CN102566551A - Data mining-based method for analyzing thermal power plant operation index optimal target value - Google Patents
Data mining-based method for analyzing thermal power plant operation index optimal target value Download PDFInfo
- Publication number
- CN102566551A CN102566551A CN2012100237215A CN201210023721A CN102566551A CN 102566551 A CN102566551 A CN 102566551A CN 2012100237215 A CN2012100237215 A CN 2012100237215A CN 201210023721 A CN201210023721 A CN 201210023721A CN 102566551 A CN102566551 A CN 102566551A
- Authority
- CN
- China
- Prior art keywords
- value
- operating mode
- thermal power
- optimal objective
- unit
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Feedback Control In General (AREA)
Abstract
The invention relates to a data mining-based method for analyzing a thermal power plant operation index optimal target value. Operation working conditions of a unit are divided according to external working conditions of the unit operation, wherein the working conditions include loads, coal qualities, and circulating water temperatures and the like; on the basis of unit operation massive historical data accumulated by a thermal power plant, by employing a data mining algorithm, operation optimal values of all important parameters on the similar operation working conditions of the unit are found out according to performance indexes including stability, economy and environmental protection and the like; and on the basis of data accumulation and continuous data updating, operation optical values of all operation parameters of the unit are found out and tracked on different working conditions of the unit. And moreover, a concrete method according to which a thermal power plant operation index optimal value is employed as a target value of an evaluation index so as to realize operation evaluation and real-time guidance functions is provided.
Description
Technical field
The invention belongs to technical field of power systems, be specifically related to the analytical approach of a kind of thermal power plant operating index optimal objective value.
Background technology
The analysis of thermal power plant's operating index desired value and calculating are the most key step and the of paramount importance links of thermal power plant's operation performance appraisal system; It provides the operational factor of reflection unit current optimal running status and the desired value of performance index, for the operation optimization operation guidance provides the foundation and foundation.Do not have the support of the correct operating index desired value that is suitable for, the operation performance appraisal has also just lost meaning.
Each operating index of unit all has a running optimal value (desired value), and promptly unit is in following optimum value that can reach of current service condition, in other words conj.or perhaps for obtain maximum economic benefit the ideal value that should reach.But the height of the instantaneous value of index and the degree of closeness quantitative response operation level between the desired value.Therefore; Desired value is to weigh the benchmark of unit operation level; It also is the evaluation criterion of operating index operation quality; The operations staff should be that operation is operated, optimized to target with the deviation of dwindling between instantaneous value and the desired value, and the operation performance appraisal also is a height of estimating operations staff's operation level with the extent of this deviation.
Unit operation target goals value comprises each operational factor of unit current optimal running status and the optimization target values of performance index; It provides optimum operating mode and the parameter control mode of unit under different external conditions (like load, fuel, external environment condition etc.) for the operations staff; The operation optimization target values is based upon on the existing equipment basis and (comprises therrmodynamic system structure, equipment running status etc.); Main through operation adjustment realization, its purpose makes unit be in the optimum state operation exactly always.
Relation from unit operating index desired value and unit operation situation; The unit operation index can be divided into two types: the first kind is that it doesn't matter for the desired value of unit operation index and the load of unit; Such as main stripping temperature, reheat steam temperature degree; When having only its numerical value to be design load, the performance driving economy of unit is best.Therefore, can think that the desired value of these indexs is the design load that manufacturing plant provides.Second type is that the unit operation index changes along with the variation of unit operation operating mode, relevant with the therrmodynamic system architectural feature with unit load, environment temperature, such as station service power consumption rate, flue gas oxygen content, exhaust gas temperature, main vapour pressure.These indexs can reach optimal value through the adjustment of equipment and parameter.
The desired value of unit operation index is to be difficult to confirm in theory in fact, turns to target because it is actually with unit heat consumption index optimum, the multidimensional constraint optimizing problem on unit operation performance state space.The following numerical value of general at present employing is as unit operation target goals value, and different classes of index adopts different desired values:
(1) design basis value.The state that can reach when designing corresponding to unit, typical load that provides according to manufacturer and the design conditions data under the environment temperature obtain the reference value under different load and the condition through model of fit and experimental formula.
