CN103711645A - Wind generating set state evaluation method based on modeling parameter feature analysis - Google Patents

Wind generating set state evaluation method based on modeling parameter feature analysis Download PDF

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CN103711645A
CN103711645A CN201310607010.7A CN201310607010A CN103711645A CN 103711645 A CN103711645 A CN 103711645A CN 201310607010 A CN201310607010 A CN 201310607010A CN 103711645 A CN103711645 A CN 103711645A
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data
wind power
parameter
analysis
state
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CN103711645B (en
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贾利民
刘展
庞宇
雷涛
童亦斌
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Beijing nenggaopukang measurement and Control Technology Co., Ltd
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BEIJING NEGO AUTOMATION TECHNOLOGY Co Ltd
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    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/70Wind energy
    • Y02E10/72Wind turbines with rotation axis in wind direction

Abstract

The invention relates to a wind generating set state evaluation method based on modeling parameter feature analysis. The method includes seven steps of wind generating set monitoring data screening, wind generating set operating condition identification, state monitoring data compilation and classification, data modeling structure identification and matching, intelligent model structure parameter estimation, secondary processing of model parameter information, and wind power generation equipment warning and alarm. According to the method, data modeling parameters of wind generating set state monitoring data are taken as kernel state evaluation variables, wind generating set state monitoring information is reflected in a model parameter form with high information summarization capacity within a certain period of time, and effective evaluation of states of wind power generation equipment is realized by means of analysis of model parameter change ranges, rules, tendencies and features.

