CN105787271A - Heat supply unit adjustable power output range evaluation method based on big data analysis technology - Google Patents
Heat supply unit adjustable power output range evaluation method based on big data analysis technology Download PDFInfo
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Abstract
The invention discloses a heat supply unit adjustable power output range evaluation method based on a big data analysis technology.Heat supply unit production and operation big data are classified from three-dimensional angles of the unit heat supply load, the environmental temperature and the equivalent load coal feed rate by using a multidimensional clustering algorithm, then Gaussian distribution is utilized, optimal iterative calculation is conducted according to a set constraint condition, the peak-regulating capacity range of the unit under a specific working condition is evaluated, timely updating of whole power grid adjustable power output can be achieved, it is beneficial for electric power dispatching operation personnel to know changes of unit power generation capacity of a whole province as soon as possible, and stable operation of a power grid is guaranteed.
Description
Technical field
The present invention relates to a kind of adjustable Interval evaluation method of exerting oneself of thermal power plant unit based on big data analysis technique, it is especially the big data analysis technique of a kind of utilization, unit history is carried out depth analysis and then the assessment method that the adjustable of unit generation load is interval under different working conditions for dsc data.
Background technology
Owing to the more traditional heating boiler of Large-sized Coal-fired Power group, small cogeneration unit have the pollutant control facility of higher efficiency of energy utilization, more clean environment firendly, along with deepening constantly of energy-saving and emission-reduction work, related governmental departments is actively promoted domestic Large-sized Coal-fired Power group and is carried out heat supply transformation, substitute periphery tradition heat supply arcola, small cogeneration enterprise, to improve overall efficiency of energy utilization, various pollutant emission is greatly reduced.For Jiangsu Province, by in by the end of June, 2015, the whole province 100MW and above thermal power plant unit capacity reach 38845MW, account for the 61.85% of the whole province's Large-sized Coal-fired Power group total capacity, heating load is close to the 20% of the whole province's coal group of motors heating load, 3180t/h is reached for heat flow, and constantly closing down and replacement along with miniature coal unit, the whole province's Large-sized Coal-fired Power group heating load will keep soaring.
After Large-sized Coal-fired Power group heat supply transformation, the peak modulation capacity of unit there is a definite limitation.It is said that in general, the heat supply amount of drawing gas is more big, maximum generation ability is more little, and minimum generating capacity is more big, and after namely coal unit participates in heat supply, its generating capacity is subjected to a definite limitation.According to incompletely statistics, at present due to factors such as heat supplies, cause that Jiangsu the whole network is exerted oneself and limited be up to about 5000MW, when grid balance plan and power scheduling (particularly summer peak meeting and meet between the kurtosis teletostage), it is necessary to large-scale coal heating unit output limited situation is assessed.
Therefore, in order to meet dispatching of power netwoks balance requirement, country's related governmental departments has put into effect the measure of a series of reinforcement thermal power plant unit peak modulation capacity supervision.Such as, northeast bureau in charge of electricity of National Energy Board just put into effect " Northeast Regional thermal power plant minimum operational mode appraises and decides management Tentative Measures " in 2011, it is intended to improve the standardization of Northeast China Power Grid peak regulation scheduling and scientific level, ensureing on the basis of resident and the basic heating of electricity consumption enterprise, excavate fired power generating unit peak regulation space, alleviate the conspicuous contradiction that Northeast China Power Grid peak modulation capacity declines further.
But in big thermoelecrtic peak load regulation capability evaluation, owing to on-the-spot experiment work is excessively complicated, relate to Operating condition adjustment more, simultaneously, theoretical variable condition calculation working condition chart differs relatively big with unit actual operating data, causes that peak load regulation ability interval accurate evaluation is comparatively difficult.
Summary of the invention
In order to solve above-mentioned technical problem, the invention provides a kind of adjustable Interval evaluation method of exerting oneself of thermal power plant unit based on big data analysis technique.
