CN101858312B - System and method thereof for evaluating real-time running state of wind generating set - Google Patents

Abstract

The invention relates to the technical field of running state monitoring of wind generating sets, in particular to a system and a method for evaluating the running state of a wind generating set. The evaluating system mainly comprises a monitoring module, a quantifying module, a weight confirmation module and an evaluating module; data monitored in real time by a wind generating set control system are utilized as the input of the evaluating system of the real-time running state; then the data are quantified to obtain a real time deterioration degree of each evaluating index; finally, when the deterioration degree of a single item evaluating index and a permissible value have greater deviation, the serious evaluating result is directly given, otherwise, the weight module and the evaluating module are used for calculating an evaluating result of the running state of the generating set, and the evaluating result is used as the output of the system. The invention provides scientific basis for state maintenance of the wind generating set, and provides technological supports for ensuring high-efficient, reliable and safe running of the wind generating set, evaluating and forecasting the operational reliability of an electric power system of a windy electric field, and has important engineering application value.

Description

Real-time running state of wind generating set evaluating system and appraisal procedure

Technical field

The present invention relates to a kind of wind power generating set running state assessment technology field, be specifically related to wind power generating set running state evaluating system and appraisal procedure.

Background technique

Along with the wind-powered electricity generation electric motor power rapid growth of China, the total installation of generating capacity ratio increases year by year, and single-machine capacity is that MW class Large-scale Wind Turbines and megawatt wind energy turbine set up to a hundred are just developed rapidly; Along with the maturation relatively of land wind power technology and the huge DEVELOPMENT PROSPECT of sea turn resource, the construction of big capacity wind power plant is just developed to the coastal waters by land, even to zone, deep-sea Development Trend.Compare the land wind power generating set, the offshore wind farm unit will face the operation expense of worse running environment condition and Geng Gao.160MW Horns Rev wind energy turbine set like Denmark's construction in 2002 and operation; At initial operating stage; The safety system of wind power generating set, automatical control system, transformer etc. have all occurred than multiple faults; Only at 2003 to 2004 run durations, every of 80 typhoon power generator groups almost average every day safeguards that so high field failure maintenance rate is that operator's Elsam unanticipated arrives 2 times.Therefore; The running state of timely comprehensive and accurate monitoring and assessment grid connected wind power unit; Effectively avoid the generation of fault and cascading failure, being incorporated into the power networks safely and efficiently with realization large-scale wind generator set for the maintenance policy of optimizing wind energy turbine set has important practical significance.

Based on China's development and the planning of construction intelligent grid and the proposition of low-carbon economy strategic objective; Reliably, on a large scale utilizing various renewable energy sourcess how safely, is one of current challenges that face; And wind-power electricity generation has a kind of generation mode of large-scale development and utilization just in China's renewable energy sources most, and single-machine capacity is that MW class Large-scale Wind Turbines and megawatt wind energy turbine set up to a hundred are just developed rapidly.Along with the progressively exploitation of the ripe relatively and wind energy on the sea resource of the land wind generating technology of China, greatly the construction of capacity wind power plant just by land to coastal waters and deep-sea regional development.Compare the land wind power generating set, the offshore wind generating group will face the maintenance cost of worse running environment, maintenance condition and Geng Gao.160MW Horns Rev wind energy turbine set like Denmark's construction in 2002 and operation; At initial operating stage; The safety system of wind power generating set, automatical control system, transformer etc. have all occurred than multiple faults, and only at 2003 to 2004 run durations, 80 typhoon power generator groups are safeguarded 2 times almost average every every day; Directly cause 2004 to be close on 4000 ten thousand loss, so high field failure maintenance rate and the high maintenance cost that brings are that operator's unanticipated arrives.Therefore; How to improve wind power generating set (being called for short the wind-powered electricity generation unit) availability; Reduce operation expense; Guarantee the high reliability of offshore wind farm unit, become the key issue that China's development offshore wind farm technology presses for solution, also will become important technology and economic indicator that China offshore wind farm unit MANUFACTURER and wind energy turbine set operator are pursued simultaneously.Particularly, aspects such as wind-powered electricity generation operating states of the units comprehensive analysis, fault diagnosis and reliability consideration are expected to become China's wind-powered electricity generation industry new growth point along with China's wind-powered electricity generation unit is installed the swift and violent increase of quantity and the development of offshore wind farm unit.

