CN113091333B - Flow feedforward-feedback control method for tower type photo-thermal power station heat absorber - Google Patents
Flow feedforward-feedback control method for tower type photo-thermal power station heat absorber Download PDFInfo
- Publication number
- CN113091333B CN113091333B CN202110327841.3A CN202110327841A CN113091333B CN 113091333 B CN113091333 B CN 113091333B CN 202110327841 A CN202110327841 A CN 202110327841A CN 113091333 B CN113091333 B CN 113091333B
- Authority
- CN
- China
- Prior art keywords
- heat absorber
- heat
- flow
- feedback control
- power station
- 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.)
- Active
Links
- 239000006096 absorbing agent Substances 0.000 title claims abstract description 85
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000013528 artificial neural network Methods 0.000 claims abstract description 23
- 230000005855 radiation Effects 0.000 claims abstract description 8
- 239000013529 heat transfer fluid Substances 0.000 claims description 12
- 230000001105 regulatory effect Effects 0.000 claims description 9
- 230000004907 flux Effects 0.000 claims description 8
- 230000003287 optical effect Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 230000010349 pulsation Effects 0.000 claims description 3
- 239000007787 solid Substances 0.000 claims description 3
- 230000004069 differentiation Effects 0.000 claims description 2
- 230000010354 integration Effects 0.000 claims description 2
- 230000008859 change Effects 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004146 energy storage Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 150000003839 salts Chemical class 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
Images
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24S—SOLAR HEAT COLLECTORS; SOLAR HEAT SYSTEMS
- F24S50/00—Arrangements for controlling solar heat collectors
- F24S50/80—Arrangements for controlling solar heat collectors for controlling collection or absorption of solar radiation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/40—Solar thermal energy, e.g. solar towers
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- General Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Artificial Intelligence (AREA)
- Biophysics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Sustainable Development (AREA)
- Sustainable Energy (AREA)
- Thermal Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- Feedback Control In General (AREA)
Abstract
一种塔式光热电站吸热器流量前馈‑反馈控制方法，其中前馈控制通过神经网络预测模型获得云层扰动下的吸热器流量调节值，可快速应对太阳辐射强度的剧烈变化；反馈控制通过PID对吸热器流量进行微调，可实现吸热器出口温度稳定在设定值。通过结合吸热器流量的神经网络预测前馈控制和PID反馈控制，本发明可实现太阳辐射波动条件下的吸热器流量快速调节，保证吸热器出口温度稳定，从而减少弃光率，提高塔式太阳能热电站的系统效率。
A feedforward-feedback control method for the flow rate of a heat absorber in a tower-type solar thermal power station, wherein the feedforward control obtains a flow regulation value of the heat absorber under cloud layer disturbance through a neural network prediction model, which can quickly respond to drastic changes in solar radiation intensity; feedback The control fine-tunes the flow rate of the heat absorber through PID, so that the outlet temperature of the heat absorber can be stabilized at the set value. By combining the neural network prediction feedforward control and PID feedback control of the heat absorber flow, the invention can realize the rapid adjustment of the heat absorber flow under the condition of solar radiation fluctuation, ensure the stability of the heat absorber outlet temperature, thereby reduce the light rejection rate and improve the System efficiency of a tower solar thermal power plant.
Description
Technical Field
The invention belongs to the technical field of solar thermal power generation and automatic control, and particularly relates to a flow feedforward-feedback control method for a heat absorber of a tower type photo-thermal power station.
Background
The solar thermal power station is provided with a large-scale, cheap, safe and environment-friendly energy storage system, and the requirement of a future power system on energy storage can be obviously reduced. Throughout the life cycle, the carbon emissions per degree of electricity from a photothermal power station are only 1/6 for photovoltaics. In actual operation, the cloud layer of light and heat power station mirror field top can cause DNI's fluctuation, and when undulant comparatively acutely, the heat absorber can receive serious cold and hot impact and shut down, and this can lead to higher light rate of abandoning, has restricted the photoelectric efficiency promotion of light and heat power station. In order to reduce the light rejection rate, a feasible method is a cloud prediction technology, namely, equipment such as an all-sky imager and the like is adopted to realize 0-30-minute cloud prediction, the flow of a heat absorber is controlled in advance, and the light rejection of a mirror field is reduced as far as possible under the condition of reducing cold and hot impact of the heat absorber. However, since the cloud change is random, a method for quickly and reliably controlling the flow of the heat absorber in cloudy weather is still lacking.