(2) maintenance reference value.State corresponding to unit can reach through the maintenance back obtains the reference value under different load and the condition according to unit maintenance back thermal performance test.
(3) calculating should reach value.Under specific load and condition, carry out performance computation and variable working condition according to performance computation model and variable working condition computation model and calculate and desirable should reach value.
Adopt design basis value, maintenance reference value, calculate in the time of to reach value as the desired value of unit operation index; All there are some problems: when adopting the design basis value; Only when the practical operation situation of unit is similar with design conditions, just feasible, otherwise then not too appropriate; When adopting the maintenance reference value, the in the initial stage of that effect is better after maintenance, and working time, elongated back desired value can change to some extent; Adopt and calculate in the time of should reaching value, though be correct in theory, result of calculation receives the influence of model, and in service often being difficult to reaches.
Summary of the invention
Be to solve the problems referred to above that occur in the prior art, the invention discloses a kind of analytical approach of the thermal power plant's operating index optimal objective value based on data mining, said method adopts the desired value of index running optimal value as thermal power plant's operating index.The running optimal value reflection unit of index is at optimum operating mode and following state that can reach of operational factor; Based on data mining technology working condition data such as index and load, environment temperature in a large amount of operation history data being carried out profound level analysis obtains; Be a series of optimal values of unit optimal operational condition under reflection different load and the external condition, thereby obtain confidence level and the higher target goals value of accuracy.
Fired power generating unit is a very complicated process industry system, and its operation optimization problem is the focus that the thermoelectricity industry is paid close attention to always.The key problem that operation is optimized is to confirm unit operation parameter optimal objective value, to instruct operation through post-installation review.Operational factor optimal objective value has reflected the current operating condition condition of unit following optimal parameter that can reach and operating mode.
The following technical scheme of the concrete employing of the present invention.
A kind of analytical approach of the thermal power plant's operating index optimal objective value based on data mining; Said analytical approach is based on the unit operation mass historical data of thermal power plant's accumulation; Find out the optimal operational condition under the similar operating condition of unit, said analytical approach comprises the steps:
Step 1: the thermal power unit operation operating mode is divided, undertaken cluster to the unit operation operating mode by three external condition parameters such as load, ature of coal and circulating water temperature and divide, three external condition parameters are respectively got and are formed an operating condition between a dividing regions;
Step 2: definition fired power generating unit performance index comprise 3 types of indexs such as stability, economy, the feature of environmental protection;
Step 3: based on the thermal power unit operation parameter and the mass historical data of performance index that are stored in the real-time historical data base; Utilize the clustering method of data mining technology; Therefrom search out one group of optimum operational parameter value of performance index under each operating mode as the optimal objective value of operational factor under this operating mode, and the time of said desired value and cluster data is saved in the database;
Step 4: the initial value of the said desired value of the fired power generating unit of confirming in the step 3 as the operational factor under the corresponding operating mode; As time goes on the variation of thermal power unit operation operating mode; Regularly utilize the data clusters analytical approach of data mining technology; Search out one group of optimum operational parameter value of performance index under each operating mode as the up-to-date optimal objective value of operational factor under this operating mode, and the time of up-to-date optimal objective value and cluster data is saved in the database; Simultaneously; The optimal objective value that this is up-to-date and the optimal objective value that searches out constantly with the last timing under the operating mode, be historical optimal objective value relatively; If up-to-date optimal objective value is superior to historical optimal objective value; Then use up-to-date optimal objective value to substitute the historical optimal objective value under this operating mode, as the current optimal desired value of thermal power unit operation parameter under this operating mode.
Step 5:, reject the said historical optimal objective value that surpasses the setting time limit for the historical optimal objective value of each operational factor of preserving in step 3 and the step 4 under each operating mode.
The present invention has following characteristic and beneficial effect:
(1) based on the actual magnanimity production run historical data of unit, these data are units and equipment the objectively responding of (such as ature of coal, environment temperature, circulating water temperature etc.) actual motion state under different service conditions;
(2) adopt data mining technology and method, in the production run historical data of unit magnanimity,, found out the optimal operational condition under the similar operating condition of unit, confirmed the optimal value of each index under these service conditions through the means that software calculates automatically;
(3) accumulation and the software through the production run data constantly carries out timing optimizing calculating to the data of new accumulation; Bring in constant renewal in the optimal data storehouse; Assurance unit index operational objective value is followed the tracks of the objective running optimal value of unit all the time; The nearly running optimal value of not disconnecting provides credible, operating index desired value accurately for real time execution performance appraisal and real time execution instruct.