Description

Wind power generating set state evaluating method based on modeling parameters signature analysis
Technical field
The present invention relates to technical field of wind power generation, particularly relate to the wind power generating set state evaluating method based on modeling parameters signature analysis.
Background technique
Wind-power electricity generation unit's cost of electricity-generating has approached thermoelectricity cost of electricity-generating substantially, is tool commercial value in current all renewable energy sourcess, and tool is promoted real variety of energy sources; Wind-power electricity generation is a kind of clean energy resource, and energy-saving ring is possessed to important economic and social benefits.
Wind-power electricity generation is the emphasis direction of national energy field medium-term and long-term plans, and the existing total installation of generating capacity of China reaches 7,400 ten thousand kilowatts, accounts for 7% of total installed capacity in power grid, and " 12 " end planning wind-powered electricity generation total installation of generating capacity reaches 0.98 hundred million kilowatt; " 13 " last wind-powered electricity generation total installation of generating capacity reaches 1.5~200,000,000 kilowatts; To the year two thousand fifty, wind-powered electricity generation total installation of generating capacity reaches 17~20% left and right of electrical network total installation of generating capacity; Therefore wind power generation field has wide and brighter and clearer prospect, by the association area industry of its drive, also must have optimistic market prospects;
China's wind-power electricity generation industry has been walked out the technological demonstration stage on a small scale completely, entered the large-scale promotion stage at present, because China's Wind Power Generation Industry is started late, technological accumulation is weak, relevant matching component still can not meet wind-powered electricity generation complete machine highly effective and safe service condition completely, the complete overall design technology mode of direct Introduced From Abroad that particularly China's wind-powered electricity generation complete machine enterprise overwhelming majority adopts further causes current wind-powered electricity generation whole aircraft reliability cannot meet actual motion demand, major accident frequency is occurred frequently, and blower fan availability is starkly lower than external unit.
Along with developing rapidly of China's Wind Power Generation Industry, due to technological trend and Cost Competition demand, the Eleventh Five-Year Plan period the 1.5MW wind power generating set of main flow can not meet current wind-power market demand, and single-machine capacity scale has progressively become the mainstream model of current domestic wind-power market at 2MW and above wind power generating set.Along with the progressively rising of single-machine capacity, wind-powered electricity generation machine shape size also increases rapidly, and the load level of complete machine is also soaring rapidly, therefore the reliability requirement of its mechanical assembly is also improved rapidly.
Under Long Yuan Power Group limited company, Zhong Neng power company is subject to National Energy Board to entrust the < < vibration of wind generating set status monitoring guide rule > > that drafts formulation to implement in national wind-powered electricity generation industry on November 1st, 2011.This guide rule has carried out detailed statement for realizing state monitoring method by detection vibration of wind generating set signal, stipulate that all offshore wind farm units should select to adopt fixed installation system, land 2MW (and more than) wind turbine group selection employing fixed installation system, optional semifixed installation system or the portable system selected of the following wind-powered electricity generation unit of land 2MW; Guide rule has been made detailed regulation to wind generating set vibration condition monitoring system simultaneously, wind-powered electricity generation Vibration Condition Monitoring link is carried out unified, and the running state of the grasp unit that can more become more meticulous, the reasonable arrangement repair time, reduces wind-powered electricity generation accident.
In existing machine performance monitoring method, oscillating signal monitoring is a kind of relatively ripe monitoring technique removing, and has been applied in widely in wind-powered electricity generation condition monitoring system abroad; Except mechanical oscillation signal, comprise that the signals such as temperature, pressure, rotating speed, video, audio frequency, stress, displacement, oil quality can reflect the real-time status of the different links of wind-powered electricity generation mechanical system, had a large amount of companies to launch the exploitation of correlation behavior monitoring product abroad.
Wind-power electricity generation is a kind of typical large rotating inertia rotating machinery, and the state of its mechanical system often can pass through to the vibration monitoring characterization of mechanical assembly out.Due to system state machine inherent characteristic slowly in time, often time domain specification difference is little therefore conventionally mechanical system to be carried out under identical operating mode vibration monitoring data in shorter time range.
In existing wind-power electricity generation method for monitoring state, to monitored variable simple transformation of wind power generating set, be secondary monitored variable conventionally, then based on secondary variable, realize the operations such as Threshold Alerts and state early warning.A kind of conventional method is that the vibration of wind generating set acceleration information of monitoring is converted into vibration severity information, by vibration severity information, carry out unit and the warning of assembly vibration threshold and carry out early warning to approaching the situation of alarm threshold value, by vibration severity variation tendency situation, carrying out vibration trend warning.
The major defect of prior art be the amount of analysis of data information less, the monitor data that often passes through timing acquiring short period scope is as analysing and processing foundation, because wind power generating set operating conditions is complicated, and machine performance changes slowly, conventionally in short period section there is certain contingency in data time-domain information, be difficult to truly reflect wind power generating set running state, need by further extracting effective status characterization parameter.
Summary of the invention
The present invention is directed in existing wind power generating set state estimation technology state estimation parameter designing comparatively simple, be difficult to effectively weigh wind power generating set effective running state in certain hour section, proposed a kind of wind power generating set state monitoring information Efficient Evaluation method
Object of the present invention is achieved through the following technical solutions:
Wind power generating set state evaluating method based on modeling parameters signature analysis, the method comprises the steps:
1) monitor message of continuous monitor data in designated state evaluation time section is differentiated, screened out manifest error data, and the data after screening out are carried out to interpolation processing;
2) monitor message of described continuous monitor data is analyzed to the typical Operational Limits in conjunction with wind power generating set under different operating conditionss, the running state of intelligent recognition wind power generating set under different monitoring data segment;
3) by designated state evaluation time section, monitor data is according to operating conditions, data type continuously, the specific data format of foundation carries out data packing to be processed;
4), according to wind power generating set operating conditions data, in conjunction with modeling data type, different types of data packet under different operating conditionss is carried out to data model coupling;
5) according to mated model structure, and the packet of step 3 classification, by setup parameter identifying method, estimate that identification model parameter operates, each packet is completed to model parameter identification according to model structure;
6) refer to that according to time sequencing, in different conditions evaluation time section, the Model Distinguish parameter of the packet of same kind is carried out secondary data analysis;
7) according to secondary data analysis result, wind power equipment running state is judged, model parameter is exceeded to normal range (NR) simultaneously and carry out Threshold Alerts, model parameter variance ratio is exceeded to setting range carry out variation tendency warning, based on state, judge that conclusion carries out fault pre-alarming to watch-dog.
Monitor message in described step 1 comprises one or more in wind power generating set and assembly vibration thereof, rotating speed, temperature, pressure, stress, moment of torsion, oil product oil, video, audio frequency, wind speed, security protection, electric network information or in above-mentioned.
