CN110059872A - A kind of marine wind electric field O&M dispatching method based on status monitoring - Google Patents
A kind of marine wind electric field O&M dispatching method based on status monitoring Download PDFInfo
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Abstract
The invention discloses a kind of marine wind electric field O&M dispatching method based on status monitoring.Step: acquisition monitoring data relevant to each offshore wind farm machine group parts deterioration state, and corresponding artificial nerve network model is constructed to predict the remaining life of each machine group parts；Remaining life reliability of each machine group parts of comprehensive analysis after repairing lead time and maintenance behavior economics, construct the effective maintenance cost function of consecutive mean of each machine group parts, and the foundation of maintenance decision is formulated as each machine group parts；State Maintenance threshold value and opportunity maintenance threshold value are set, and the effective maintenance cost of consecutive mean of each machine group parts of building is compared with the two threshold values, to formulate optimal O&M operation plan.The present invention realizes different times to the Maintenance Scheduling of the machine group parts with different deterioration states a series of in marine wind electric field, and reduces the O&M cost of marine wind electric field.
Description
Technical field
The invention belongs to marine wind electric field technical fields, in particular to a kind of marine wind electric field O&M dispatching method.
Background technique
Unique maritime environment brings importance challenge to the operation management of marine wind electric field.Offshore wind farm unit is long-term
It operates under dynamic load condition and severe natural environment, causes the high failure risk of Wind turbines component and complicated bad
Change process.Furthermore Pit Crew enter execute maintenance activity in marine wind electric field must be via the fortune of the tools such as ship or aircraft
It send, and limitation of these means of transports vulnerable to marine weather, so that the accessibility difference into wind power plant causes to repair the waiting time
It is longer；And the single transportation cost of these marine vehicles is prohibitively expensive.
The O&M dispatching method of current marine wind electric field still continues to use the experience of land wind power plant mostly, using subsequent and prevention
Property the strategy that combines of maintenance, the maintenance decision of each machine group parts is that the manufacture characteristic and engineering knowledge based on the base part are estimated
The product data (time or reliability) of calculation are with formulation.However these product data are in the maintenance for formulating same type units component
When decision, single machine group parts are not often accounted in the deterioration feature of practical wind power plant, are directed to can not formulate and have
The maintenance project of property.Current O&M dispatching method has that maintenance is insufficient or maintenance is excessive, and between each machine group parts
Maintenance, which lacks, to be coordinated and cooperates, and so as to cause the O&M cost of great number in marine wind electric field, it is complete to account for about offshore wind farm project
Life cycle cost 14-30%.
Summary of the invention
In order to solve the technical issues of above-mentioned background technique proposes, the invention proposes a kind of scheduling of marine wind electric field O&M
Method, realize different times to the Maintenance Schedulings of the machine group parts with different deterioration states a series of in marine wind electric field, and
Reduce the O&M cost of marine wind electric field.
In order to achieve the above technical purposes, the technical solution of the present invention is as follows:
A kind of marine wind electric field O&M dispatching method based on status monitoring, the marine wind electric field include on several Taiwan Straits
Wind power generating set, every offshore wind turbine include several machine group parts, comprising the following steps:
(1) monitoring data relevant to each offshore wind farm machine group parts deterioration state are acquired, and construct corresponding artificial mind
The remaining life of each machine group parts is predicted through network model；
(2) remaining life reliability of each machine group parts of comprehensive analysis after repairing lead time and maintenance behavior economics,
Construct the effective maintenance cost function of consecutive mean of each machine group parts, and as each machine group parts formulate maintenance decision according to
According to；
(3) State Maintenance threshold value and opportunity maintenance threshold value are set, and the dynamic of each machine group parts of step (2) building is flat
Effective maintenance cost function is compared with the two threshold values, to formulate optimal O&M operation plan.
Further, in step (1), the artificial neural network includes:
Model parameter choose, selected mode input parameter be offshore wind farm machine group parts currently and the previous monitoring moment
Enlistment age and multiple Condition Monitoring Data measured values；Selected model output parameters use the longevity for offshore wind farm machine group parts
Order percentage；
Model construction, constructed model are one with multi input and the BP network model singly exported；
Model training, used training algorithm are Levenberg-Marquardt algorithm.
Further, in step (2), the effective maintenance cost function of consecutive mean is as follows:
In above formula,For the average effective maintenance cost of offshore wind farm unit i inner part j；ti,jFor offshore wind farm unit i
The enlistment age of inner part j；tLTo repair lead time；Indicate the predicting residual useful life value of offshore wind farm unit i inner part j；For
The preventative maintenance cost of offshore wind farm unit i inner part j；For the breakdown maintenance cost of offshore wind farm unit i inner part j；
P (*) indicates probability.