(4) overall process of this computing method is based on the production run historical data of unit; Adopt data mining technology that mass data is analyzed; Excavate out unit (such as ature of coal, environment temperature, circulating water temperature etc.) actual motion state under different service conditions; Reflect the actual objective moving law of unit, avoided artificial factor such as artificial target setting value.
Description of drawings
Fig. 1 is the thermal power plant's operation optimal objective value analytical approach process flow diagram that the present invention is based on data mining.
Embodiment
Below in conjunction with Figure of description technical scheme of the present invention is explained further details.
When the real time execution performance appraisal of carrying out unit and real time execution instruct; The operating index desired value that adopts which type of mathematical model to make to calculate has high as far as possible confidence level and accuracy; Be a relatively problem of difficulty,, relation also arranged with factors such as equipment state and environmental baselines because the desired value of each index is not only relevant with operating condition, load and the design parameter of unit; Have only and correctly and all sidedly consider various influence factors just can draw the desired value that conforms to the actual situation.
At present, curve fitting method and method of interpolation are generally adopted in the calculating of operating index desired value.
(1) curve fitting method
Under a certain load; Can get operational parameter value under the optimum operating condition as the desired value of index; In time; Present certain functional relation between the change curve of index parameter and the change curve of unit load, so can simulate the desired value curve of index with methods of numerical, these curves are functions of unit load.
(2) method of interpolation
Interpolation calculation index operating index desired value; At first need obtain the desired value (Xi of index under each load condition condition; Yi), wherein: Xi representes load variation, and Yi representes target goals value variable; The value of i is looked the computational accuracy of actual needs and is decided, and uses the method for numerical interpolation to try to achieve the target goals value then.
Be illustrated in figure 1 as the process flow diagram of the computing method of the thermal power plant's operating index optimal objective value based on data mining disclosed by the invention, concrete steps are following:
(1) the thermal power unit operation operating mode is divided; Being undertaken cluster to the unit operation operating mode by three external condition parameters such as load, ature of coal, circulating water temperature divides; Set up the operating mode storehouse of system, mainly comprise desired value of each parameter under the upper and lower bound value (numerical range of working condition parameter), each operating mode number of operating mode number, each working condition parameter of each operating mode etc.
For load parameter, adopt the K-averaging method to carry out discretize, cluster division to meeting parameter, be divided into such as load parameter certain 600MW unit ... [377,410], [410; 440], [440,475]; [475,511] ... Interval one by one like this, three external condition parameters are respectively got operating condition of an interval composition;
For the ature of coal parameter; Its characteristic is characterized by net calorific value, moisture, three amounts of fugitive constituent (analysis data); In order to simplify the calculating in operating mode storehouse; At first these three amounts are carried out discretize and different combinations are classified according to general regulation, be divided into poor, in, good, four classifications (or interval).
For circulating water temperature, between 0 to 40 ℃, it is divided into 8 temperature ranges from low to high continuously, 5 ℃ of each temperature range scopes as the one of which.
Assembled arrangement is carried out in each interval for above load, ature of coal, three external condition parameters of circulating water temperature, forms the whole working condition storehouse, comprises between operating mode number, loading zone, ature of coal is interval, circulating water temperature is interval, each operating index desired value etc.
(2) definition fired power generating unit performance index comprise 3 types of indexs such as stability, economy, the feature of environmental protection;
For every type of performance index, based on the operating mode storehouse of setting up in (1) step, the optimal objective value under difference computer set operating index each operating mode when such performance index are optimum.
