-15 | 贵州电网有限责任公司 | Meter and the electric grid operating task optimization method for changing maintenance risk and waiting time |
| CN111311133A (en) * | 2020-04-24 | 2020-06-19 | 广东卓维网络有限公司 | Monitoring system applied to power grid production equipment |
| WO2020140393A1 (en) * | 2018-12-30 | 2020-07-09 | 广东电网有限责任公司电力调度控制中心 | Large-scale wind power plant group auxiliary scheduling method and apparatus |
| CN112633531A (en) * | 2020-12-26 | 2021-04-09 | 北京中恒博瑞数字电力科技有限公司 | Wind power plant operation maintenance system |
-
2021
- 2021-08-13 CN CN202110929155.3A patent/CN113610250A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5841964A (en) * | 1995-06-28 | 1998-11-24 | Canon Kabushiki Kaisha | Operating state management system |
| CN101770612A (en) * | 2009-09-17 | 2010-07-07 | 宁波北电源兴电力工程有限公司 | Device defect management module of EAM (Enterprise Asset Management) system in power plant |
| CN104361419A (en) * | 2014-09-10 | 2015-02-18 | 国家电网公司 | Electric transmission and transformation equipment state monitoring defect management system and method based on regulation and control integration |
| CN105117830A (en) * | 2015-08-11 | 2015-12-02 | 中节能港建（甘肃）风力发电有限公司 | Wind farm production operation and maintenance information collection application system and method |
| CN108074197A (en) * | 2016-11-11 | 2018-05-25 | 河北新天科创新能源技术有限公司 | The control method of fan trouble data analysis system |
| CN106874525A (en) * | 2017-04-18 | 2017-06-20 | 重庆工商大学 | A kind of Wind turbines equipment fault examination, the method and apparatus of statistics |
| CN109472459A (en) * | 2018-10-17 | 2019-03-15 | 贵州电网有限责任公司 | Meter and the electric grid operating task optimization method for changing maintenance risk and waiting time |
| WO2020140393A1 (en) * | 2018-12-30 | 2020-07-09 | 广东电网有限责任公司电力调度控制中心 | Large-scale wind power plant group auxiliary scheduling method and apparatus |
| CN111311133A (en) * | 2020-04-24 | 2020-06-19 | 广东卓维网络有限公司 | Monitoring system applied to power grid production equipment |
| CN112633531A (en) * | 2020-12-26 | 2021-04-09 | 北京中恒博瑞数字电力科技有限公司 | Wind power plant operation maintenance system |
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