Further, the predicting residual useful life value of offshore wind farm unit i inner part jDistribution it is as follows:
Wherein,For the service life percentage of artificial nerve network model prediction；up,jAnd σp,jRespectively artificial neuron
The average value and standard deviation of network model service life percent prediction error.
Further, in step (3), by the effective maintenance cost function of the consecutive mean of each machine group parts and two threshold values
It is compared, to formulate optimal O&M operation plan, detailed process is as follows:
When the effective maintenance cost of the consecutive mean of machine group parts is less than State Maintenance threshold value, expression is comprehensive to measure current unit
Remaining life reliability, maintenance cost and the enlistment age of component, the performance state of machine group parts executes after repairing lead time at this time
The opportunity of preventative maintenance behavior is suitably, will to determine the State Maintenance decision of the machine group parts；
When the effective maintenance cost of the consecutive mean of machine group parts is more than or equal to State Maintenance threshold value and is less than opportunity maintenance threshold
Value, indicates comprehensive and measures the machine group parts current remaining life reliability, maintenance cost and enlistment age, determines with State Maintenance is determined
The machine group parts of plan execute together maintenance be it is economical, the opportunity maintenance plan of the machine group parts will be formulated；
When the effective maintenance cost of the consecutive mean of machine group parts is greater than opportunity maintenance threshold value, any maintenance will not be executed and gone
For；
Determining O&M operation plan according to the method described above will all implement common maintenance after the maintenance lead time of variation and live
It is dynamic；When all qualified machine group parts all execute common maintenance after repairing lead time, this maintenance cycle is indicated
Terminate, each machine group parts are by continuous service until the machine group parts consecutive mean that failure next time occurs or newly monitors the moment has
It imitates maintenance cost and is less than State Maintenance threshold value.
Further, in step (3), the State Maintenance threshold value and opportunity maintenance threshold value are rule of thumb or emulation experiment
To set.
Determine that the method for State Maintenance threshold value and opportunity maintenance threshold value is as follows using emulation experiment:
Establish following marine wind electric field O&M scheduling optimization model:
Above formula is that the year O&M cost of marine wind electric field minimizes objective function, CE(cc,co) it is transporting in year for marine wind electric field
Tie up cost, ccAnd coRespectively State Maintenance threshold value and opportunity maintenance threshold value, trAnd tqIt is the q times maintenance cycle and the r times respectively
The time that maintenance cycle starts；TCkFor total O&M cost in marine wind electric field single maintenance cycle, TCk=TCR+TCC+TCO+
Cfixed, when unit component malfunction any in marine wind electric field, will determine the correction maintenance plan of the machine group parts, and generate
Correction maintenance costWherein,Be offshore wind farm unit i inner part j breakdown maintenance at
This,It is binary variable, indicates whether the component j of i in unit breaks down, value is 1 or 0,It is to enter offshore wind farm
The fixed cost of unit i, IiIt is binary variable, is expressed as Pit Crew and whether enters in offshore wind farm unit i to execute maintenance
Activity, value are 1 or 0, and N is Wind turbines number, and M is the component count in each Wind turbines；When the consecutive mean of machine group parts
When effective maintenance cost is less than State Maintenance threshold value, the State Maintenance plan of the machine group parts will be formulated, and generate State Maintenance
CostWherein,It is the preventative maintenance cost of unit i inner part j,It is that binary system becomes
Amount, indicates whether machine group parts meet the condition for formulating State Maintenance, and value is 1 or 0；When the consecutive mean of machine group parts is effective
Maintenance cost is more than or equal to State Maintenance threshold value and is less than or equal to opportunity maintenance threshold value, will determine the opportunity maintenance of the machine group parts
Plan, and generate opportunity maintenance costWherein,For the preventative dimension of unit i inner part j
Accomplish this,It is binary variable, indicates whether machine group parts meet the condition for formulating State Maintenance, value is 1 or 0；When opening
The dynamic Single Maintenance period will generate the fixed cost C that O&M team is once transported to by O&M ship marine wind electric fieldfixed；
Above formula is maintenance necessity constraint, is meant that in marine wind electric field in any maintenance cycle Wind turbines on every Taiwan Straits
Interior most failures for a component occur；
Above formula is maintainability constraint, is meant that the maintenance personal's limited amount possessed in marine wind electric field, causes
The ability to work that maintenance is executed in maintenance period has limitation；
By emulating the operating parameter of practical marine wind electric field, it is corresponding to solve above-mentioned model middle age O&M cost minimum
Optimal solution, as State Maintenance threshold value and opportunity maintenance threshold value.