(3) based on the thermal power unit operation parameter and the mass historical data of performance index that are stored in the real-time historical data base; Utilize the clustering method of data mining technology; Therefrom search out one group of optimum operational parameter value of performance index under each operating mode as the desired value of operational factor under this operating mode, and the time of desired value and cluster data is saved in the database;
(4) the desired value of each type of unit performance index of confirming in (3) as the initial value under the corresponding operating mode; As time goes on the variation of thermal power unit operation duty parameter; Adopt regularly (such as every month) execution data mining and data clusters analytic process; Extract the up-to-date optimal value of each unit operation index under each operating mode, and the time of up-to-date optimal value and cluster data is saved in the database; Simultaneously, the optimal value that this is up-to-date with the historical optimal value under the operating mode relatively, if be superior to the latter, then substitute the optimal value under this operating mode, as new operational factor desired value.
(5): for the historical optimal value of each operating index of preserving in step 3 and the step 4; After unit carries out big light maintenance every year; Reject some out-of-date historical datas, only need to keep the historical data of a period of time (such as nearest 3 years), thereby guarantee the ageing of historical optimal value.
The present invention obtains current thermal power unit operation parameter in above-mentioned steps (5) and on the basis of the optimal objective value under each operating mode, can further include following steps:
The current operating condition of calculating in real time of operation performance management system and decision-making system; According to the operating mode of current operating condition number and performance index type; The optimal objective value of each operating index of inquiry from the operating mode storehouse; With the desired value of these optimal objective values, carry out the examination scoring of index as performance assessment criteria.
The height of operations staff's operation level is estimated in the operation performance appraisal with the instantaneous value of index and the deviation size between the desired value.Unit is in actual motion, and operational factor often is in continuous fluctuation status, can not operational factor be adjusted to desired value and remain unchanged; But allow operational factor in some scopes, to fluctuate; Therefore, operational factor generally all has the economic line of going up, following economic line, goes up four " attention lines " such as safety line, following safety lines, and these four lines are that index has been divided several examinations interval; Also can as required interval division be got carefullyyer, carry out more fine-grained examination.
Examine in being provided with of standards of grading in index; Each interval score difference, interval score beyond the safety line is high up and down for the proportion by subtraction that gets that economic up and down line is interval, simultaneously; Economic up and down line interval often also is an optimum interval, and the interval beyond the safety line often also is that early warning is interval up and down.In practical application; The desired value of a lot of indexs changes with the variation of operating mode; Corresponding economic line up and down, safety line also changes with the variation of operating mode up and down; Curve rather than straight line often, four line value corresponding need calculate according to the real-time working condition condition through various algorithms, such as adopting curve fitting method, experimental formula method, Lagrange's interpolation etc.
Claims (3)
1. analytical approach based on thermal power plant's operating index optimal objective value of data mining; Said analytical approach is based on the unit operation mass historical data of thermal power plant's accumulation; Find out the optimal operational condition under the similar operating condition of unit, said analytical approach comprises the steps:
Step 1: the thermal power unit operation operating mode is divided, undertaken cluster to the unit operation operating mode by three external condition parameters such as load, ature of coal and circulating water temperature and divide, three external condition parameters are respectively got and are formed an operating condition between a dividing regions;
Step 2: definition fired power generating unit performance index comprise 3 types of indexs such as stability, economy, the feature of environmental protection;
Step 3: based on the thermal power unit operation parameter and the mass historical data of performance index that are stored in the real-time historical data base; Utilize the clustering method of data mining technology; Therefrom search out one group of optimum operational parameter value of performance index under each operating mode as the optimal objective value of operational factor under this operating mode, and the time of said desired value and cluster data is saved in the database;
Step 4: the initial value of the said desired value of the fired power generating unit of confirming in the step 3 as the operational factor under the corresponding operating mode; As time goes on the variation of thermal power unit operation operating mode; Regularly utilize the data clusters analytical approach of data mining technology; Search out one group of optimum operational parameter value of performance index under each operating mode as the up-to-date optimal objective value of operational factor under this operating mode, and the time of up-to-date optimal objective value and cluster data is saved in the database; Simultaneously; The optimal objective value that this is up-to-date and the optimal objective value that searches out constantly with the last timing under the operating mode, be historical optimal objective value relatively; If up-to-date optimal objective value is superior to historical optimal objective value; Then use up-to-date optimal objective value to substitute the historical optimal objective value under this operating mode, as the current optimal desired value of thermal power unit operation parameter under this operating mode.