Summary of the invention

In view of this,, the invention discloses a kind of real-time running state of wind generating set evaluating system, can carry out online evaluation real-time running state of wind generating set in order to address the above problem.

The real-time running state of wind generating set evaluating system comprises

The wind power generating set monitoring modular is used for obtaining from wind turbine control system the Monitoring Data of each evaluation index physical quantity of operating states of the units;

Quantization modules, the Monitoring Data of each evaluation index physical quantity of operating states of the units that the wind power generating set monitoring modular is obtained is carried out quantification treatment, obtains the impairment grade of wind power generating set running state evaluation index;

The weights determination module is divided into a plurality of sub-projects with wind power generating set running state evaluation index, according to the impairment grade of wind power generating set running state evaluation index, calculates the weights of wind power generating set running state evaluation index in real time;

The fuzzy evaluation module constitutes the fuzzy evaluation matrix with the wind power generating set running state evaluation index in each project, in conjunction with the weights of each wind power generating set running state evaluation index, and the assessment real-time running state of wind generating set.

Further, the wind power generating set running state evaluation index physical quantity obtained from wind turbine control system of said wind power generating set monitoring modular comprises box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency;

Further, said quantization modules is to more little excellent more type index: the quantification of box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, cabin vibration acceleration, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree is carried out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<\mathrm{\&alpha;}\\ \frac{x-\mathrm{\&alpha;}}{\mathrm{\&beta;}-\mathrm{\&alpha;}},& \mathrm{\&alpha;}\&le;x\&le;\mathrm{\&beta;}\\ 1,& x>\mathrm{\&beta;}\end{array}\right.;$

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, and α is this index lower limit, and β is the CLV ceiling limit value of this index;

To the middle type index: the quantification of position, cabin, ambient wind velocity, generator speed, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency is carried out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}1,& x<{\mathrm{\&alpha;}}_1\\ \frac{x-{\mathrm{\&alpha;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&alpha;}}_1},& {\mathrm{\&alpha;}}_1\&le;x<{\mathrm{\&beta;}}_1\\ 0,& {\mathrm{\&beta;}}_1\&le;x\&le;{\mathrm{\&alpha;}}_2\\ \frac{{\mathrm{\&beta;}}_2-x}{{\mathrm{\&beta;}}_2-{\mathrm{\&alpha;}}_2},& a_2<x\&le;{\mathrm{\&beta;}}_2\\ 1,& x>{\mathrm{\&beta;}}_2\end{array}\right.;$

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, wherein, β ₁And α ₂Be index good value, α ₁And β ₂Be respectively evaluation index lower limit and CLV ceiling limit value;

Further, said weights determination module is confirmed the normal weights of each wind power generating set running state evaluation index, and obtains the change weights of each wind power generating set running state evaluation index through following formula:

$A_{R_{\mathrm{ijk}}}({\mathrm{<figure-callout\; id="1"\; label="g\; R\; ij"\; filenames="HSA00000145202000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{R_{\mathrm{ij}}},...,g_{R_{\mathrm{ijm}}})=A_{R_{\mathrm{ijk}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijk}}})}^{\mathrm{\&delta;}-1}/\underset{s=1}{\overset{d}{\mathrm{\&Sigma;}}}{A}_{R_{\mathrm{ijs}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijs}}})}^{\mathrm{\&delta;}-1}$

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, δ=-1; D is the judging quota number that comprises in the sub-project layer;

representes the impairment grade of each judging quota.

The present invention also provides a kind of real-time running state of wind generating set appraisal procedure, comprises the steps:

1) obtains wind power generating set running state evaluation index physical quantity from wind power generating set;

2) the wind power generating set running state evaluation index physical quantity data that the wind power generating set monitoring modular obtained are carried out quantification treatment, obtain the impairment grade of wind power generating set running state evaluation index;

3) engineering design of application wind-powered electricity generation unit and maintenance record; The statistics of unit rate of fault is a foundation; The normal weights of each level of unit during the binding hierarchy analytic method is confirmed; According to the impairment grade of wind-powered electricity generation unit real-time running state evaluation index, calculate the change weights of wind power generating set running state evaluation index in real time again;

4) adopt fuzzy comprehensive evaluation method, set up wind-powered electricity generation operating states of the units assessment models, obtain the wind-powered electricity generation operating states of the units.