Disclosure of Invention
In order to overcome the defects of the prior art and solve the problem that a method for quickly and reliably controlling the flow of a heat absorber under cloudy weather is still lacked at present, the invention aims to provide a flow feedforward-feedback control method for the heat absorber of a tower type photothermal power station.
In order to achieve the purpose, the invention adopts the technical scheme that:
a feedforward-feedback control method for the flow of a heat absorber of a tower-type photo-thermal power station is characterized in that the feedforward control obtains a heat absorber flow adjusting value under cloud disturbance through a neural network prediction model, and the feedback control finely adjusts the flow of the heat absorber through PID.
The neural network prediction model is obtained through the following steps:
step 1: the method comprises the steps of constructing a physical model of a light-gathering and heat-collecting system of the tower type photo-thermal power station, and obtaining the heat flux density distribution on the surface of a heat absorber under the condition of a full mirror field by adopting a Monte Carlo ray tracing method or a convolution method and the like, wherein the condition of the full mirror field refers to that the optical propagation process of a heliostat on each side of the light in the mirror field is considered in the optical propagation calculation.
Step 2: calculating to obtain the flow path mass flow q corresponding to the set outlet temperature of the heat absorber by adopting a finite volume method and taking the heat flux density distribution on the surface of the heat absorber obtained in the step 1 as a thermal boundary conditionmIn particular, the heat absorber mass flow qmThe temperature of the inlet and the outlet of the heat absorber is calculated and obtained under the set value of actual operation.
In the finite volume method, a standard k-epsilon turbulence model is used, the control equation of which is as follows:
continuity equation:
the momentum equation:
k equation:
the equation of ε:
energy equation:
in the formula, rho, cpAnd T is the density, specific heat and temperature of the heat absorbing pipe solid or heat transfer fluid respectively; t represents time; x is the coordinate direction of the three-dimensional coordinate system; u, p, μ are the velocity, pressure and kinetic viscosity coefficients of the heat transfer fluid, respectively; deltaijIs a component of the unit second order tensor, whose value is 1 when i ═ j, and 0 otherwise;
is the Reynolds stress; k. epsilon, mutTurbulent pulsating energy, dissipation ratio and turbulent viscosity coefficient of the heat transfer fluid are respectively; sigmak、σεTurbulent prandtl numbers which are turbulent pulsation energy and dissipation ratio, respectively; pr and PrtThe ratio is the prandtl number and turbulent prandtl number of the heat transfer fluid; sTIs a heat source; c1And C2Is a constant; the indices i, j, k, l are the summing convention indices.
And step 3: repeating the step 1 and the step 2 at different moments and under different weather conditions to obtain q under the corresponding working conditions of the heat absorbermForming a database of time and weather condition input parameters and mass flow output parameters, wherein the weather conditions include wind speed, ambient temperature and direct solar radiation intensity (DNI) for a particular photovoltaic plant;
and 4, step 4: and (3) training the database in the step (3) by adopting a neural network prediction algorithm, and obtaining a prediction model. Based on the prediction model, q can be predicted quickly after output parameters are givenmThe predicted required time is only a few seconds.
On the basis, when the cloud layer passes through and DNI of the heliostat field of the tower-type photo-thermal power station is changed violently, corresponding parameters are input according to the neural network prediction model to obtain corresponding output parameters qm，qmNamely the adjusting value in the flow feedforward control of the heat absorber under the cloud layer disturbance. The flow rate of the heat absorber can be quickly adjusted through feed-forward control,thereby shortening the time required for the outlet temperature of the heat absorber to return to the set value.