By adopting the above technical scheme bring the utility model has the advantages that
(1) present invention constructs the offshore wind farm machine group parts enlistment age based on the current and previous monitoring moment and multiple states
The artificial nerve network model of monitoring data integration driving, is effectively extracted the deterioration shape of each machine group parts in actual operation
State reduces each offshore wind farm machine group parts to provide accurate predicting residual useful life information for backing up maintenance decision
Failure risk；
(2) different times of the invention that analyze enter the maintenance lead time that the accessibility of marine wind electric field leads to length and changes
Particularity, a kind of new maintenance foundation --- effective maintenance cost function of consecutive mean is proposed, for formulating each machine group parts
Maintenance decision, be effectively reduced the effect that fixed maintenance project is embodied in accessibility factor after repairing lead time
It influences；
(3) present invention considers maintenance and economical dependence in marine wind electric field between machine group parts, by once to more
A offshore wind farm machine group parts implement common maintenance, to reduce Pit Crew and means of transport enters marine wind electric field
Number and transportation cost；
(4) present invention is with easy, data have it is ready availability, in addition to marine wind electric field has facility and existing technologies, nothing
Other extras costs are needed, therefore are all feasible and effective technically and economically；
(5) the present disclosure applies equally to the scheduling of the O&M in off-lying sea sea area, to challenge deep-sea sea for the following marine wind electric field
Domain provides reliable, economical operation possibility.
Detailed description of the invention
Fig. 1 is overall flow figure of the invention；
Fig. 2 is specific flow chart of the invention；
Fig. 3 is the artificial nerve network model structure chart based on Condition Monitoring Data in the present invention；
Fig. 4 is the year O&M cost curve graph in embodiment under different maintenance sets of threshold values conjunctions；
Fig. 5 is in embodiment with the year O&M cost curve graph of maintenance lead time variation.
Specific embodiment
Below with reference to attached drawing, technical solution of the present invention is described in detail.
As depicted in figs. 1 and 2, a kind of marine wind electric field O&M dispatching method based on status monitoring, including three big modules,
Module first is that the predicting residual useful life based on Condition Monitoring Data, module second is that the maintenance based on average effective maintenance cost according to
According to module is specific as follows third is that opportunity maintenance strategy:
Module one: acquisition monitoring data relevant to each offshore wind farm machine group parts deterioration state, and construct corresponding people
Artificial neural networks model is to predict the remaining life of each machine group parts；
Module two: remaining life reliability of each machine group parts of comprehensive analysis after repairing lead time and maintenance behavior economics
Property, the effective maintenance cost function of consecutive mean of each machine group parts is constructed, and formulate maintenance decision as each machine group parts
Foundation；
Module three: setting State Maintenance threshold value and opportunity maintenance threshold value, and by the consecutive mean of each machine group parts of building
Effective maintenance cost is compared with the two threshold values, to formulate optimal O&M operation plan.
In the present embodiment, module one can be realized using following preferred embodiment:
Step 1-1: it collects the historical failure data of each component in marine wind electric field and establishes database, wherein in database
Enlistment age value and multiple Condition Monitoring Datas relevant to the component deterioration state including each component difference monitoring moment；
Step 1-2: available status data is divided into training set and verifying collects, and carries out corresponding data processing；
Step 1-3: the artificial nerve network model of constructed offshore wind farm machine group parts is as shown in figure 3, with processing
The Artificial Neural Network Prediction Model of each component of data training afterwards, and choose the verifying collection the smallest people of mean square deviation in training process
Prediction model of the artificial neural networks model as subsequent machine group parts；In Fig. 3, ti,jWithBe unit i inner part j currently and
The enlistment age at previous monitoring moment,WithIt is unit i inner part j currently and the status monitoring at previous monitoring moment measures 1
Value,WithIt is value of the unit i inner part j currently with the state measurement q at previous monitoring moment,It is that unit i inner part j works as
The service life percentage at preceding monitoring moment；
Step 1-4: machine group parts enlistment age and multiple Condition Monitoring Data measured values based on the current and previous monitoring moment
Input value, the service life percentage of each machine group parts of the currently monitored moment is predicted with the artificial nerve network model after training
Than.