In order to achieve the above object, the technical solution adopted in the present invention is:
Based on the adjustable Interval evaluation method of exerting oneself of thermal power plant unit of big data analysis technique, comprise the following steps,
Step 1, the history heat supply data set of the given unit of definition is
Wherein,For unit floor data,For data unit operation, M is unit history heat supply data sample number, i ∈ [1, M]；
Step 2, uses multidimensional clustering algorithm, by whole history heat supply data set by different operating modes, is divided into several data subsets S={S1,S2,...,SK}；
Wherein, K is operating mode type number, SjConfession dsc data for jth kind operating mode；
Step 3, adopts Gauss distributionTo unit generation power collection P={p under each operating mode1,p2,...,pnBe described；
Wherein, μPFor average,For variance, n is unit discharge power sample number, p under an operating modelIt is the l discharge power sample of unit under an operating mode, l ∈ [1, n]；
Step 4, to under each operating mode, the confidence interval of the Gauss distribution of unit generation power collection being carried out optimizing iterative computation, makes this confidence interval meet constraints set in advance, the upper and lower limit of this confidence interval is the upper and lower limit that under corresponding operating mode, peak modulation capacity is interval.
The dimension criteria for classifying of multidimensional clustering algorithm includes heating demand, ambient temperature and equivalent load coal-supplying amount.
Multidimensional clustering algorithm is k-means algorithm.
Constraints is,
One, the peak load regulation ability interval upper limit is less than or equal to unit rated load；
Two, peak load regulation ability interval limit is be more than or equal to R% unit rated load；
The minimum oil operating load of not throwing that every unit is appraised and decided by R% according to government department is determined；
Three, the confidence level of confidence interval is be more than or equal to Y%, Y >=90.
The process of optimizing iterative computation is,
A1) definition j=1；
A2) confidence level X=100%-j α is calculated；
Wherein, j α is step-length；
A3) upper and lower limit of confidence interval is calculated；
The upper limit of confidence interval,
Pmax=μp+λ×σP
The lower limit of confidence interval,
Pmax=μp-λ×σP；
Wherein, λ is parameter, λ=tjα/2(n-1) expression searches λ according to j α and n on t-distribution table；
A4) judge whether confidence level X meets constraints one, two respectively equal to the upper and lower limit of Y% or confidence interval, if it is, terminate；Otherwise, j=j+1, go to step A2.
The beneficial effect that the present invention reaches: the present invention uses multidimensional clustering algorithm from unit heating demand, ambient temperature and equivalent load coal-supplying amount three-dimensional perspective, the big data of thermal power plant unit production run are divided, then Gauss distribution is used, optimizing iterative computation is carried out according to the constraints set, under assessment specific operation, the peak modulation capacity of unit is interval, can realize the whole network adjustable exert oneself upgrade in time, power scheduling operations staff is conducive to understand the whole province's unit generation capacity variation situation as early as possible, it is ensured that the stable operation of electrical network.
Accompanying drawing explanation
Fig. 1 is the flow chart of the present invention.
Fig. 2 is the contrast effect figure of the present invention.
Detailed description of the invention
Below in conjunction with accompanying drawing, the invention will be further described.Following example are only for clearly illustrating technical scheme, and can not limit the scope of the invention with this.
As it is shown in figure 1, based on the adjustable Interval evaluation method of exerting oneself of thermal power plant unit of big data analysis technique, comprise the following steps:
Step 1, the history heat supply data set of the given unit of definition is
Wherein,For unit floor data,For data unit operation, M is unit history heat supply data sample number, i ∈ [1, M].
Step 2, uses multidimensional clustering algorithm, by whole history heat supply data set by different operating modes, is divided into several data subsets S={S1,S2,...,SK}；Wherein, K is operating mode type number, SjConfession dsc data for jth kind operating mode.
The dimension criteria for classifying of multidimensional clustering algorithm includes heating demand, ambient temperature and equivalent load coal-supplying amount.Here multidimensional clustering algorithm is k-means algorithm, and this algorithm clusters centered by k point in space, near their object categorization, by the method for iteration, gradually updates the value of each cluster centre, until obtaining best cluster result.
Step 3, adopts Gauss distributionTo unit generation power collection P={p under each operating mode1,p2,...,pnBe described；
Wherein, μPFor average,For variance, n is unit discharge power sample number, p under an operating modelIt is the l discharge power sample of unit under an operating mode, l ∈ [1, n].
Step 4, to under each operating mode, the confidence interval of the Gauss distribution of unit generation power collection being carried out optimizing iterative computation, makes this confidence interval meet constraints set in advance, the upper and lower limit of this confidence interval is the upper and lower limit that under corresponding operating mode, peak modulation capacity is interval.
Constraints is specific as follows:
One, the peak load regulation ability interval upper limit is less than or equal to unit rated load；
Two, peak load regulation ability interval limit is be more than or equal to R% unit rated load；
The minimum oil operating load of not throwing that every unit is appraised and decided by R% according to government department is determined；
Three, the confidence level of confidence interval is be more than or equal to Y%, Y >=90.