Further; In the said step 1), obtain wind power generating set running state evaluation index physical quantity from wind power generating set and comprise box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency;

Further; Said step 2) in, to more little excellent more type index: the quantification of box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree is carried out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<\mathrm{\&alpha;}\\ \frac{x-\mathrm{\&alpha;}}{\mathrm{\&beta;}-\mathrm{\&alpha;}},& \mathrm{\&alpha;}\&le;x\&le;\mathrm{\&beta;}\\ 1,& x>\mathrm{\&beta;}\end{array}\right.;$

The impairment grade of g in the formula (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, and α is this index lower limit, and β is the CLV ceiling limit value of this index;

To the middle type index: the quantification of position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency, carry out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}1,& x<{\mathrm{\&alpha;}}_1\\ \frac{x-{\mathrm{\&alpha;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&alpha;}}_1},& {\mathrm{\&alpha;}}_1\&le;x<{\mathrm{\&beta;}}_1\\ 0,& {\mathrm{\&beta;}}_1\&le;x\&le;{\mathrm{\&alpha;}}_2\\ \frac{{\mathrm{\&beta;}}_2-x}{{\mathrm{\&beta;}}_2-{\mathrm{\&alpha;}}_2},& a_2<x\&le;{\mathrm{\&beta;}}_2\\ 1,& x>{\mathrm{\&beta;}}_2\end{array}\right.;$

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, wherein, β ₁And α ₂Be index good value, α ₁And β ₂Be respectively evaluation index lower limit and CLV ceiling limit value;

Further; In the said step 3); Box bearing temperature in the wind power generating set running state evaluation index, gear-box returning-oil temperature, gear-box oil sump temperature are divided into gear-box state subgroup project; Dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed are divided into generator state subgroup project; Reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree are divided into control cabinet state subgroup project; Position, cabin, cabin vibration acceleration are divided into the cabin sub-project, and ambient wind velocity, ambient temperature are divided into the environmental factor sub-project, and phase voltage, phase current, wattless power, active power and mains frequency are divided into electrical network factor sub-project;

Further, in the said step 3), when a certain evaluation index impairment grade of wind power generating set running state less than 0.9 the time, confirm the change weights of each wind power generating set running state evaluation index through following formula:

$A_{R_{\mathrm{ijk}}}({\mathrm{<figure-callout\; id="1"\; label="g\; R\; ij"\; filenames="HSA00000145202000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{R_{\mathrm{ij}}},...,g_{R_{\mathrm{ijm}}})=A_{R_{\mathrm{ijk}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijk}}})}^{\mathrm{\&delta;}-1}/\underset{s=1}{\overset{d}{\mathrm{\&Sigma;}}}{A}_{R_{\mathrm{ijs}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijs}}})}^{\mathrm{\&delta;}-1}$

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, δ=-1; D is the judging quota number that comprises in the sub-project layer;

representes the impairment grade of each judging quota;

Further, said step 4) specifically comprises the steps:

41) setting real-time running state of wind generating set assessment comment is " well ", " qualified ", " attention ", " seriously " 4 kinds of situation;

42) definite degree of membership of passing judgment on each judging quota in the matrix;

43) the evaluation index R that comprises with the sub-project layer among the real-time running state of wind generating set evaluation index layered architecture figure _IjkThe wind-powered electricity generation unit is assessed the state l that comment is concentrated _iThe degree of membership of (i=1,2,3,4) is v _IjMembership grade sets V is used in (j=1,2,3,4) _i=[V _I1, V _I2, V _I3, V _I4] expression presses evaluation index R _IjkThe result of assessment, all judging quotas under the sub-project have promptly just constituted its fuzzy evaluation matrix; R wherein _IjkBe k judging quota of the j sub-project in i the project;

Whether the impairment grade of 44) judging wind power generating set running state evaluation index greater than 0.9, and in this way, assessment result is serious, and as not, then execution in step 45);

45) adopt fuzzy comprehensive evoluation, its representation is: B=A _R&V _R, wherein & is the generalized fuzzy operator, A _RBe variable weight, obtaining assessed value b through fuzzy comprehensive evoluation _jAfter (j=1,2,3,4), adopt maximum membership grade principle to get maximum assessed value b _Max=max (b _j| j=1,2,3,4) corresponding l _iAs assessment result.