In the present embodiment, module two can be realized using following preferred embodiment:
Step 2-1: the remaining life for calculating the currently monitored moment machine group parts according to the service life percentage of prediction is pre-
Measured value, in addition, the prediction of our the service life percentages using artificial nerve network model in trained and verification process misses
Difference, to estimate the uncertainty of predicting residual useful life value；
Wherein, if the enlistment age of the offshore wind farm machine group parts at the currently monitored moment is ti,j, artificial nerve network model prediction
Service life percentage beError collection is predicted according to the ANN of acquisition, can calculate ANN service life percent prediction mistake
The average value mu of differencep,jAnd standard deviationp,j.The prediction fault time of so machine group parts will beResidue uses the longevity
Ordering predicted value isAnd the standard deviation of remaining life predicted value willTherefore, also followed normal distribution is distributed the remaining life of the machine group parts at the currently monitored moment, remaining
Service life distributionIt can be expressed as follows:
Where it is assumed that in artificial nerve network model service life percentage prediction error Normal Distribution；With
When the machine group parts enlistment age and multiple Condition Monitoring Data measured values that the new monitoring moment collects can be used, step 1-4 and step are repeated
2-1, the predicting residual useful life distribution of sustainable renewal machine group parts；
Step 2-2: the unit residual service life of components prediction distribution based on the currently monitored moment, each machine group parts of comprehensive analysis
In the remaining life reliability and maintenance and detection of maintenance lead time that is long and changing, the dynamic of each machine group parts of real-time update is flat
Effective maintenance cost:
In above formula,For the average effective maintenance cost of offshore wind farm unit i inner part j；ti,jFor offshore wind farm unit i
The enlistment age of inner part j；tLTo repair lead time；Indicate the predicting residual useful life value of offshore wind farm unit i inner part j；For
The preventative maintenance cost of offshore wind farm unit i inner part j；For the breakdown maintenance cost of offshore wind farm unit i inner part j；
P (*) indicates probability.
In the present embodiment, module three can be realized using following preferred embodiment:
Step 3-1: preset State Maintenance threshold value and opportunity maintenance threshold value can be obtained by way of emulation；
Step 3-2: by each machine group parts average effective maintenance cost of update and preset State Maintenance threshold value and chance
Maintenance threshold value is compared, the O&M operation plan of each machine group parts in the marine wind electric field to formulate the currently monitored moment, tool
Body implementation process are as follows:
Step 3-2-1: any unit component malfunction in marine wind electric field will determine the correction maintenance of the machine group parts
Plan, and generate correction maintenance cost TCR, the unit portion including cost and maintenance failure in unit where entering trouble unit
The summation of cost needed for part；
Wherein,It is the breakdown maintenance cost of offshore wind farm unit i inner part j.It is binary variable, indicates unit
Whether the component j of interior i breaks down, and value is 1 or 0.It is the fixed cost into offshore wind farm unit i, IiIt is expressed as tieing up
Repair whether team enters execution maintenance in offshore wind farm unit i, value is 1 or 0；
Step 3-2-2: if the machine group parts average effective maintenance cost at the currently monitored moment is lower than State Maintenance threshold value,
The State Maintenance plan of the machine group parts will be formulated, and generates State Maintenance cost TCC, mainly by determining State Maintenance plan
The compositions such as cost needed for the machine group parts of the entering cost of unit where component and maintenance current operating conditions；
Wherein,Indicate the preventative maintenance cost of unit i inner part j,It is binary variable, indicates machine group parts
Whether the condition of formulating State Maintenance is met, value can be 1 or 0,It indicates to determine the State Maintenance of unit i inner part j
Plan；
Step 3-2-3: above-mentioned any dimension practice Buddhism or Taoism for generation all will start the Single Maintenance period, at this time will generate once by
O&M team is transported to the fixed cost C of marine wind electric field by O&M shipfixed；
Step 3-2-4: in this maintenance cycle, judge that other are repaired still in the machine group parts average effective of operating status
Cost should by determining if the average effective maintenance cost of machine group parts is fallen between State Maintenance threshold value and opportunity maintenance threshold value
The opportunity maintenance plan of machine group parts, and generate opportunity maintenance cost TCO, mainly cost needed for workover rig group parts；
Wherein,It is binary variable,Indicate that the machine group parts have determined that the machine group parts of maintenance project with other
It is economically viable for implementing common maintenance；
Step 3-2-5: opportunity maintenance threshold value is greater than for the average effective maintenance cost of machine group parts, indicates current unit
The operating status of component is good, does not need to implement any preventive maintenance schedule；
When all qualified machine group parts all execute common maintenance after repairing lead time, expression is this time repaired
The end in period, each machine group parts are by continuous service until failure next time occurs or the machine group parts at new monitoring moment are averaged
Effective maintenance cost is less than State Maintenance threshold value；
Step 3-2-6: total O&M cost based on above-mentioned maintenance implementation process, in marine wind electric field single maintenance cycle
TCkIt can be expressed as follows:
The O&M cost summation that the q times maintenance cycle is started to start to the r times maintenance cycle is divided by maintenance cycle twice
Between the available day O&M cost of time interval, wherein q < r, marine wind electric field pursues year O&M cost and minimizes, therefore
The year O&M cost that marine wind electric field can be obtained minimizes objective function:
Wherein, trAnd tqIt is the time that the q times maintenance cycle and the r times maintenance cycle start, c respectivelycAnd coRespectively shape
State repairs threshold value and opportunity maintenance threshold value；
(1) maintenance necessity constraint:
It is meant that in marine wind electric field a component at most occur in Wind turbines on every Taiwan Straits in any maintenance cycle
Failure；
(2) maintainability constrains:
It is meant that the maintenance personal's limited amount possessed in marine wind electric field, causes to execute maintenance work within the maintenance period
Dynamic ability to work has certain limitation；
Based on marine wind electric field O&M scheduling optimization model constructed by step 3-2-6, by simulating practical offshore wind farm
Operating parameter (operational process of machine group parts, the weather conditions of location, Wind turbines type and the quantity, maintenance people of field
Quantity and quality, the type of O&M ship and quantity of member etc.), solving model middle age O&M cost minimizes corresponding optimal
Solution can be used as the preset state maintenance threshold value and opportunity maintenance threshold value of practical marine wind electric field, for formulating each machine group parts
O&M operation plan.