The feedback control is realized by comparing the difference value between the actual outlet temperature and the set temperature of the heat absorber and finely adjusting q through PID feedback controlmFinally, the outlet temperature of the heat absorber is stabilized at the set value.
Compared with the prior art, the method can realize the rapid and reliable adjustment of the flow of the heat absorber under the condition of solar radiation fluctuation by combining the neural network prediction with the feedforward-feedback control of the flow, thereby solving the problem that the rapid and reliable control of the flow of the heat absorber under the cloudy weather is still lacked at present. Under the cloudy condition, the flow predicted value of the heat absorber is used as a flow adjusting value in feed-forward adjustment to quickly adjust the flow of the heat absorber, and further PID (proportion integration differentiation) feedback adjustment is adopted to finely adjust the flow, so that the outlet temperature is ensured to be stable, the light rejection rate is reduced, and the system efficiency of the tower type solar thermal power station is improved.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a schematic diagram of a neural network prediction model.
FIG. 3 is a graph comparing the predicted flowrate lower heat absorber outlet temperature to a set point.
FIG. 4 shows the DNI step increase.
FIG. 5 is a graph of the regulatory performance of the present invention as the DNI step increases.
Fig. 6 shows the DNI step reduction values.
FIG. 7 is a graph of the regulatory performance of the present invention as the DNI step decreases.
FIG. 8 shows the DNI continuously varied values.
FIG. 9 is a graph of the regulatory performance of the present invention with continuous variation of DNI.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the drawings and examples.
The invention relates to a flow feedforward-feedback control method for a tower type photo-thermal power station heat absorber based on neural network prediction, wherein feedforward control obtains a flow adjustment value of the heat absorber under cloud disturbance through a neural network prediction model, and can quickly cope with severe changes of solar radiation intensity; the feedback control can realize the stabilization of the outlet temperature of the heat absorber at a set value by fine adjustment of the flow of the heat absorber through PID. By combining the neural network prediction feedforward control and the PID feedback control of the heat absorber flow, the invention can realize the rapid adjustment of the heat absorber flow under the solar radiation fluctuation condition and ensure the stable temperature of the outlet of the heat absorber, thereby reducing the light rejection rate and improving the system efficiency of the tower type solar thermal power station.
The invention is explained in detail by taking a Solar tower type molten salt Solar thermal power station as an example:
as shown in fig. 1, the concrete implementation flow of the heliostat aiming strategy of the tower-type molten salt solar thermal power station of the invention is as follows:
(1) aiming at the concentrating and heat collecting system of the Solar Two power station, the Monte Carlo ray tracing method or the convolution method is adopted to obtain the heat flux density distribution on the surface of the heat absorber under the condition of a full mirror field. The present embodiment briefly describes the heat collector energy flow density distribution calculation method by taking a monte carlo ray tracing method as an example. Firstly, tracking the propagation process of a large amount of light rays in a heliostat field and a heat absorber; secondly, counting the number of light rays absorbed by each grid unit of the heat absorber; finally, obtaining the heat flux density q of each grid unit of the heat absorber:
wherein q is the heat flux density; e is the energy carried by each ray; n is the number of photons absorbed by the grid cells; a is the area of the grid cell;
(2) adopting a finite volume method, taking the heat flow density distribution of the heat absorber obtained in the step 1 as a thermal boundary condition, calculating to obtain the mass flow of a flow path corresponding to the set outlet temperature of the heat absorber, adopting FLUENT commercial software to calculate the flow heat transfer process of the heat absorber so as to obtain the outlet flow of the heat absorber, wherein a turbulence model adopts a standard k-epsilon model, and a control equation is as follows:
continuity equation:
the momentum equation:
k equation:
the equation of ε:
energy equation:
where ρ, cpAnd T is the density, specific heat and temperature of the heat absorbing pipe solid or heat transfer fluid respectively; t represents time; x is the coordinate direction of the three-dimensional coordinate system; u, p, μ are the velocity, pressure and kinetic viscosity coefficients of the heat transfer fluid, respectively; deltaijIs a component of the unit second order tensor, whose value is 1 when i ═ j, and 0 otherwise;
is the Reynolds stress; k, epsilon, mutTurbulent pulsating energy, dissipation ratio and turbulent viscosity coefficient of the heat transfer fluid are respectively; sigmak,σεTurbulent prandtl numbers which are turbulent pulsation energy and dissipation ratio, respectively; pr, PrtThe ratio is the prandtl number and turbulent prandtl number of the heat transfer fluid; sTIs a heat source; c1And C2Is a constant; the subscripts i, j, k, l are the summing convention indices.