In above-mentioned constraints, R is generally 30, Y and is generally 90.
The process of optimizing iterative computation is as follows:
A1) definition j=1；
A2) confidence level X=100%-j α is calculated；
Wherein, j α is step-length, α=0.01；
A3) upper and lower limit of confidence interval is calculated；
The upper limit of confidence interval,
Pmax=μp+λ×σP
The lower limit of confidence interval,
Pmax=μp-λ×σP；
Wherein, λ is parameter, λ=tjα/2(n-1) expression searches λ according to j α and n on t-distribution table；
A4) judge whether confidence level X meets constraints one, two respectively equal to the upper and lower limit of Y% or confidence interval, if it is, terminate；Otherwise, j=j+1, go to step A2.
Said method uses multidimensional clustering algorithm from unit heating demand, ambient temperature and equivalent load coal-supplying amount three-dimensional perspective, the big data of thermal power plant unit production run are divided, then Gauss distribution is used, carrying out optimizing iterative computation according to the constraints set, under assessment specific operation, the peak modulation capacity of unit is interval.
In order to further illustrate said method, with certain 140MW grade supertension thermal power plant unit, it is carried out adjustable interval analysis of exerting oneself as follows:
When unit heat supply running data being carried out hyperspace and dividing, have chosen operating mode Classification Index for consideration:
1, coal characteristic: owing to coal situation is relatively big to systematic influences such as unit powder process, burnings, therefore should give consideration when to thermal power plant unit Analysis of Peak Regulation Capability.
Selecting equivalent load coal-supplying amount as the metric of coal characteristic, its computational methods are,
In formula, G is total coal-supplying amount, and unit is t/h, p is generated output, and unit is MW, ε is equivalent load coal-supplying amount, and unit is t/MWh.
In example, equivalent load coal-supplying amount divides with 0.05t/MWh for interval.
2, atmospheric environment: power generation process needs to obtain heat transmission and the exchange medias such as empty gas and water from natural environment, its impact be should not be underestimated by atmosphere outside, therefore when analyzing thermal power plant unit peak modulation capacity, first have to select the data that in sample data, the external environment condition of unit operation is consistent to be analyzed, under otherwise different external environment conditions, between data, might not have comparability.For this characteristic, in this method, Environment temperature is as the metric of atmospheric environment characteristic, and this index is field monitoring parameter.
In example, ambient temperature divides with 1 DEG C for interval.
3, heating demand: divide for interval with 1t/h.
Processing by data unit operation being carried out cluster analysis, Gauss distribution optimal Confidence Interval search etc., concentrating from mass data the peak modulation capacity extracting unit various operating mode interval.
Figure below is the relative analysis figure that this thermal power plant unit uses big data analysing method, working condition chart method assessment result.In figure, abscissa is that coal unit is for heat flow, vertical coordinate respectively generated output and unit heat supply flow distribution probability, dotted line and realize respectively applying working condition figure method and each the exerting oneself for unit maximum adjustable under heat flow that big data analysis method obtains herein adjustable is exerted oneself with minimum.
As can be seen from the figure, big data analysis method is basically identical with two kinds of methods analyst result general trends of heat supply working condition chart method: two kinds of method assessment peak modulation capacity upper limits all reduce along with the increase for heat flow, and peak modulation capacity lower limit increases along with the increase for heat flow.
But two kinds of methods there is also certain difference, is mainly manifested in:
1, at below 60t/h in thermal condition, the peak modulation capacity lower limit that big data analysis method obtains is lower than working condition chart method result of calculation；
2, at more than 60t/h in thermal condition, the peak modulation capacity lower limit that big data analysis method obtains is higher than working condition chart method result of calculation；Meanwhile, the peak modulation capacity upper limit that big data analysis method obtains is totally higher than working condition chart method result of calculation.
Owing to big data analysis method is the result of calculation obtained according to the actual operating data extraction and analysis that unit is annual, therefore maximum, the minimum load of its output is substantially all reproducible, more true and reliable compared with working condition chart scheduling theory derivation method.
And, along with the continuous accumulation of data unit operation, to apply big data analysing method from various dimensions such as ambient temperature, ature of coal situation, return water temperatures and analyse in depth, its application effect will more accurately and reliably, and assessment result also will be more authentic and valid.