The invention has the beneficial effects as follows: real-time running state of wind generating set evaluating system disclosed by the invention and appraisal procedure: with the online physical quantity of monitoring of wind power generating set as input; Obtain the output of operating states of the units through specific running state assessment models; May be used in the control system of wind power generating set; Realize wind power generating set running state real-time assessment; For the repair based on condition of component of wind power generating set provides scientific basis, for guaranteeing that wind power generating set is efficient, reliable, safe operation has important engineering application value, the Operation of Electric Systems reliability that also contains wind energy turbine set for assessment and prediction provides technical support; The running state evaluation index system that the present invention sets up the level type from the unit performance that influences the wind power generating set running state and 2 aspects of external factor; Calculate the weights of running state evaluation index in real time; Can make assessment result press close to current operating conditions; Impairment grade through judging the individual event evaluation index departs from situation, can avoid when multinomial running state evaluation index substantial deviation the invalid problem of change weights of gained; The fuzzy operator of selecting for use has been considered the influence of main evaluation index to running state, keeps the full detail of single evaluation index again, makes assessment result more tally with the actual situation.

Description of drawings

In order to make the object of the invention, technological scheme and advantage clearer, will combine accompanying drawing that the present invention is made further detailed description below:

Fig. 1 shows the structural representation of real-time running state of wind generating set evaluating system;

Fig. 2 shows real-time running state of wind generating set evaluation index layered architecture figure;

Fig. 3 shows half trapezoidal and triangle Membership Function Distribution figure;

Fig. 4 shows the schematic flow sheet of real-time running state of wind generating set appraisal procedure.

Embodiment

Below will combine accompanying drawing, the preferred embodiments of the present invention will be carried out detailed description.

Referring to Fig. 1, the real-time running state of wind generating set evaluating system comprises wind power generating set monitoring modular, quantization modules, weights determination module and fuzzy evaluation module:

The wind power generating set monitoring modular; Be used for obtaining the Monitoring Data of each evaluation index physical quantity of operating states of the units, comprise box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency from wind turbine control system;

Quantization modules, the Monitoring Data of each evaluation index physical quantity of operating states of the units that the wind power generating set monitoring modular is obtained is carried out quantification treatment, obtains the impairment grade of wind power generating set running state evaluation index;

The weights determination module is divided into a plurality of sub-projects with wind power generating set running state evaluation index, according to the impairment grade of wind power generating set running state evaluation index, calculates the weights of wind power generating set running state evaluation index in real time; Said weights comprise normal weights and become weights; Normal weights can the wind-powered electricity generation unit engineering design and maintenance record; The statistics of unit rate of fault is a foundation; The normal weights of each level of unit during the binding hierarchy analytic method is confirmed once more then through following formula, become power to each layer evaluation index and improve on the basis that normal weights are confirmed:

$A_{R_{\mathrm{ijk}}}({\mathrm{<figure-callout\; id="1"\; label="g\; R\; ij"\; filenames="HSA00000145202000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{R_{\mathrm{ij}}},...,g_{R_{\mathrm{ijm}}})=A_{R_{\mathrm{ijk}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijk}}})}^{\mathrm{\&delta;}-1}/\underset{s=1}{\overset{d}{\mathrm{\&Sigma;}}}{A}_{R_{\mathrm{ijs}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijs}}})}^{\mathrm{\&delta;}-1}$

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, δ=-1; D is the evaluation index number that comprises in the sub-project layer;

representes the impairment grade of each evaluation index;

The fuzzy evaluation module constitutes the fuzzy evaluation matrix with the wind power generating set running state evaluation index in each project, in conjunction with the weights of each wind power generating set running state evaluation index, and the assessment real-time running state of wind generating set.

At first; Use of the input of the real-time data monitored of control system of wind turbines as the real-time running state evaluating system; Through these data are quantized, obtain the impairment grade of each real-time judging quota once more, last;, directly provide evaluation result and be " seriously " when big (g >=0.9) as single index impairment grade and permitted value deviation.Otherwise weights module and evaluation module calculate other assessment result of operating states of the units, promptly as the output of system.Function in the face of these several modules is described in detail down.

(1) monitoring modular

The characteristic quantity that record and storage wind-powered electricity generation operating states of the units evaluating system record and storage wind-powered electricity generation operating states of the units evaluating system require comprises box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency.

(2) quantization modules

1) to writing down and stores the limit value module of each judging quota.