(3) Repeating the step 1 and the step 2 at different moments and under different weather conditions to obtain q of the heat absorber under corresponding working conditionsmForming a database of time and weather condition input parameters and mass flow output parameters; in this embodiment, there are 3 input parameters including date, time and DNI, and the output parameter is the mass flow rate of the heat absorber under the corresponding condition.
(4) And (4) training the database in the step (3) by adopting a neural network prediction algorithm, and obtaining a corresponding neural network prediction model. The data in step (3) is trained by using two layers of feedforward neural networks, and the schematic diagram of the neural network is shown in fig. 2. The hidden layer employs 10 neural units. The transformation function of the hidden layer adopts a Sigmoid function, and the transformation function of the output layer adopts a linear function. FIG. 3 is a graph comparing the predicted flowrate lower heat absorber outlet temperature to a set point. As can be seen from the figure, for most of the predicted flow data, the error between the outlet temperature of the heat absorber and the set value is within +/-2.5 ℃, and the method for obtaining the flow of the heat absorber by adopting neural network prediction is proved to be reliable.
(5) When the cloud layer passes and DNI of the tower type photothermal power station is changed violently, inputting corresponding parameters according to the neural network prediction model obtained in the step 4 to obtain corresponding qmOutput parameter, qmNamely the adjusting value in the flow feedforward control of the heat absorber under the cloud layer disturbance. Further, the q is finely adjusted by PID feedback control by comparing the difference between the actual outlet temperature of the heat absorber and the set temperaturemAnd finally, stabilizing the outlet temperature of the heat absorber at a set value. For the Solar Two plant, the measurement error of the outlet temperature of the heat absorber was ± 2.8 ℃. Therefore, in this embodiment, the allowable deviation between the heat absorber outlet temperature control value and the set value is set to ± 2.8 ℃, that is, when the deviation between the outlet temperature and the set value is within ± 2.8 ℃, the heat absorber flow rate is not adjusted.
FIG. 4 is a graph of DNI as a function of time as the DNI step increases. FIG. 5 is a graph of the regulatory performance of the present invention with corresponding step increases in DNI. As can be seen from the graph 5, when DNI (numerical input) is increased suddenly, the flow feedforward-feedback control method for the tower type photothermal power station based on the neural network prediction model can restore the outlet temperature of the heat absorber to the set value within 40s, and the temperature fluctuation is within +/-2 ℃. FIG. 6 is a graph showing the change in DNI with time when the DNI step is decreased. FIG. 7 is a graph of the regulatory performance of the present invention with corresponding DNI step reductions. As can be seen from fig. 7, the present invention also allows a quick return of the absorber outlet temperature to the set value with temperature fluctuations within ± 2 ℃ when the DNI step is reduced. FIG. 8 is a graph showing the change of DNI with time when the DNI is continuously changed. FIG. 9 is a graph of the regulatory performance of the present invention with continuous variation of the corresponding DNI. As can be seen from FIG. 9, when the DNI is continuously and violently changed, the invention can limit the fluctuation value of the outlet temperature of the heat absorber within +/-6 ℃, and can meet the requirements of practical engineering application.
According to the invention, through the combination of neural network prediction and feedforward-feedback control of the flow of the heat absorber, the rapid and reliable prediction of the flow of the heat absorber in cloudy weather can be realized, the flow of the heat absorber under the condition of solar radiation fluctuation can be rapidly regulated, and the stable outlet temperature is ensured, so that the light rejection rate is reduced, and the system efficiency of the tower type solar thermal power station is improved.