By aforesaid operations, under the existing equipment of current marine wind electric field and technology, with based on Condition Monitoring Data
Marine wind electric field O&M dispatching method, it can be achieved that a series of with different deterioration states in the marine wind electric field of different times
The scheduling of the optimal maintenance project of machine group parts.
The following are a specific embodiments:
It constructing example and carries out simulation analysis, marine wind electric field is made of the offshore wind turbine of 60 same types,
Wherein there is 4 critical pieces (rotor, base bearing, gear-box and generator), the event of any one component in every unit
Barrier can all lead to the stoppage in transit of entire unit.The operation time limit of marine wind electric field is set as 20 years；Assuming that the maintenance of each machine group parts
Lead time is fixed value, t in the process of runningL=30 days；Furthermore simultaneously every the remaining life of the unit component of prediction in 10 days
Calculate its average effective maintenance cost, tI=10 days.The relevant parameter of each machine group parts such as table 1 is to table 2.
The cost and Weibull distribution parameters of 1 offshore wind farm unit critical component of table
The service life percent prediction error of 2 artificial nerve network model of table
| Component j | Predict average error | Predict the standard deviation of error |
| Rotor | 8.00% | 10.00% |
| Base bearing | 6.68% | 7.53% |
| Gear-box | 8.00% | 10.00% |
| Generator | 6.68% | 7.53% |
The simulation model is realized in MATLAB, and the year O&M cost obtained under different maintenance threshold value combinations is bent
Line, as shown in figure 4, optimum results show that preset State Maintenance threshold value and opportunity maintenance threshold value take respectively:When, it is right
Answering optimal marine wind electric field year O&M cost is 2423050 dollars/year.
It is imitated for the economy for analyzing proposed O&M dispatching method by the way that following 5 kinds of O&M dispatching methods are arranged
True comparative analysis.
1) method 1: the O&M scheduling that only at sea the machine group parts are just formulated in the failure of machine group parts in wind power plant is counted
It draws.
1) method 2: the O&M operation plan of single machine group parts in marine wind electric field is formulated respectively based on reliability.
2) it method 3: is formulated respectively by Fixed Time Interval based on the O&M scheduling of single machine group parts in marine wind electric field
It draws.
3) method 4: the O&M tune of single machine group parts in marine wind electric field is formulated respectively based on average effective maintenance cost
Degree plan.
4) O&M scheduling method 5: is formulated to machine group parts a series of in marine wind electric field based on average effective maintenance cost
Plan namely the present invention proposed in O&M dispatching method.
Wherein O&M dispatching method 1 is a kind of the simplest and basis method in marine wind electric field；Method 2 and method 3
The most commonly used O&M dispatching method in current marine wind electric field；Method 4 be based on maintenance new proposed in the present invention according to
According to the method for formulating single offshore wind farm machine group parts O&M operation plan；Method 5 is on the basis of the maintenance foundation proposed
Opportunity maintenance strategy is considered, as has not yet been reached but the machine group parts of its deterioration state proximity state maintenance requirement provides one
Chance and the machine group parts of other determination maintenance projects implement the dispatching method of common maintenance.Above-mentioned 5 kinds of O&M dispatching parties
The obtained optimum results of method are as shown in table 3.
O&M dispatching method 4 is compared analysis with the optimum results of method 1, method 2 and method 3, what is proposed is new
Maintenance have in the O&M operation plan for formulating single machine group parts in marine wind electric field according to being average effective maintenance cost
Good expression power, be able to reduce marine wind electric field in year O&M cost 34.14%, 32.92%, 27.08%.