Data model in described step 4 coupling comprises one or more in statistics class model, linearity and non-new relationship mapping class model or above-mentioned model.
In described step 6, secondary data analysis comprises one or more in data statistics, trends analysis, variance ratio analysis, pattern analysis or the above-mentioned analysis of model parameter information
The invention has the advantages that:
The method be take the data modeling parameter of wind power generating set condition monitoring data and is kernel state assessment variable, wind power generating set status monitoring information in certain hour section is embodied to have the model parameter form of elevation information abstract ability, by model parameter excursion, rule, trend, feature are analyzed, realize wind power plant state Efficient Evaluation.The method is not only applicable to wind-power electricity generation state estimation, is applicable to the long-time continuous condition monitoring appraisal procedure of the slow change system of various different occasion state yet.
Accompanying drawing explanation
The wind power generating set state evaluating method step schematic diagram of Fig. 1 based on modeling parameters signature analysis.
Embodiment
If Fig. 1 is the wind power generating set state evaluating method step schematic diagram based on modeling parameters signature analysis.The method comprises following 7 steps:
Step 1: wind-powered electricity generation unit monitor data screening;
Step 2: running of wind generating set operating mode identification;
Step 3: condition monitoring data reduction classification;
Step 4: data modeling Structure Identification coupling;
Step 5: model of mind on-line identification;
Step 6: model parameter information secondary treatment;
Step 7: wind power equipment state early warning and alarming.
Below each step is described in detail.
In step 1, the screening of wind-powered electricity generation unit monitor data is mainly that the validity of continuous monitor data monitor message in designated state evaluation time section is differentiated, and screens out manifest error data, and it is carried out to interpolation processing.Wind power generating set state monitoring information includes but are not limited to the information such as wind power generating set and assembly vibration thereof, rotating speed, temperature, pressure, stress, moment of torsion, oil product oil, video, audio frequency, wind speed, security protection, electric network information.Carry out data screening and can eliminate in data capture or transmitting procedure owing to being subject to the issuable wrong report data of influence of noise, improve the accuracy of data modeling.
In step 2, the identification of running of wind generating set operating mode mainly refers to by continuous monitor data is analyzed, typical Operational Limits in conjunction with wind power generating set under different operating conditionss, the running state of intelligent recognition wind power generating set under different monitoring data segment.Preferably the identification of running of wind generating set operating mode can be carried out operating mode identification according to mean wind velocity information or wind power generator rotor rotary speed information.
In step 3, condition monitoring data reduction classification refers to that the specific data format of foundation carries out data packing to be processed by designated state evaluation time section, monitor data is according to operating conditions, data type continuously.
Preferably can, according to 10 minutes different mean wind velocitys, dissimilar monitor data be carried out classifying packing or carry out uniform packing to having correlation data.Correlation data comprises but is not limited only to rotating speed and vibration, wind speed and vibration etc.
In step 4, data modeling Structure Identification coupling refers to according to wind power generating set operating conditions data, and in conjunction with modeling data type, intelligence is carried out data model coupling to different types of data packet under different operating conditionss.Matching Model includes but are not limited to: statistics class model, linearity and non-new relationship mapping class model etc.Wherein, statistical model typically refers to the distribution character of describing monitored variable, can adopt methods such as including but are not limited to data statistics, fitting of a polynomial to carry out modeling to the distribution character of monitored variable, for example to vibration amplitude information, can pass through polynomial fitting method or particular model parameter identification, such as Weibull distribution parameters identification, Gaussian Distribution Parameters identification etc., sets up vibration amplitude distributed model; Linear and non-new relationship mapping class model typically refers to the mapping relations of describing between two or more variablees, can realize by including but are not limited to linearity, the non-linear modeling methods such as transfer function, neuron network, for example, to wind speed information and tower cylinder vibration information, can set up wind speed information and tower cylinder vibration information non-linear relation model by Nonlinear Modeling.
In step 5, model of mind on-line identification refers to the model structure according to coupling, the packet that step 3 is classified according to operating conditions, data type, carry out based on Computational intelligence technology, according to setup parameter identifying method, estimate the operation of identification model parameter, each packet is completed to model parameter identification according to model structure.Model of mind on-line identification method can adopt intelligent search algorithms such as including but are not limited to statistical approach, polynomial fitting method, method of least squares, random search etc.
In step 6, the secondary treatment of model parameter information mainly refers to according to time sequencing in different conditions evaluation time section, the Model Distinguish parameter of the packet of same kind is carried out secondary data analysis, and secondary data is analyzed content and included but are not limited to data statistics for model parameter information, trends analysis, variance ratio analysis, pattern analysis etc.Described secondary treatment is mainly that order different time sections model parameter data variation is in time analyzed, the technology platform of reporting to the police and providing convenience for state failure judgement.For example, the secondary treatment of model parameter information can be to all under same operating conditions, the statistical information of tower cylinder vibration amplitude is carried out continization graphic data processing, generate under the same operating conditions of temporal evolution, tower cylinder vibration amplitude statistical parameter change curve, facilitates advanced techniques personnel to carry out state analysis.
In step 7, wind power equipment state early warning and alarming is mainly according to model parameter information secondary treatment conclusion, intelligence judges wind power equipment running state, model parameter exceeded to normal range (NR) simultaneously and carries out Threshold Alerts, model parameter variance ratio is exceeded to setting range carries out variation tendency warning, based on state, judges that conclusion carries out fault pre-alarming to watch-dog.
Based on state, judge that conclusion carries out fault pre-alarming to watch-dog, preferably scheme is with a life period of equipment variation track, based on current data modeling parameters numerical value corresponding device life cycle state by experience and historical data intelligence generated data modeling parameters.
The present invention is directed in existing wind power generating set state estimation technology state estimation variable, to contain data information less, is difficult to effectively wind-powered electricity generation set state be assessed, and proposed a kind of wind power generating set state evaluating method based on modeling parameters signature analysis.The method is analyzed by model parameter excursion, rule, trend, feature to elevation information feature extraction, improved greatly the design of conventional method evaluate parameter comparatively simple, be difficult to effectively realize the shortcoming of wind-powered electricity generation unit complex state assessment, effectively promoted validity and the practicability of wind power equipment state estimation.
Should be appreciated that the above detailed description of technological scheme of the present invention being carried out by preferred embodiment is illustrative and not restrictive.Those of ordinary skill in the art modifies reading the technological scheme that can record each embodiment on the basis of specification of the present invention, or part technical characteristics is wherein equal to replacement; And these modifications or replacement do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technological scheme.