To more little excellent more type judging quota: the quantification of box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, cabin vibration acceleration, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree is carried out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<\mathrm{\&alpha;}\\ \frac{x-\mathrm{\&alpha;}}{\mathrm{\&beta;}-\mathrm{\&alpha;}},& \mathrm{\&alpha;}\&le;x\&le;\mathrm{\&beta;}\\ 1,& x>\mathrm{\&beta;}\end{array}\right.;$

In the formula, the impairment grade of g (x) wind power generating set running state evaluation index, x is the physical quantity of wind power generating set running state evaluation index, and α is this index good value, and β is the CLV ceiling limit value of this index;

Quantification to position, middle type index cabin, ambient wind velocity, generator speed, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency is carried out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}1,& x<{\mathrm{\&alpha;}}_1\\ \frac{x-{\mathrm{\&alpha;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&alpha;}}_1},& {\mathrm{\&alpha;}}_1\&le;x<{\mathrm{\&beta;}}_1\\ 0,& {\mathrm{\&beta;}}_1\&le;x\&le;{\mathrm{\&alpha;}}_2\\ \frac{{\mathrm{\&beta;}}_2-x}{{\mathrm{\&beta;}}_2-{\mathrm{\&alpha;}}_2},& a_2<x\&le;{\mathrm{\&beta;}}_2\\ 1,& x>{\mathrm{\&beta;}}_2\end{array}\right.;$

In the formula, the impairment grade of g (x) wind power generating set running state evaluation index, x is the physical quantity of wind power generating set running state evaluation index, α ₁, β ₁, α ₂And β ₂Be the interval endpoint value, wherein, α ₁And β ₂Be respectively evaluation index lower limit and CLV ceiling limit value.

2) online calculate the impairment grade module of each judging quota.

(3) weights determination module

1) the normal weights record of each level judging quota of wind-powered electricity generation unit storage module;

Through to the engineering design of wind-powered electricity generation unit and maintenance record, and to combine the statistics of unit rate of fault be foundation, and the normal weights of each level of unit were seen shown in the table 1 during binding hierarchy analytic method (AHP) was confirmed.

The normal weights of table 1 evaluation index

2) become the weights computing module.

Because of the wind-powered electricity generation unit switchover operation under the different operating conditionss of being everlasting, the impairment grade of each judging quota will change with the variation of running state.Given this, plan is becoming the power improvement to each layer judging quota on the fixed basis of normal power.The change weights of each judging quota into:

$A_{R_{\mathrm{ijk}}}({\mathrm{<figure-callout\; id="1"\; label="g\; R\; ij"\; filenames="HSA00000145202000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{R_{\mathrm{ij}}},...,g_{R_{\mathrm{ijm}}})=A_{R_{\mathrm{ijk}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijk}}})}^{\mathrm{\&delta;}-1}/\underset{s=1}{\overset{d}{\mathrm{\&Sigma;}}}{A}_{R_{\mathrm{ijs}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijs}}})}^{\mathrm{\&delta;}-1}$

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, and this paper gets δ=-1.D is the judging quota number that comprises in the sub-project layer.

(4) evaluation module

1) impairment grade monitoring modular

The division of comment collection is to be " well ", " qualified ", " attention ", " seriously " 4 kinds of situation.

, directly provide evaluation result and be " seriously " when big (g >=0.9) as single index impairment grade and permitted value deviation.

2) pass judgment on matrix module

See shown in the accompanying drawing 4, when g＜0.9, otherwise again through confirm to become weights with pass judgment on the evaluation result that matrix draws other, promptly adopt the fuzzy comprehensive evoluation representation to be: B=A _R&V _R, wherein & is the generalized fuzzy operator, A _RBe variable weight.This patent selects the weighted mean pattern to stick with paste operator; With M (+;) expression, i.e.

(j=1,2; 3..., n).In addition, obtaining assessed value b through fuzzy comprehensive evoluation _jAfter (j=1,2,3,4), adopt maximum membership grade principle to get maximum assessed value b _Max=max (b _j| j=1,2,3,4) corresponding l _iAs assessment result.

Referring to Fig. 4, the real-time running state of wind generating set appraisal procedure of present embodiment comprises the steps:

1) obtains wind power generating set running state evaluation index physical quantity from wind power generating set, comprise box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency;

2) the wind power generating set running state evaluation index physical quantity data that the wind power generating set monitoring modular obtained are carried out quantification treatment, obtain the impairment grade of wind power generating set running state evaluation index;

To more little excellent more type index: the quantification of box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree is carried out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}0,& x<\mathrm{\&alpha;}\\ \frac{x-\mathrm{\&alpha;}}{\mathrm{\&beta;}-\mathrm{\&alpha;}},& \mathrm{\&alpha;}\&le;x\&le;\mathrm{\&beta;}\\ 1,& x>\mathrm{\&beta;}\end{array}\right.;$