In addition the optimum results of control methods 5 and method 4 are it is found that formulating offshore wind farm based on average effective maintenance cost
Consider on the basis of the O&M operation plan of machine group parts opportunity maintenance strategy can directly for the annual O&M of marine wind electric field at
Originally 193050 dollars of reduction is brought.
35 kinds of O&M dispatching method optimum results of table
| O&M dispatching method | Method 1 | Method 2 | Method 3 | Method 4 | Method 5 |
| Total O&M cost | 79756000 | 77996000 | 71750000 | 52322000 | 48461000 |
| Year O&M cost | 3987800 | 3899800 | 3587500 | 2616100 | 2423050 |
| Cost reduces percentage | 34.14% | 32.92% | 27.08% | 7.38% | — |
Entering marine wind electric field accessibility difference for concrete analysis different times causes maintenance lead time that is long and changing to true
The influence of fixed offshore wind farm machine group parts O&M operation plan carries out sensitivity analysis to O&M dispatching method 4 in this example,
As a result as shown in Figure 5.
The result shows that in the case where other parameters are constant, with the variation of maintenance lead time, expected from marine wind electric field
The situation of year O&M cost presentation big ups and downs, it is known that the variation for repairing lead time has the year O&M cost of marine wind electric field
Conspicuousness influences.Consider maintenance lead time that is long and changing to the dimension for formulating offshore wind farm machine group parts O&M operation plan
Repairing foundation is reasonable and necessary therefore proposed by the invention new maintenance according in each offshore wind farm machine group parts of formulation
Maintenance decision is reasonable and effective.
Cause the variation of maintenance lead time to the most critical design of the present invention is: considering marine wind electric field accessibility
Determine that the implementation result of maintenance project influences, it is reliable by remaining life of each machine group parts of comprehensive analysis after repairing lead time
Property and maintenance behavior economics propose average effective maintenance cost function, as machine group parts each in wind power plant formulate maintenance determine
The foundation of plan；And consider maintenance and economical dependence between machine group parts, by the way that an opportunity maintenance section is arranged, for not yet
Reach but its deterioration state is close to the machine group parts of maintenance requirement, one and other machine group parts common implementing maintenances are provided
Chance.
Embodiment is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, it is all according to
Technical idea proposed by the present invention, any changes made on the basis of the technical scheme are fallen within the scope of the present invention.
Claims (7)
1. a kind of marine wind electric field O&M dispatching method based on status monitoring, the marine wind electric field includes several Taiwan Straits windwards
Power generator group, every offshore wind turbine include several machine group parts, which comprises the following steps:
(1) monitoring data relevant to each offshore wind farm machine group parts deterioration state are acquired, and construct corresponding artificial neural network
Network model is to predict the remaining life of each machine group parts；
(2) remaining life reliability of each machine group parts of comprehensive analysis after repairing lead time and maintenance behavior economics, building
The effective maintenance cost function of the consecutive mean of each machine group parts, and as the foundation of each machine group parts formulation maintenance decision；
(3) State Maintenance threshold value and opportunity maintenance threshold value are set, and the consecutive mean of each machine group parts of step (2) building is had
Effect maintenance cost is compared with the two threshold values, to formulate optimal O&M operation plan.
2. the marine wind electric field O&M dispatching method based on status monitoring according to claim 1, which is characterized in that in step
(1) in, the artificial neural network includes:
Model parameter is chosen, and selected mode input parameter is labour of the offshore wind farm machine group parts currently with the previous monitoring moment
Age and multiple Condition Monitoring Data measured values；Selected model output parameters are the service life hundred of offshore wind farm machine group parts
Divide ratio；
Model construction, constructed model are one with multi input and the BP network model singly exported；
Model training, used training algorithm are Levenberg-Marquardt algorithm.
3. the marine wind electric field O&M dispatching method based on status monitoring according to claim 1, which is characterized in that in step
(2) in, the effective maintenance cost function of consecutive mean is as follows:
In above formula,For the average effective maintenance cost of offshore wind farm unit i inner part j；ti,jInside offshore wind farm unit i
The enlistment age of part j；tLTo repair lead time；Indicate the predicting residual useful life value of offshore wind farm unit i inner part j；For sea
The preventative maintenance cost of Wind turbines i inner part j；For the breakdown maintenance cost of offshore wind farm unit i inner part j；P(*)
Indicate probability.
4. the marine wind electric field O&M dispatching method based on status monitoring according to claim 3, which is characterized in that sea turn
The predicting residual useful life value of motor group i inner part jDistribution it is as follows:
Wherein,For the service life percentage of artificial nerve network model prediction；up,jAnd σp,jRespectively artificial neural network
The average value and standard deviation of mould life percent prediction error.