Claims (4)

1. the wind power generating set state evaluating method based on modeling parameters signature analysis, is characterized in that, the method comprises the steps:
1) monitor message of continuous monitor data in designated state evaluation time section is differentiated, screened out manifest error data, and the data after screening out are carried out to interpolation processing;
2) monitor message of described continuous monitor data is analyzed to the typical Operational Limits in conjunction with wind power generating set under different operating conditionss, the running state of intelligent recognition wind power generating set under different monitoring data segment;
3) by designated state evaluation time section, monitor data is according to operating conditions, data type continuously, the specific data format of foundation carries out data packing to be processed;
4), according to wind power generating set operating conditions data, in conjunction with modeling data type, different types of data packet under different operating conditionss is carried out to data model coupling;
5) according to mated model structure, and the packet of step 3 classification, by setup parameter identifying method, estimate that identification model parameter operates, each packet is completed to model parameter identification according to model structure;
6) refer to that according to time sequencing, in different conditions evaluation time section, the Model Distinguish parameter of the packet of same kind is carried out secondary data analysis;
7) according to secondary data analysis result, wind power equipment running state is judged, model parameter is exceeded to normal range (NR) simultaneously and carry out Threshold Alerts, model parameter variance ratio is exceeded to setting range carry out variation tendency warning, based on state, judge that conclusion carries out fault pre-alarming to watch-dog.
2. the wind power generating set state evaluating method based on modeling parameters signature analysis according to claim 1, it is characterized in that, the monitor message in described step 1 comprises one or more in wind power generating set and assembly vibration thereof, rotating speed, temperature, pressure, stress, moment of torsion, oil product oil, video, audio frequency, wind speed, security protection, electric network information or in above-mentioned.
3. the wind power generating set state evaluating method based on modeling parameters signature analysis according to claim 1, it is characterized in that, data model in described step 4 coupling comprises one or more in statistics class model, linearity and non-new relationship mapping class model or above-mentioned model.
4. the wind power generating set state evaluating method based on modeling parameters signature analysis according to claim 1, it is characterized in that, in described step 6, secondary data analysis comprises one or more in data statistics, trends analysis, variance ratio analysis, pattern analysis or the above-mentioned analysis of model parameter information.
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CN105134482A (en) * 2015-07-22 2015-12-09 扬州大学 Gray combined modeling and optimized vibration controlling method of large intelligent draught fan blade system
CN105888987A (en) * 2016-04-21 2016-08-24 华电电力科学研究院 Wind generating set performance assessment method based on correlation analysis
CN106815771A (en) * 2015-12-02 2017-06-09 中国电力科学研究院 A kind of long-term evaluation method of wind power plant load
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CN109238727A (en) * 2018-09-26 2019-01-18 广州文搏科技有限公司 A kind of engine failure monitoring and warning system
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CN104218571A (en) * 2014-08-28 2014-12-17 中国南方电网有限责任公司超高压输电公司检修试验中心 Running state evaluation method for wind power generation equipment
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CN109238727A (en) * 2018-09-26 2019-01-18 广州文搏科技有限公司 A kind of engine failure monitoring and warning system

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