The impairment grade of g in the formula (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, and α is this index lower limit, and β is the CLV ceiling limit value of this index;

To the middle type index: the quantification of position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency, carry out through following formula respectively:

$g\left(x\right)=\left\{\begin{array}{cc}1,& x<{\mathrm{\&alpha;}}_1\\ \frac{x-{\mathrm{\&alpha;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&alpha;}}_1},& {\mathrm{\&alpha;}}_1\&le;x<{\mathrm{\&beta;}}_1\\ 0,& {\mathrm{\&beta;}}_1\&le;x\&le;{\mathrm{\&alpha;}}_2\\ \frac{{\mathrm{\&beta;}}_2-x}{{\mathrm{\&beta;}}_2-{\mathrm{\&alpha;}}_2},& a_2<x\&le;{\mathrm{\&beta;}}_2\\ 1,& x>{\mathrm{\&beta;}}_2\end{array}\right.;$

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, α ₁, β ₁, α ₂And β ₂Be the interval endpoint value, wherein, α ₁And β ₂Be respectively evaluation index lower limit and CLV ceiling limit value.

3) referring to Fig. 2; Box bearing temperature in the wind power generating set running state evaluation index, gear-box returning-oil temperature, gear-box oil sump temperature are divided into gear-box state subgroup project; Dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed are divided into generator state subgroup project; Reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree are divided into control cabinet state subgroup project; Position, cabin, cabin vibration acceleration are divided into the cabin sub-project; Ambient wind velocity, ambient temperature are divided into the environmental factor sub-project, and phase voltage, phase current, wattless power, active power and mains frequency are divided into electrical network factor sub-project, use the engineering design and maintenance record of wind-powered electricity generation unit; The statistics of unit rate of fault is a foundation; The normal weights of each level of unit again according to the impairment grade of wind-powered electricity generation unit real-time running state evaluation index, calculated the change weights of wind power generating set running state evaluation index in real time during the binding hierarchy analytic method was confirmed; When a certain evaluation index impairment grade of wind power generating set running state less than 0.9 the time, confirm the change weights of each wind power generating set running state evaluation index through following formula:

$A_{R_{\mathrm{ijk}}}({\mathrm{<figure-callout\; id="1"\; label="g\; R\; ij"\; filenames="HSA00000145202000021.png"\; state="\{\{state\}\}">g</figure-callout>}}_{R_{\mathrm{ij}}},...,g_{R_{\mathrm{ijm}}})=A_{R_{\mathrm{ijk}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijk}}})}^{\mathrm{\&delta;}-1}/\underset{s=1}{\overset{d}{\mathrm{\&Sigma;}}}{A}_{R_{\mathrm{ijs}}}^{\left(0\right)}{(1-g_{R_{\mathrm{ijs}}})}^{\mathrm{\&delta;}-1}$

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, δ=-1; D is the judging quota number that comprises in the sub-project layer;

representes the impairment grade of each judging quota;

4) adopt fuzzy comprehensive evaluation method, set up wind-powered electricity generation operating states of the units assessment models, obtain the wind-powered electricity generation operating states of the units:

41) setting real-time running state of wind generating set assessment comment be " well ", " qualified ", " attentions ", " seriously " 4 kinds of situation, promptly L=[well, qualified, attention, seriously]=[l ₁, l ₂, l ₃, l ₄];

42) definite degree of membership of passing judgment on each judging quota in the matrix; Concrete definite square tube of membership function is crossed impairment grade, confirms to set up the membership function of impairment grade for each state grade at last between the fuzzy watershed area of impairment grade for 4 kinds of state grades of Fig. 3 intermediate cam shape and the half trapezoidal distribution function that combines.For example, for box bearing temperature R ₁₁₁Judging quota, its membership function v (g) corresponding to each state can confirm as respectively:

$v_{11}\left(g\right)=\left\{\begin{array}{cc}3.5-5.56g& 0.45<g<0.63\\ 1& g\&le;0.45\\ 0& g\&GreaterEqual;0.63\end{array}\right.$

$v_{14}\left(g\right)=\left\{\begin{array}{cc}1& g\&GreaterEqual;0.85\\ 8.33g-6.08& 0.73<g<0.85\\ 0& g\&le;0.73\end{array}\right.$

In the following formula: v ₁₁(g)～v ₁₄When (g) representing the box bearing impairment grade respectively for g corresponding to state l ₁～l ₄Membership function.In like manner, can obtain the membership function of other judging quota, no longer list at this.