5. the marine wind electric field O&M dispatching method based on status monitoring according to claim 1, which is characterized in that in step
(3) in, the effective maintenance cost function of the consecutive mean of each machine group parts is compared with two threshold values, to formulate optimal fortune
Tieing up operation plan, detailed process is as follows:
When the effective maintenance cost of the consecutive mean of machine group parts is less than State Maintenance threshold value, expression is comprehensive to measure current machine group parts
Remaining life reliability, maintenance cost and enlistment age, the performance state of machine group parts executes prevention after repairing lead time at this time
Property maintenance behavior opportunity be it is suitable, will determine the State Maintenance decision of the machine group parts；
When the effective maintenance cost of the consecutive mean of machine group parts is more than or equal to State Maintenance threshold value and is less than or equal to opportunity maintenance threshold
Value, indicates comprehensive and measures the machine group parts current remaining life reliability, maintenance cost and enlistment age, determines with State Maintenance is determined
The machine group parts of plan execute together maintenance be it is economical, the opportunity maintenance plan of the machine group parts will be formulated；
When the effective maintenance cost of the consecutive mean of machine group parts be greater than opportunity maintenance threshold value, any maintenance behavior will not be executed；
Determining O&M operation plan according to the method described above, will all implement common maintenance after the maintenance lead time of variation；
When all qualified machine group parts all execute common maintenance after repairing lead time, this maintenance cycle knot is indicated
Beam, it is effective that continuous service is monitored the machine group parts consecutive mean at moment by each machine group parts until failure generation next time or newly
Maintenance cost is less than State Maintenance threshold value.
6. the marine wind electric field O&M dispatching method based on status monitoring according to claim 5, which is characterized in that in step
(3) in, the State Maintenance threshold value and opportunity maintenance threshold value are rule of thumb or emulation experiment determines.
7. the marine wind electric field O&M dispatching method based on status monitoring according to claim 6, which is characterized in that using imitative
True experiment determines that the method for State Maintenance threshold value and opportunity maintenance threshold value is as follows:
Establish following marine wind electric field O&M scheduling optimization model:
Above formula is that the year O&M cost of marine wind electric field minimizes objective function, CE(cc,co) be marine wind electric field year O&M at
This, ccAnd coRespectively State Maintenance threshold value and opportunity maintenance threshold value, trAnd tqIt is the q times maintenance cycle and the r times maintenance respectively
The time that period starts；TCkFor total O&M cost in marine wind electric field single maintenance cycle, TCk=TCR+TCC+TCO+
Cfixed, when unit component malfunction any in marine wind electric field, will determine the correction maintenance plan of the machine group parts, and generate
Correction maintenance costWherein,Be offshore wind farm unit i inner part j breakdown maintenance at
This,It is binary variable, indicates whether the component j of i in unit breaks down, value is 1 or 0,It is to enter offshore wind farm
The fixed cost of unit i, IiIt is binary variable, is expressed as Pit Crew and whether enters in offshore wind farm unit i to execute maintenance
Activity, value are 1 or 0, and N is Wind turbines number, and M is the component count in each Wind turbines；When the consecutive mean of machine group parts
When effective maintenance cost is less than State Maintenance threshold value, the State Maintenance plan of the machine group parts will be formulated, and generate State Maintenance
CostWherein,It is the preventative maintenance cost of unit i inner part j,It is that binary system becomes
Amount, indicates whether machine group parts meet the condition for formulating State Maintenance, and value is 1 or 0；When the consecutive mean of machine group parts is effective
Maintenance cost is more than or equal to State Maintenance threshold value and is less than or equal to opportunity maintenance threshold value, will determine the opportunity maintenance of the machine group parts
Plan, and generate opportunity maintenance costWherein,For the preventative dimension of unit i inner part j
Accomplish this,It is binary variable, indicates whether machine group parts meet the condition for formulating State Maintenance, value is 1 or 0；When opening
The dynamic Single Maintenance period will generate the fixed cost C that O&M team is once transported to by O&M ship marine wind electric fieldfixed；
Above formula is maintenance necessity constraint, is meant that in marine wind electric field in any maintenance cycle on every Taiwan Straits in Wind turbines most