Step 5:, reject the said historical optimal objective value that surpasses the setting time limit for the historical optimal objective value of each operational factor of preserving in step 3 and the step 4 under each operating mode.
2. the analytical approach of the thermal power plant's operating index optimal objective value based on data mining according to claim 1 is characterized in that:
In said step 5, be limited to 3 years or 2 years during said setting.
3. the analytical approach of the thermal power plant's operating index optimal objective value based on data mining according to claim 1 is characterized in that:
Described in the step 4 regularly the time be limited to one month.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210023721.5A CN102566551B (en) | 2012-02-03 | 2012-02-03 | Data mining-based method for analyzing thermal power plant operation index optimal target value |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210023721.5A CN102566551B (en) | 2012-02-03 | 2012-02-03 | Data mining-based method for analyzing thermal power plant operation index optimal target value |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102566551A true CN102566551A (en) | 2012-07-11 |
| CN102566551B CN102566551B (en) | 2014-07-09 |
Family
ID=46412195
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210023721.5A Active CN102566551B (en) | 2012-02-03 | 2012-02-03 | Data mining-based method for analyzing thermal power plant operation index optimal target value |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102566551B (en) |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102841583A (en) * | 2012-08-15 | 2012-12-26 | 广东电网公司电力科学研究院 | Method and system for searching optimal operation condition of thermal generator set |
| CN103279658A (en) * | 2013-05-21 | 2013-09-04 | 广东电网公司电力科学研究院 | Thermal generator set working condition optimizing method |
| CN103543719A (en) * | 2013-10-17 | 2014-01-29 | 中国科学院软件研究所 | Method for adaptively adjusting operation modes of process industry on basis of working conditions |
| CN103699786A (en) * | 2013-12-12 | 2014-04-02 | 广东电网公司电力科学研究院 | Energy consumption difference analysis method for load varying of ultra-supercritical generating unit of thermal power plant |
| CN103745414A (en) * | 2013-12-31 | 2014-04-23 | 湖南大唐先一科技有限公司 | Online scoring method for performance of gas-steam combined circulating power generation unit |
| CN104318104A (en) * | 2014-10-24 | 2015-01-28 | 国家电网公司 | Comprehensive evaluation method of energy conservation and emission reduction achievement of thermal power generating unit |
| CN104750973A (en) * | 2015-02-28 | 2015-07-01 | 河北省电力建设调整试验所 | Thermal power generating unit load (quasi) steady-state working condition clustering algorithm based on data smoothness functions |
| CN106094744A (en) * | 2016-06-04 | 2016-11-09 | 上海大学 | The determination method of thermoelectricity factory owner's operational factor desired value based on association rule mining |
| CN106406089A (en) * | 2016-08-31 | 2017-02-15 | 北京金控数据技术股份有限公司 | Environmental protection facility operating parameter optimization method and device |
| CN106960395A (en) * | 2017-03-14 | 2017-07-18 | 西安西热控制技术有限公司 | A kind of fired power generating unit historical data management device and method |
| CN107145981A (en) * | 2017-05-05 | 2017-09-08 | 张勇 | A kind of optimization method and device of boiler of power plant exhaust gas temperature |
| CN107315852A (en) * | 2017-05-22 | 2017-11-03 | 大唐宝鸡热电厂 | Unit running optimization management and performance appraisal system based on power consumption analysis |
| CN108288111A (en) * | 2018-01-26 | 2018-07-17 | 广东电网有限责任公司电力科学研究院 | Thermal power plant's exhaust gas temperature reference value determination method based on correlation rule and device |
| CN109344488A (en) * | 2018-09-25 | 2019-02-15 | 云南电网有限责任公司电力科学研究院 | A kind of evidence characterization construction method of fired power generating unit bleeder heater performance state |
| CN109407506A (en) * | 2018-11-28 | 2019-03-01 | 深圳圣缘节能科技有限公司 | A kind of acquisition methods of the power plant units dynamic optimal value based on data mining |