43) the evaluation index R that comprises with the sub-project layer among the real-time running state of wind generating set evaluation index layered architecture figure _IjkThe wind-powered electricity generation unit is assessed the state l that comment is concentrated _iThe degree of membership of (i=1,2,3,4) is v _IjMembership grade sets V is used in (j=1,2,3,4) _i=[v _I1, v _I2, v _I3, v _I4] expression presses evaluation index R _IjkThe result of assessment, all judging quotas under the sub-project have promptly just constituted its fuzzy evaluation matrix; R wherein _IjkBe k judging quota of the j sub-project in i the project; As, with sub-project layer middle gear case R ₁₁Be example, the judge matrix is:

$V_{R_{11}}=\left[\begin{array}{c}{V}_{R_{111}}\\ V_{R_{112}}\\ V_{R_{113}}\end{array}\right]=\left[\begin{array}{cccc}{v}_{11}& v_{12}& v_{13}& v_{14}\\ v_{21}& v_{22}& v_{23}& v_{24}\\ v_{31}& v_{32}& v_{33}& v_{34}\end{array}\right];$

Whether the impairment grade of 44) judging wind power generating set running state evaluation index greater than 0.9, and in this way, assessment result is serious, and as not, then execution in step 45);

45) adopt fuzzy comprehensive evoluation, its representation is: B=A _R&V _R, wherein & is the generalized fuzzy operator, A _RBe variable weight, obtaining assessed value b through fuzzy comprehensive evoluation _jAfter (j=1,2,3,4), adopt maximum membership grade principle to get maximum assessed value b _Max=max (b _j| j=1,2,3,4) corresponding l _iAs assessment result.

The above is merely the present invention that preferably is not limited to of the present invention, and obviously, those skilled in the art can carry out various changes and modification and not break away from the spirit and scope of the present invention the present invention.Like this, belong within the scope of claim of the present invention and equivalent technologies thereof if of the present invention these are revised with modification, then the present invention also is intended to comprise these changes and modification interior.

Claims (10)

1. the real-time running state of wind generating set evaluating system is characterized in that: comprise

The wind power generating set monitoring modular is used for obtaining from wind turbine control system the Monitoring Data of each evaluation index physical quantity of operating states of the units;

Quantization modules, the Monitoring Data of each evaluation index physical quantity of operating states of the units that the wind power generating set monitoring modular is obtained is carried out quantification treatment, obtains the impairment grade of wind power generating set running state evaluation index;

The weights determination module is divided into a plurality of sub-projects with wind power generating set running state evaluation index, according to the impairment grade of wind power generating set running state evaluation index, calculates the weights of wind power generating set running state evaluation index in real time;

The fuzzy evaluation module constitutes the fuzzy evaluation matrix with the wind power generating set running state evaluation index in each project, in conjunction with the weights of each wind power generating set running state evaluation index, and the assessment real-time running state of wind generating set.

2. real-time running state of wind generating set evaluating system as claimed in claim 1 is characterized in that: the wind power generating set running state evaluation index physical quantity that said wind power generating set monitoring modular obtains from wind turbine control system comprises box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency.

3. real-time running state of wind generating set evaluating system as claimed in claim 2 is characterized in that: said quantization modules is to more little excellent more type index: the quantification of box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, cabin vibration acceleration, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree is carried out through following formula respectively:

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, and α is this index lower limit, and β is the CLV ceiling limit value of this index;

To the middle type index: the quantification of position, cabin, ambient wind velocity, generator speed, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency is carried out through following formula respectively:

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, wherein, β ₁And α ₂Be index good value, α ₁And β ₂Be respectively evaluation index lower limit and CLV ceiling limit value.

4. real-time running state of wind generating set evaluating system as claimed in claim 1; It is characterized in that: said weights determination module is confirmed the normal weights of each wind power generating set running state evaluation index, and obtains the change weights of each wind power generating set running state evaluation index through following formula:

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, δ=-1; D is the judging quota number that comprises in the sub-project layer;

representes the impairment grade of each judging quota.