Traditional method, by thermal power plant unit is carried out performance test, prepares to carrying out on-the-spot test from test, consuming time reaches several weeks.And we's rule can complete dependent evaluation work in several hours, and scene is with little need for the test job carrying out any complexity, is respectively provided with irreplaceable advantage from time and cost angle.
In sum, said method can realize the whole network adjustable exert oneself upgrade in time, be conducive to power scheduling operations staff to understand the whole province's unit generation capacity variation situation as early as possible, it is ensured that the stable operation of electrical network.
The above is only the preferred embodiment of the present invention; it should be pointed out that, for those skilled in the art, under the premise without departing from the technology of the present invention principle; can also making some improvement and deformation, these improve and deformation also should be regarded as protection scope of the present invention.
Claims (5)
1. based on the adjustable Interval evaluation method of exerting oneself of the thermal power plant unit of big data analysis technique, it is characterised in that: comprise the following steps,
Step 1, the history heat supply data set of the given unit of definition is
Wherein,For unit floor data,For data unit operation, M is unit history heat supply data sample number, i ∈ [1, M]；
Step 2, uses multidimensional clustering algorithm, by whole history heat supply data set by different operating modes, is divided into several data subsets S={S1,S2,…,SK}；
Wherein, K is operating mode type number, SjConfession dsc data for jth kind operating mode；
Step 3, adopts Gauss distributionTo unit generation power collection P={p under each operating mode1,p2,…,pnBe described；
Wherein, μPFor average,For variance, n is unit discharge power sample number, p under an operating modelIt is the l discharge power sample of unit under an operating mode, l ∈ [1, n]；
Step 4, to under each operating mode, the confidence interval of the Gauss distribution of unit generation power collection being carried out optimizing iterative computation, makes this confidence interval meet constraints set in advance, the upper and lower limit of this confidence interval is the upper and lower limit that under corresponding operating mode, peak modulation capacity is interval.
2. the adjustable Interval evaluation method of exerting oneself of the thermal power plant unit based on big data analysis technique according to claim 1, it is characterised in that: the dimension criteria for classifying of multidimensional clustering algorithm includes heating demand, ambient temperature and equivalent load coal-supplying amount.
3. the adjustable Interval evaluation method of exerting oneself of the thermal power plant unit based on big data analysis technique according to claim 1 and 2, it is characterised in that: multidimensional clustering algorithm is k-means algorithm.
4. the adjustable Interval evaluation method of exerting oneself of the thermal power plant unit based on big data analysis technique according to claim 1, it is characterised in that: constraints is,
One, the peak load regulation ability interval upper limit is less than or equal to unit rated load；
Two, peak load regulation ability interval limit is be more than or equal to R% unit rated load；
The minimum oil operating load of not throwing that every unit is appraised and decided by R% according to government department is determined；
Three, the confidence level of confidence interval is be more than or equal to Y%, Y >=90.
5. the adjustable Interval evaluation method of exerting oneself of the thermal power plant unit based on big data analysis technique according to claim 4, it is characterised in that: the process of optimizing iterative computation is,
A1) definition j=1；
A2) confidence level X=100%-j α is calculated；
Wherein, j α is step-length；
A3) upper and lower limit of confidence interval is calculated；
The upper limit of confidence interval,
Pmax=μp+λ×σP
The lower limit of confidence interval,
Pmax=μp-λ×σP；
Wherein, λ is parameter, λ=tjα/2(n-1) expression searches λ according to j α and n on t-distribution table；
A4) judge whether confidence level X meets constraints one, two respectively equal to the upper and lower limit of Y% or confidence interval, if it is, terminate；Otherwise, j=j+1, go to step A2.
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Inventor after: Sun Shuanzhu Inventor after: Zhou Chunlei Inventor after: Wang Ming Inventor after: Li Chunyan Inventor after: Yang Chenchen Inventor after: Xu Guoqiang Inventor after: Zhou Zhixing Inventor after: She Guojin Inventor after: Jiang Yefeng Inventor after: Zhou Ting Inventor after: Xu Chunlei Inventor after: Yang Zijun Inventor after: Dai Jiayuan Inventor after: Wang Lin Inventor after: Zhang Youwei Inventor after: Li Jie Inventor before: Sun Shuanzhu Inventor before: Dai Jiayuan Inventor before: Zhou Chunlei Inventor before: Wang Lin Inventor before: Zhang Youwei Inventor before: Wang Ming Inventor before: Li Chunyan Inventor before: Yang Chenchen |
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