| CN109917641A (en) * | 2017-12-12 | 2019-06-21 | 南京凯盛国际工程有限公司 | A kind of denitration system optimizing control based on model of mind PREDICTIVE CONTROL |
| CN110471378A (en) * | 2019-08-09 | 2019-11-19 | 昆山市自来水集团有限公司 | A kind of Apparatus of Water Plant Automation and data analysis system |
| CN110556033A (en) * | 2019-07-30 | 2019-12-10 | 华电青岛发电有限公司 | Operation guiding system based on typical and accident case base of thermal power plant |
| CN110989360A (en) * | 2019-12-23 | 2020-04-10 | 武汉博晟信息科技有限公司 | Thermal power generating unit steady-state history optimizing method based on full data |
| WO2020134801A1 (en) * | 2018-12-26 | 2020-07-02 | 厦门邑通软件科技有限公司 | Energy-saving optimization method of cement raw material vertical mill system |
| CN112288170A (en) * | 2020-10-30 | 2021-01-29 | 北方魏家峁煤电有限责任公司 | Power plant unit working condition optimizing analysis method based on big data analysis |
| CN112650167A (en) * | 2020-12-15 | 2021-04-13 | 江苏方天电力技术有限公司 | Thermal power plant operation parameter optimization analysis method based on data mining |
| CN112749840A (en) * | 2020-12-29 | 2021-05-04 | 华北电力大学 | Method for acquiring reference value of energy efficiency characteristic index of thermal power generating unit |
| CN113220753A (en) * | 2021-05-10 | 2021-08-06 | 西安热工研究院有限公司 | Method for automatically generating operation parameter target curve based on historical data of power plant |
| CN116312882A (en) * | 2023-02-17 | 2023-06-23 | 宝胜高压电缆有限公司 | Polypropylene cable production process optimization method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101187804A (en) * | 2006-11-15 | 2008-05-28 | 北京华电天仁电力控制技术有限公司 | Thermal power unit operation optimization rule extraction method based on data excavation |
| EP1965316A2 (en) * | 2007-02-27 | 2008-09-03 | Siemens Energy & Automation, Inc. | Storage of multiple, related time-series data streams |
| CN101727085A (en) * | 2009-11-24 | 2010-06-09 | 太原理工大学 | Device for monitoring states and diagnosing faults of coal mine underground working surface conveyer |
| CN101763106A (en) * | 2010-01-27 | 2010-06-30 | 浙江国华浙能发电有限公司 | Operation optimum management control system and method of thermal generator set |
-
2012
- 2012-02-03 CN CN201210023721.5A patent/CN102566551B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101187804A (en) * | 2006-11-15 | 2008-05-28 | 北京华电天仁电力控制技术有限公司 | Thermal power unit operation optimization rule extraction method based on data excavation |
| EP1965316A2 (en) * | 2007-02-27 | 2008-09-03 | Siemens Energy & Automation, Inc. | Storage of multiple, related time-series data streams |
| CN101727085A (en) * | 2009-11-24 | 2010-06-09 | 太原理工大学 | Device for monitoring states and diagnosing faults of coal mine underground working surface conveyer |
| CN101763106A (en) * | 2010-01-27 | 2010-06-30 | 浙江国华浙能发电有限公司 | Operation optimum management control system and method of thermal generator set |
Cited By (34)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102841583B (en) * | 2012-08-15 | 2014-09-17 | 广东电网公司电力科学研究院 | Method and system for searching optimal operation condition of thermal generator set |
| CN102841583A (en) * | 2012-08-15 | 2012-12-26 | 广东电网公司电力科学研究院 | Method and system for searching optimal operation condition of thermal generator set |
| CN103279658B (en) * | 2013-05-21 | 2017-02-15 | 广东电网公司电力科学研究院 | Thermal generator set working condition optimizing method |
| CN103279658A (en) * | 2013-05-21 | 2013-09-04 | 广东电网公司电力科学研究院 | Thermal generator set working condition optimizing method |
| CN103543719A (en) * | 2013-10-17 | 2014-01-29 | 中国科学院软件研究所 | Method for adaptively adjusting operation modes of process industry on basis of working conditions |
| CN103543719B (en) * | 2013-10-17 | 2015-10-07 | 中国科学院软件研究所 | A kind of workflow industry operator scheme self-adapting regulation method based on operating mode |
| CN103699786A (en) * | 2013-12-12 | 2014-04-02 | 广东电网公司电力科学研究院 | Energy consumption difference analysis method for load varying of ultra-supercritical generating unit of thermal power plant |