5. the real-time running state of wind generating set appraisal procedure is characterized in that: comprise the steps:

1) obtains wind power generating set running state evaluation index physical quantity from wind power generating set;

2) the wind power generating set running state evaluation index physical quantity data that the wind power generating set monitoring modular obtained are carried out quantification treatment, obtain the impairment grade of wind power generating set running state evaluation index;

3) engineering design of application wind-powered electricity generation unit and maintenance record; The statistics of unit rate of fault is a foundation; The binding hierarchy analytic method is confirmed the normal weights of each level of unit; According to the impairment grade of wind-powered electricity generation unit real-time running state evaluation index, calculate the change weights of wind power generating set running state evaluation index in real time again;

4) adopt fuzzy comprehensive evaluation method, set up wind-powered electricity generation operating states of the units assessment models, obtain the wind-powered electricity generation operating states of the units.

6. real-time running state of wind generating set appraisal procedure as claimed in claim 5; It is characterized in that: in the said step 1), the wind power generating set running state evaluation index physical quantity of obtaining from wind power generating set comprises box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree, position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency.

7. real-time running state of wind generating set appraisal procedure as claimed in claim 6; It is characterized in that: said step 2), to more little excellent more type index: the quantification of box bearing temperature, gear-box returning-oil temperature, gear-box oil sump temperature, dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed, reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree is carried out through following formula respectively:

The impairment grade of g in the formula (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, and α is this index lower limit, and β is the CLV ceiling limit value of this index;

To the middle type index: the quantification of position, cabin, cabin vibration acceleration, ambient wind velocity, ambient temperature, phase voltage, phase current, wattless power, active power and mains frequency, carry out through following formula respectively:

In the formula, the impairment grade of g (x) expression wind power generating set running state evaluation index, x is the Monitoring Data of the physical quantity of wind power generating set running state evaluation index, wherein, β ₁And α ₂Be index good value, α ₁And β ₂Be respectively evaluation index lower limit and CLV ceiling limit value.

8. real-time running state of wind generating set appraisal procedure as claimed in claim 5; It is characterized in that: in the said step 3); Box bearing temperature in the wind power generating set running state evaluation index, gear-box returning-oil temperature, gear-box oil sump temperature are divided into gear-box state subgroup project; Dynamo bearing temperature, generator windings temperature, generator cooling-water temperature, generator speed are divided into generator state subgroup project; Reactor temperature, capacitor temperature, IGBT temperature, rectifier temperature, control cabinet temperature, column foot control cabinet outlet wind-warm syndrome degree are divided into control cabinet state subgroup project; Position, cabin, cabin vibration acceleration are divided into the cabin sub-project; Ambient wind velocity, ambient temperature are divided into the environmental factor sub-project, and phase voltage, phase current, wattless power, active power and mains frequency are divided into electrical network factor sub-project.

9. real-time running state of wind generating set appraisal procedure as claimed in claim 5; It is characterized in that: in the said step 3); When a certain evaluation index impairment grade of wind power generating set running state less than 0.9 the time, confirm the change weights of each wind power generating set running state evaluation index through following formula:

and

is respectively the corresponding normal power weight and change power weight of k index of i the j sub-project in the project in the following formula; δ is for becoming weight coefficient, δ=-1; D is the judging quota number that comprises in the sub-project layer;

representes the impairment grade of each judging quota.

10. like each described real-time running state of wind generating set appraisal procedure in the claim 5 to 9, it is characterized in that: said step 4) specifically comprises the steps:

41) setting real-time running state of wind generating set assessment comment is " well ", " qualified ", " attention ", " seriously " 4 kinds of situation;

42) definite degree of membership of passing judgment on each judging quota in the matrix;

43) with wind power generating set running state evaluation index R _IjkThe wind-powered electricity generation unit is assessed the state l that comment is concentrated _iThe degree of membership of (i=1,2,3,4) is v _IjMembership grade sets V is then used in (j=1,2,3,4) _i=[v _I1, v _I2, v _I3, v _I4] represent by index R _IjkThe result of assessment, all judging quotas under the sub-project have promptly just constituted its fuzzy evaluation matrix; R wherein _IjkBe k judging quota of the j sub-project in i the project;

Whether the impairment grade of 44) judging wind power generating set running state evaluation index greater than 0.9, and in this way, assessment result is serious, and as not, then execution in step 45);

45) adopt fuzzy comprehensive evoluation, its representation is: B=A _R&V _R, wherein & is the generalized fuzzy operator, A _RBe variable weight, obtaining assessed value b through fuzzy comprehensive evoluation _jAfter (j=1,2,3,4), adopt maximum membership grade principle to get maximum assessed value b _Max=max (b _j| j=1,2,3,4) corresponding l _iAs assessment result.

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