Often has the failure of a component；
Above formula is maintainability constraint, is meant that the maintenance personal's limited amount possessed in marine wind electric field, causes repairing
During execute maintenance ability to work have limitation；
By emulating the operating parameter of practical marine wind electric field, it is corresponding optimal to solve above-mentioned model middle age O&M cost minimum
Solution, as State Maintenance threshold value and opportunity maintenance threshold value.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612219A (en) * | 2020-04-24 | 2020-09-01 | 明阳智慧能源集团股份公司 | Wind power generation prediction system |
| CN111817880A (en) * | 2020-06-17 | 2020-10-23 | 安徽创米信息技术有限公司 | Oil and gas field production equipment health management system and implementation method |
| CN113627706A (en) * | 2020-05-06 | 2021-11-09 | 株式会社 A2M | Maintenance support operation management platform device of wind driven generator based on dispatcher |
| CN113919965A (en) * | 2020-07-10 | 2022-01-11 | 上海电动工具研究所（集团）有限公司 | Model training method and system, energy storage inverter and prediction method based on energy storage inverter |
| CN115358639A (en) * | 2022-10-20 | 2022-11-18 | 国网山东省电力公司烟台供电公司 | Offshore wind power operation risk analysis system based on data analysis |
| US11635060B2 (en) | 2021-01-20 | 2023-04-25 | General Electric Company | System for operating a wind turbine using cumulative load histograms based on actual operation thereof |
| US11661919B2 (en) | 2021-01-20 | 2023-05-30 | General Electric Company | Odometer-based control of a wind turbine power system |
| US11728654B2 (en) | 2021-03-19 | 2023-08-15 | General Electric Renovables Espana, S.L. | Systems and methods for operating power generating assets |
| WO2023226539A1 (en) * | 2022-05-25 | 2023-11-30 | 江苏科技大学 | Offshore wind farm multi-unit operation and maintenance strategy optimization method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011112627A1 (en) * | 2011-09-06 | 2013-03-07 | Robert Bosch Gmbh | Method for monitoring and operating wind energy plant within wind farm, involves determining mechanical load of energy plant by evaluating device, and providing control variables of energy plant to control device based on measured variables |
| CN109301852A (en) * | 2018-11-23 | 2019-02-01 | 武汉理工大学 | A kind of micro-capacitance sensor classification united economic load dispatching method of multiple target |
| CN109359742A (en) * | 2018-06-27 | 2019-02-19 | 广州地铁集团有限公司 | A kind of generation method in subway subsystem preventive maintenance period |
-
2019
- 2019-04-09 CN CN201910280153.9A patent/CN110059872A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011112627A1 (en) * | 2011-09-06 | 2013-03-07 | Robert Bosch Gmbh | Method for monitoring and operating wind energy plant within wind farm, involves determining mechanical load of energy plant by evaluating device, and providing control variables of energy plant to control device based on measured variables |
| CN109359742A (en) * | 2018-06-27 | 2019-02-19 | 广州地铁集团有限公司 | A kind of generation method in subway subsystem preventive maintenance period |
| CN109301852A (en) * | 2018-11-23 | 2019-02-01 | 武汉理工大学 | A kind of micro-capacitance sensor classification united economic load dispatching method of multiple target |
Non-Patent Citations (2)
| Title |
|---|
| ZHIGANG TIAN: "Condition based maintenance optimization for wind power generation systems under continuous monitoring", 《RENEWABLE ENERGY》 * |
| 张路朋: "风电机组的状态机会维修策略", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111612219A (en) * | 2020-04-24 | 2020-09-01 | 明阳智慧能源集团股份公司 | Wind power generation prediction system |
| CN113627706A (en) * | 2020-05-06 | 2021-11-09 | 株式会社 A2M | Maintenance support operation management platform device of wind driven generator based on dispatcher |
| CN113627706B (en) * | 2020-05-06 | 2024-05-10 | 株式会社AtwoM | Maintenance support operation management platform device of wind driven generator based on scheduler |
| CN111817880A (en) * | 2020-06-17 | 2020-10-23 | 安徽创米信息技术有限公司 | Oil and gas field production equipment health management system and implementation method |
| CN113919965A (en) * | 2020-07-10 | 2022-01-11 | 上海电动工具研究所（集团）有限公司 | Model training method and system, energy storage inverter and prediction method based on energy storage inverter |
| US11635060B2 (en) | 2021-01-20 | 2023-04-25 | General Electric Company | System for operating a wind turbine using cumulative load histograms based on actual operation thereof |
| US11661919B2 (en) | 2021-01-20 | 2023-05-30 | General Electric Company | Odometer-based control of a wind turbine power system |
| US11728654B2 (en) | 2021-03-19 | 2023-08-15 | General Electric Renovables Espana, S.L. | Systems and methods for operating power generating assets |
| WO2023226539A1 (en) * | 2022-05-25 | 2023-11-30 | 江苏科技大学 | Offshore wind farm multi-unit operation and maintenance strategy optimization method |
| CN115358639A (en) * | 2022-10-20 | 2022-11-18 | 国网山东省电力公司烟台供电公司 | Offshore wind power operation risk analysis system based on data analysis |
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