| CN103745414A (en) * | 2013-12-31 | 2014-04-23 | 湖南大唐先一科技有限公司 | Online scoring method for performance of gas-steam combined circulating power generation unit |
| CN104318104A (en) * | 2014-10-24 | 2015-01-28 | 国家电网公司 | Comprehensive evaluation method of energy conservation and emission reduction achievement of thermal power generating unit |
| CN104750973B (en) * | 2015-02-28 | 2019-01-29 | 国网河北能源技术服务有限公司 | Unit load (standard) steady state condition clustering algorithm based on data smoothness function |
| CN104750973A (en) * | 2015-02-28 | 2015-07-01 | 河北省电力建设调整试验所 | Thermal power generating unit load (quasi) steady-state working condition clustering algorithm based on data smoothness functions |
| CN106094744A (en) * | 2016-06-04 | 2016-11-09 | 上海大学 | The determination method of thermoelectricity factory owner's operational factor desired value based on association rule mining |
| CN106406089A (en) * | 2016-08-31 | 2017-02-15 | 北京金控数据技术股份有限公司 | Environmental protection facility operating parameter optimization method and device |
| CN106960395A (en) * | 2017-03-14 | 2017-07-18 | 西安西热控制技术有限公司 | A kind of fired power generating unit historical data management device and method |
| CN107145981A (en) * | 2017-05-05 | 2017-09-08 | 张勇 | A kind of optimization method and device of boiler of power plant exhaust gas temperature |
| CN107315852A (en) * | 2017-05-22 | 2017-11-03 | 大唐宝鸡热电厂 | Unit running optimization management and performance appraisal system based on power consumption analysis |
| CN109917641A (en) * | 2017-12-12 | 2019-06-21 | 南京凯盛国际工程有限公司 | A kind of denitration system optimizing control based on model of mind PREDICTIVE CONTROL |
| CN108288111A (en) * | 2018-01-26 | 2018-07-17 | 广东电网有限责任公司电力科学研究院 | Thermal power plant's exhaust gas temperature reference value determination method based on correlation rule and device |
| CN108288111B (en) * | 2018-01-26 | 2021-07-02 | 广东电网有限责任公司电力科学研究院 | Thermal power plant exhaust smoke temperature reference value determining method and device based on association rules |
| CN109344488A (en) * | 2018-09-25 | 2019-02-15 | 云南电网有限责任公司电力科学研究院 | A kind of evidence characterization construction method of fired power generating unit bleeder heater performance state |
| CN109344488B (en) * | 2018-09-25 | 2023-11-24 | 云南电网有限责任公司电力科学研究院 | Evidence characterization construction method for performance state of regenerative heater of thermal power generating unit |
| CN109407506A (en) * | 2018-11-28 | 2019-03-01 | 深圳圣缘节能科技有限公司 | A kind of acquisition methods of the power plant units dynamic optimal value based on data mining |
| WO2020134801A1 (en) * | 2018-12-26 | 2020-07-02 | 厦门邑通软件科技有限公司 | Energy-saving optimization method of cement raw material vertical mill system |
| CN110556033A (en) * | 2019-07-30 | 2019-12-10 | 华电青岛发电有限公司 | Operation guiding system based on typical and accident case base of thermal power plant |
| CN110471378A (en) * | 2019-08-09 | 2019-11-19 | 昆山市自来水集团有限公司 | A kind of Apparatus of Water Plant Automation and data analysis system |
| CN110989360A (en) * | 2019-12-23 | 2020-04-10 | 武汉博晟信息科技有限公司 | Thermal power generating unit steady-state history optimizing method based on full data |
| CN112288170A (en) * | 2020-10-30 | 2021-01-29 | 北方魏家峁煤电有限责任公司 | Power plant unit working condition optimizing analysis method based on big data analysis |
| CN112650167A (en) * | 2020-12-15 | 2021-04-13 | 江苏方天电力技术有限公司 | Thermal power plant operation parameter optimization analysis method based on data mining |
| CN112749840A (en) * | 2020-12-29 | 2021-05-04 | 华北电力大学 | Method for acquiring reference value of energy efficiency characteristic index of thermal power generating unit |
| CN112749840B (en) * | 2020-12-29 | 2023-10-20 | 华北电力大学 | Method for acquiring energy efficiency characteristic index reference value of thermal power generating unit |
| CN113220753A (en) * | 2021-05-10 | 2021-08-06 | 西安热工研究院有限公司 | Method for automatically generating operation parameter target curve based on historical data of power plant |