Claims (6)
1. The feedforward control obtains a heat absorber flow regulating value under cloud layer disturbance through a neural network prediction model, and the feedback control finely adjusts the heat absorber flow through PID (proportion integration differentiation), and is characterized in that the neural network prediction model is obtained through the following steps:
step 1: constructing a physical model of a light-gathering and heat-collecting system of the tower-type photo-thermal power station, and obtaining the surface heat flux density distribution of a heat absorber under the condition of a full mirror field;
step 2: calculating to obtain the flow path mass flow q corresponding to the set outlet temperature of the heat absorber by adopting a finite volume method and taking the heat flux density distribution on the surface of the heat absorber obtained in the step 1 as a thermal boundary conditionm；
And step 3: repeating the step 1 and the step 2 at different moments and under different weather conditions to obtain q under the corresponding working conditions of the heat absorbermForming a database of time and weather condition input parameters and mass flow output parameters, wherein the weather conditions include wind speed, ambient temperature and direct solar radiation intensity (DNI) for a particular photovoltaic plant;
and 4, step 4: and (3) training the database in the step (3) by adopting a neural network prediction algorithm, and obtaining a prediction model.
2. The feedforward-feedback control method for the flow of the heat absorber of the tower-type photothermal power station according to claim 1, wherein in the step 1, the monte carlo ray tracing method or the convolution method is adopted to obtain the heat flux density distribution on the surface of the heat absorber under the condition of a full mirror field, and the condition of the full mirror field refers to that in the optical propagation calculation, the optical propagation process of rays on each heliostat in the mirror field is considered.
3. The tower-type photothermal power station heat absorber flow feedforward-feedback control method according to claim 1, wherein in step 2, the heat absorber mass flow qmThe temperature of the inlet and the outlet of the heat absorber is calculated and obtained under the set value of actual operation.
4. The feedforward-feedback control method for the flow of the heat absorber of the tower-type photothermal power station according to claim 1, wherein in the finite volume method, a standard k-epsilon turbulence model is adopted, and a control equation is as follows:
continuity equation:
the momentum equation:
k equation:
the equation of ε:
energy equation:
in the formula, rho, cpT is respectivelyDensity, specific heat and temperature of the absorber tube solids or heat transfer fluid; t represents time; x is the coordinate direction of the three-dimensional coordinate system; u, p, μ are the velocity, pressure and kinetic viscosity coefficients of the heat transfer fluid, respectively; deltaijIs a component of the unit second order tensor, whose value is 1 when i ═ j, and 0 otherwise;
is the Reynolds stress; k. epsilon, mutTurbulent pulsating energy, dissipation ratio and turbulent viscosity coefficient of the heat transfer fluid are respectively; sigmak、σεTurbulent prandtl numbers which are turbulent pulsation energy and dissipation ratio, respectively; pr and PrtThe ratio is the prandtl number and turbulent prandtl number of the heat transfer fluid; sTIs a heat source; c1And C2Is a constant; the indices i, j, k, l are the summing convention indices.
5. The method for feedforward-feedback control of tower-type photothermal power station heat absorber flow according to any one of claims 1 to 4, wherein when cloud passes and DNI of heliostat field of tower-type photothermal power station is changed drastically, corresponding parameters are input according to the neural network prediction model to obtain corresponding output parameters, i.e. qm，qmIs the regulating value in the feedforward control of the flow of the heat absorber under the cloud layer disturbance.