| CN113220753B (en) * | 2021-05-10 | 2023-01-20 | 西安热工研究院有限公司 | Method for automatically generating operation parameter target curve based on historical data of power plant |
| CN116312882A (en) * | 2023-02-17 | 2023-06-23 | 宝胜高压电缆有限公司 | Polypropylene cable production process optimization method and system |
| CN116312882B (en) * | 2023-02-17 | 2024-02-20 | 宝胜高压电缆有限公司 | Polypropylene cable production process optimization method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102566551B (en) | 2014-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102566551B (en) | Data mining-based method for analyzing thermal power plant operation index optimal target value | |
| Fazlollahi et al. | Multi-objectives, multi-period optimization of district energy systems: I. Selection of typical operating periods | |
| EP3582359A1 (en) | Stability evaluation and static control method for electricity-heat-gas integrated energy system | |
| CN103742901B (en) | A kind of defining method of Properties of CFB power consumption analysis optimization target values system | |
| CN102778538B (en) | Soft measuring method based on improved SVM (Support Vector Machine) for measuring boiler unburned carbon content in fly ash | |
| CN104534507B (en) | A kind of boiler combustion optimization control method | |
| CN101893877A (en) | Optimization operational method based on energy consumption analysis for power plant and system thereof | |
| CN102809928B (en) | Control optimizing method for energy consumption of thermal equipment of industrial enterprise | |
| CN103455635A (en) | Thermal process soft sensor modeling method based on least squares and support vector machine ensemble | |
| CN103631140B (en) | Based on the coke oven heating-combustion process fire path temperature Automatic adjustment method of Performance Evaluation | |
| CN106703904B (en) | A kind of Steam Distribution of Steam Turbine optimization of profile method based on data mining technology | |
| CN103543719B (en) | A kind of workflow industry operator scheme self-adapting regulation method based on operating mode | |
| CN101187804A (en) | Thermal power unit operation optimization rule extraction method based on data excavation | |
| CN111754030B (en) | Thermal power generating unit power supply coal consumption optimization method based on HAC and RF-GA | |
| CN105787271B (en) | The adjustable output Interval evaluation method of thermal power plant unit based on big data analytical technology | |
| CN103728879A (en) | Power station boiler emission soft measuring method based on least squares support vector machine and on-line updating | |
| US20230229124A1 (en) | Operation control system and a control method for a gas-steam combined cycle generator unit | |
| CN106594794B (en) | A kind of hybrid intelligent boiler efficiency burning optimization model update method | |
| CN103729569A (en) | Soft measurement system for flue gas of power-station boiler on basis of LSSVM (Least Squares Support Vector Machine) and online updating | |
| CN105317476A (en) | Turbine flow curve identification and optimization method based on feature flow area | |
| CN110533247B (en) | Monthly power consumption prediction method adopting temperature data anomaly point compensation | |
| CN103294015A (en) | Set of optimization control methods of ethylene cracking furnace | |
| Wang et al. | Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process | |
| CN113504765A (en) | Method for optimizing operation of circulating fluidized bed boiler of power plant | |
| CN202075248U (en) | Analysis system for maturity degrees of cokes in coke oven |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: 101303 2nd floor, building 307, Guodian New Energy Institute, 9 Yingcai North 2nd Street, future science and Technology City, Changping District, Beijing Patentee after: Guoneng xinkong Internet Technology Co.,Ltd. Patentee after: NORTH CHINA ELECTRIC POWER University Address before: Building 1, yard 16, West Fourth Ring Middle Road, Haidian District, Beijing 100039 Patentee before: BEIJING HUADIAN TIANREN ELECTRIC POWER CONTROL TECHNOLOGY Co.,Ltd. Patentee before: NORTH CHINA ELECTRIC POWER University |