6. The feedforward-feedback control method for flow rate of heat absorbers in tower-type photothermal power station according to any one of claims 1 to 4, wherein the feedback control is performed by comparing the difference between the actual outlet temperature and the set temperature of the heat absorbers and performing fine tuning q by PID feedback controlmFinally, the outlet temperature of the heat absorber is stabilized at the set value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110327841.3A CN113091333B (en) | 2021-03-26 | 2021-03-26 | Flow feedforward-feedback control method for tower type photo-thermal power station heat absorber |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110327841.3A CN113091333B (en) | 2021-03-26 | 2021-03-26 | Flow feedforward-feedback control method for tower type photo-thermal power station heat absorber |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113091333A CN113091333A (en) | 2021-07-09 |
| CN113091333B true CN113091333B (en) | 2022-03-22 |
Family
ID=76670118
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110327841.3A Active CN113091333B (en) | 2021-03-26 | 2021-03-26 | Flow feedforward-feedback control method for tower type photo-thermal power station heat absorber |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113091333B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113609727B (en) * | 2021-07-26 | 2022-12-09 | 西安交通大学 | Multi-scale heat absorber photo-thermal coupling performance obtaining method considering near-field optical effect |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6095426A (en) * | 1997-11-07 | 2000-08-01 | Siemens Building Technologies | Room temperature control apparatus having feedforward and feedback control and method |
| WO2010085592A2 (en) * | 2009-01-22 | 2010-07-29 | Kenneth Oosting | Actuated feedforward controlled solar tracking system |
| CN101504736A (en) * | 2009-02-27 | 2009-08-12 | 江汉大学 | Method for implementing neural network algorithm based on Delphi software |
| US8378151B2 (en) * | 2009-06-09 | 2013-02-19 | Sundrop Fuels, Inc. | Systems and methods for an integrated solar driven chemical plant |
| CN102425865B (en) * | 2011-11-07 | 2013-06-05 | 深圳市联讯创新工场科技开发有限公司 | Solar heat collecting device |
-
2021
- 2021-03-26 CN CN202110327841.3A patent/CN113091333B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113091333A (en) | 2021-07-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104408534B (en) | Tower type solar heat and power system running optimizatin method based on simultaneousequation model | |
| Roca et al. | Solar field control for desalination plants | |
| CN108256670B (en) | Optimal model for joint peak regulation of solar thermal power generation and thermal power units based on combined heat and power generation | |
| CN112528542B (en) | Photo-thermal power generation system dynamic modeling method considering heat collector working mode and control method | |
| Rodat et al. | Dynamic simulations of Fresnel solar power plants | |
| CN113091333B (en) | Flow feedforward-feedback control method for tower type photo-thermal power station heat absorber | |
| CN112541609A (en) | Wind-light-heat and water energy storage combined renewable energy power generation system capacity optimization model | |
| Hamada et al. | Identification of a different design of a photovoltaic thermal collector based on fuzzy logic control and the ARMAX model | |
| CN105160435A (en) | Tower-type solar thermal power plant heliostat field focusing strategy optimization method | |
| CN111736467B (en) | Heliostat field focusing strategy optimization method for tower type solar thermal power station | |
| Flores-Hernández et al. | Numerical energy evaluation of an optimized hybrid energy harvesting system (CPV-TEG) considering the effects of solar tracking | |
| CN107133694B (en) | Optimization method of mirror field scheduling period for tower solar thermal power station | |
| Oberkirsch et al. | Validation of a closed-loop aim point management system at the jülich solar tower | |
| CN105205562B (en) | The running optimizatin method of solar power tower receiver | |
| Li et al. | Parametric analysis on G-PV/T collector: Performance optimization and energy trade-off among two critical structures under various outside conditions | |
| Yang et al. | Active disturbance rejection coordinated control for integrated solar combined cycle system considering system inertia difference | |
| CN118153426A (en) | Real-time optimization method and system for focusing strategy of tower type photo-thermal mirror field | |
| Camacho et al. | Control of solar power systems: a survey | |
| CN111767646B (en) | Optimization design method of receiver for tower solar thermal power station | |
| Lengge et al. | Study on dynamic model and dynamic characteristics of Delingha 50 MW trough solar field | |
| CN115271244A (en) | Two-stage distribution robust optimization-based short-term peak regulation model of cascade hydropower station | |
| Oudah et al. | Wind turbines control: Features and trends | |
| Liang et al. | Simulation research on the grid connected generation system of solar thermal power generation | |
| Singh et al. | Use of response surface methodology and CFD for analysing and optimization of simple-air cooled solar panel | |
| Hassan et al. | Adaptive look-ahead feed-forward wind turbine pitch control using lidar technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |