CN115329487A - Parameter setting method for photovoltaic power generation system and terminal equipment - Google Patents
Parameter setting method for photovoltaic power generation system and terminal equipment Download PDFInfo
- Publication number
- CN115329487A CN115329487A CN202210976141.1A CN202210976141A CN115329487A CN 115329487 A CN115329487 A CN 115329487A CN 202210976141 A CN202210976141 A CN 202210976141A CN 115329487 A CN115329487 A CN 115329487A
- Authority
- CN
- China
- Prior art keywords
- power generation
- photovoltaic
- igbt
- generation system
- photovoltaic power
- 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.)
- Pending
Links
- 238000010248 power generation Methods 0.000 title claims abstract description 121
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000005286 illumination Methods 0.000 claims abstract description 33
- 230000007613 environmental effect Effects 0.000 claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims description 51
- 238000004422 calculation algorithm Methods 0.000 claims description 37
- 230000006870 function Effects 0.000 claims description 36
- 238000004590 computer program Methods 0.000 claims description 15
- 238000010801 machine learning Methods 0.000 claims description 14
- 239000011159 matrix material Substances 0.000 claims description 14
- 238000012545 processing Methods 0.000 claims description 14
- 238000005457 optimization Methods 0.000 abstract description 5
- 238000011217 control strategy Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 6
- 239000013598 vector Substances 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 238000012937 correction Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000003066 decision tree Methods 0.000 description 3
- 238000012854 evaluation process Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 230000035772 mutation Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000035882 stress Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000008646 thermal stress Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/04—Ageing analysis or optimisation against ageing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/06—Power analysis or power optimisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/08—Thermal analysis or thermal optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Economics (AREA)
- Computer Hardware Design (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Mathematical Optimization (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Mathematics (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Photovoltaic Devices (AREA)
Abstract
The embodiment of the invention relates to the technical field of distributed photovoltaic power generation system parameter optimization, and discloses a photovoltaic power generation system parameter setting method and terminal equipment. The parameter setting method of the photovoltaic power generation system comprises the following steps: collecting historical illumination intensity data and environmental temperature data of the photovoltaic power generation system; establishing an IGBT service life estimation model, wherein the input quantity of the IGBT service life estimation model comprises historical illumination intensity data, environmental temperature data and photovoltaic power generation system parameters, and the output quantity is IGBT service life and net increase power generation quantity; initializing a population, wherein individuals in the population are parameters of a photovoltaic power generation system; and constructing a fitness function through the IGBT life estimation model, optimizing the population through differential evolution by taking the fitness function as a basis, and determining the optimal individual, wherein the optimal individual represents the optimal parameter of the photovoltaic power generation system. The reliability of the photovoltaic inverter is improved while the parameters of the photovoltaic power generation system are optimized.
Description
Technical Field
The invention relates to distributed photovoltaic power generation system parameter optimization, in particular to a photovoltaic power generation system parameter setting method and terminal equipment.
Background
At present, photovoltaic power generation has entered into a stage of overall development, and a photovoltaic inverter is low in reliability and short in service life, so that a photovoltaic power generation system is high in maintenance cost. In order to reduce the photovoltaic power generation cost and improve the solar power generation competitiveness, higher requirements are put on the reliability of a photovoltaic inverter which is a key component of a photovoltaic power generation system.
The power device IGBT is a core module of the photovoltaic inverter, junction temperature fluctuation of the power device IGBT module is large due to randomness and intermittence of photovoltaic power generation, the service life of the IGBT module is influenced, and therefore reliability of the photovoltaic inverter is reduced. The IGBT service life evaluation is an important step in the photovoltaic inverter reliability analysis process, junction Temperature (Junction Temperature) is the actual working Temperature of a semiconductor in electronic equipment, and the IGBT service life evaluation firstly needs to know Junction Temperature data of the IGBT.
In the prior art, dynamic thermal stress of the IGBT is generally obtained through an IGBT power loss model and thermal model simulation, and then IGBT junction temperature is obtained, but the method cannot be applied to analysis under a long time dimension. In order to solve the problem, some students use a table look-up method to calculate the junction temperature of the IGBT, but the calculation precision of the method is poor; some students also propose a junction temperature calculation simplification method aiming at the power device under the drought climate condition, but the method is only suitable for junction temperature calculation under the special environment of the drought climate; meanwhile, an Artificial Neural Network (ANN) based model is proposed to replace the power loss and thermal model in the reliability evaluation process, but the junction temperature calculation process is based on the fixed ambient temperature and cannot be calculated for the scene of the change of the ambient temperature. An IGBT junction temperature calculation method suitable for long-time dimensional temperature change is needed, and conditions are provided for photovoltaic inverter state analysis and configuration optimization.
Currently, due to the relatively low cost of photovoltaic modules, the power rating of photovoltaic arrays can be designed to be higher than the power rating of photovoltaic inverters in low light intensity areas to enable more power to be generated during off-peak hours. However, during peak periods, the output power of the photovoltaic array is greater than the rated power of the photovoltaic inverter, causing damage to the photovoltaic inverter. Therefore, how to find the parameter balance point of the maximum photovoltaic power generation power while maintaining the reliability of the photovoltaic inverter becomes a new problem.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method for setting parameters of a photovoltaic power generation system, which optimizes the parameters of the photovoltaic power generation system and improves the reliability of a photovoltaic inverter.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, an embodiment of the present invention provides a method for setting parameters of a photovoltaic power generation system, including: collecting historical illumination intensity data and environmental temperature data of the photovoltaic power generation system; establishing an IGBT service life estimation model, wherein the input quantity of the IGBT service life estimation model comprises historical illumination intensity data, environmental temperature data and photovoltaic power generation system parameters, and the output quantity is IGBT service life and net increase power generation quantity; initializing a population, wherein individuals in the population are parameters of a photovoltaic power generation system; and constructing a fitness function through the IGBT life estimation model, optimizing the population through differential evolution by taking the fitness function as a basis, and determining the optimal individual, wherein the optimal individual represents the optimal parameter of the photovoltaic power generation system.
In some embodiments, the service life of the IGBT is brought into a fitness function for evaluating individual preference of the population, the optimal parameters of the photovoltaic power generation system are obtained based on a differential evolution algorithm, and the reliability of the photovoltaic inverter is improved while the parameters of the photovoltaic power generation system are optimized. The core of combining the IGBT service life and the fitness is an IGBT service life estimation model, the IGBT service life estimation model can reduce the time consumed by IGBT junction temperature calculation, simplify the reliability evaluation process of the photovoltaic inverter and provide an efficient and accurate method for determining the optimal parameters of the photovoltaic system.
Based on the first aspect, in some embodiments, the IGBT life estimation model includes a data processing module, a photovoltaic array maximum power calculation module, a photovoltaic power generation system parameter collection module, a photovoltaic inverter output power calculation module, a junction temperature calculation module, and a life estimation module; the data processing module is used for processing historical illumination intensity data and environmental temperature data, extracting the data and uploading the data to the photovoltaic array maximum power calculating module and the junction temperature calculating module.
Based on the first aspect, in some embodiments, the photovoltaic array maximum power calculation module is to determine a maximum power point in the matrix of photovoltaic array output characteristics; determining a maximum power point in a photovoltaic array output characteristic matrix, comprising: inputting historical illumination intensity data and environmental temperature data into a photovoltaic power generation engineering calculation model, and calculating to obtain a photovoltaic array output characteristic matrix; and traversing and searching the output characteristic matrix of the photovoltaic array by a short-step traversal search method to determine a maximum power point.
Based on the first aspect, in some embodiments, the photovoltaic power generation system parameter collection module is configured to receive the photovoltaic power generation system parameters and transmit the photovoltaic power generation parameters to the photovoltaic inverter output power calculation module; the photovoltaic power generation system parameters are individuals in the initialized population, and the IGBT service life estimation model is input for fitness calculation.
Based on the first aspect, in some embodiments, the pv inverter output power calculation module is configured to determine the pv inverter output power according to the pv power generation system parameters and a maximum power point in the pv array output characteristic matrix; the output power of the photovoltaic inverter is as follows:
wherein P (t) is output power without considering the capacity ratio and the power limit value of the photovoltaic power generation system, and P inv (t) is the output power when the capacity-ratio and power limit of the photovoltaic power generation system are taken into consideration, P limit Is the power limit, R s Is the volume ratio of the photovoltaic power generation system.
Based on the first aspect, in some embodiments, the junction temperature calculation module is configured to calculate, according to the historical illumination intensity data, the ambient temperature data, and the photovoltaic inverter output power, IGBT junction temperature data.
Based on the first aspect, in some embodiments, calculating IGBT junction temperature data according to the historical illumination intensity data, the ambient temperature data, and the photovoltaic inverter output power includes: and calculating to obtain IGBT junction temperature data through an XGboost machine learning algorithm model, wherein the input quantity of the XGboost machine learning algorithm model is historical illumination intensity data, environmental temperature data and photovoltaic inverter output power, and the output quantity is IGBT junction temperature data.
Based on the first aspect, in some embodiments, the lifetime estimation module is configured to calculate an IGBT lifetime based on the IGBT junction temperature data; calculating the IGBT life based on IGBT junction temperature data, including: extracting IGBT junction temperature cycle information in IGBT junction temperature data through a rain flow counting method; calculating the cycle failure number through a Bayer life model based on IGBT junction temperature cycle information; and calculating the annual damage of the IGBT according to the cycle failure number, and further calculating to obtain the service life of the IGBT.
Based on the first aspect, in some embodiments, constructing the fitness function by the IGBT lifetime estimation model includes: obtaining the IGBT service life through an IGBT service life estimation model; constructing a fitness function based on the IGBT service life and the net increase power generation amount, wherein the net increase power generation amount is calculated by initializing the known photovoltaic power generation system parameters in the population; the fitness function is: fitness = LC damage /F j Wherein LC damage Is an IGBT annual damage, said IGBT annual damage being the reciprocal of IGBT lifetime; f j The net generated energy is generated after the capacity ratio and the limited power are considered by the photovoltaic system.
In a second aspect, an embodiment of the present invention provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the steps of the photovoltaic power generation system parameter tuning method according to any one of the first aspect.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a parameter setting method of a photovoltaic power generation system according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an IGBT lifetime estimation model provided by the embodiment of the invention;
FIG. 3 is a flowchart of a maximum output power calculation of a photovoltaic array according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a power-limited control strategy provided by an embodiment of the invention;
FIG. 5 is a block diagram of a limited power control strategy provided by an embodiment of the invention;
FIG. 6 is a flow chart of a method for calculating a life estimation module according to an embodiment of the invention;
FIG. 7 is a plot of annual light intensity data from the Malaysia area provided by an embodiment of the present invention;
FIG. 8 is a plot of annual ambient temperature data from the Malaysia area provided by an embodiment of the present invention;
FIG. 9 is a diagram illustrating the effect of power control according to an embodiment of the present invention;
FIG. 10 is a diagram of a fitness curve of a differential evolution algorithm provided by an embodiment of the present invention;
fig. 11 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
The present invention will be more clearly described below with reference to specific examples. The following examples will assist those skilled in the art in further understanding the role of the invention, but are not intended to limit the invention in any way. It should be noted that various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention.
To make the objects, technical solutions and advantages of the present application more apparent, the following description is given by way of example with reference to the accompanying drawings.
In the operation management of the photovoltaic power generation system, how to determine the photovoltaic power generation system parameters meeting the reliability requirements of the photovoltaic inverter becomes a key problem, and aiming at the problem, the invention builds a micro-grid distributed photovoltaic power generation system parameter setting system based on IGBT service life estimation, and realizes the determination of the optimal capacity ratio and the power limit value of the photovoltaic power generation system under the condition of considering the IGBT service life and the net generated energy.
As shown in fig. 1, the method for tuning parameters of a photovoltaic power generation system may include steps S101 to S104.
Step S101: historical illumination intensity data and environmental temperature data of the photovoltaic power generation system are collected.
Step S102: and establishing an IGBT service life estimation model, wherein the input quantity of the IGBT service life estimation model comprises historical illumination intensity data, environment temperature data and photovoltaic power generation system parameters, and the output quantity is IGBT service life and net increase power generation quantity.
As shown in fig. 2, the IGBT lifetime estimation model includes a data processing module, a photovoltaic array maximum power calculation module, a photovoltaic power generation system parameter collection module, a photovoltaic inverter output power calculation module, a junction temperature calculation module, and a lifetime estimation module.
The photovoltaic power generation system comprises a photovoltaic array maximum power calculation module, a photovoltaic inverter output power calculation module, a junction temperature calculation module, a data processing module, a photovoltaic array maximum power calculation module, a photovoltaic inverter output power calculation module, a photovoltaic power generation system parameter collection module, a photovoltaic inverter output power calculation module, a junction temperature calculation module and a service life estimation module.
The data processing module is used for processing historical illumination intensity data and environmental temperature data, extracting the data and uploading the data to the photovoltaic array maximum power calculating module and the junction temperature calculating module.
The photovoltaic array maximum power calculation module is used for determining a maximum power point in the photovoltaic array output characteristic matrix.
In some embodiments, the calculation method of the photovoltaic array maximum power calculation module is to input historical illumination intensity data and environmental temperature data into the photovoltaic power generation engineering calculation model, and calculate to obtain the photovoltaic array output characteristic matrix. And traversing and searching the output characteristic matrix of the photovoltaic array by a short-step traversal search method to determine a maximum power point.
The photovoltaic power generation engineering calculation model comprises the following steps:
ΔT＝T-T ref (1)
V′ oc ＝V oc (1-cΔT)ln(1+bΔS) (4)
V' m ＝V m (1-cΔT)ln(1+bΔS) (6)
wherein S is the illumination intensity, T is the ambient temperature, S ref For reference illumination intensity, 1000W/m 2 ；T ref 25 ℃ for reference ambient temperature; Δ T is an ambient temperature correction amount; Δ S is the illumination intensity correction amount; i is sc Is the short circuit current value of the photovoltaic cell, V oc Is the open-circuit voltage value of the photovoltaic cell, I m A current value, V, corresponding to the maximum power point of the photovoltaic cell m The compensation coefficients a, b and c are constants corresponding to the maximum power point of the photovoltaic cell, and are generally a =0.0025 (DEG C) -1 ，b＝0.0005(W/m 2 ) -1 ，c＝0.00288(℃) -1 ，I’ sc 、I’ m 、V’ OC And V' m Are respectively I sc 、I m 、V oc And V m The correction amount of (1).
In consideration of the long time consumption of analog simulation, the invention provides a photovoltaic array maximum output power numerical calculation method based on short-step traversal search in order to obtain the photovoltaic array output maximum power in a one-year time period in a short time. When the local shadow condition is not considered, the photovoltaic array output P-U characteristic has strict concave function property, namely, the whole situation has only one maximum power point, so that the maximum power point can be obtained through traversal search. The specific process is shown in FIG. 3, the illumination intensity S and the ambient temperature T are input, and the correction coefficient D is calculated 1 ，D 2 Wherein, in the step (A),
D 2 ＝(1-cΔT)ln(1+bΔS)。
photovoltaic arraySetting the output initial voltage to 0, calculating the output current I L And the output power P, the voltage iteration step length each time is 0.01V, and the photovoltaic array output voltage feasible region is
U pv ∈{0,0.01,0.02,…U mpp …,U oc } (7)
The output power of the photovoltaic array can be operated in the range of
P pv ∈{P 1 ,P 2 ,P 3 ,…P mpp …,P n } (8)
Wherein, P n And outputting power for the photovoltaic panel. With increasing cycle number, when power down occurs, i.e. P i+1 <P i Or the voltage U is greater than the open-circuit voltage U oc Ending the circulation to obtain the maximum power P mpp And the voltage U corresponding to the maximum power point mpp 。
The photovoltaic power generation system parameter collection module is used for receiving the photovoltaic power generation system parameters and transmitting the photovoltaic power generation parameters to the photovoltaic inverter output power calculation module. The photovoltaic power generation system parameters are individuals in the initialized population, and the photovoltaic power generation system parameters are input into the IGBT life estimation model as input quantities to carry out fitness calculation. The parameters of the photovoltaic power generation system comprise the capacity ratio of the photovoltaic power generation system and the power limit value of the photovoltaic power generation system.
The volume ratio of the photovoltaic power generation system is defined as follows:
wherein, P pv,rated Rated power for the photovoltaic power generation system; p is inv,rated The rated power of the photovoltaic inverter.
Illustratively, if R s >1, the photovoltaic power generation system can generate more electric energy under the condition of low illumination intensity; general formula R s ∈[1,1.5]。
When the capacity ratio of the photovoltaic power generation system is more than 1, the output power of the inverter is more than the rated power of the inverter under high illumination intensity, and further the reliable operation of the photovoltaic inverter is reducedAnd the working life of the inverter is shortened, so that a power limit value P needs to be set for the output of the photovoltaic inverter limit 。
In particular, when the photovoltaic inverter outputs power P pv Greater than power limit P limit While the output power is limited to P limit (ii) a When the output power P of the photovoltaic inverter pv When the output power is less than the power limit value, the output power is the maximum power under the MPPT control strategy, namely
The power limit of a photovoltaic power generation system is defined as:
exemplary if K s The output power of the photovoltaic inverter can not exceed the rated capacity of the photovoltaic inverter under high illumination intensity, and the operation reliability of the photovoltaic inverter is ensured. General K s ∈[0.5,1]。
Illustratively, to implement a limited power control strategy, as shown in FIG. 4. When the illumination intensity changes, the output power of the photovoltaic power supply after the maximum power tracking is larger than the output power limit value of the photovoltaic power supply, and at the moment, the voltage reference value of the photovoltaic power supply needs to change along the direction of reducing the output power, namely, the voltage reference value of the photovoltaic power supply changes through the change
Calculating voltages corresponding to the variable power points
The control block diagram of the limited power control strategy is shown in fig. 5, when the output power P of the photovoltaic inverter is pv Below the power limit P limit Then, the maximum power output is controlled according to the MPPT control strategy, and then the control parameter k p 、k i And s, adjusting the output power; when the output power P of the photovoltaic inverter pv Above power limit P limit When the output power is limited at the power limit value according to the VPPT control strategy, and then the control parameter k is used p 、k i And s setting the output power.
In some embodiments, the photovoltaic inverter output power calculation module is configured to determine the photovoltaic inverter output power based on the photovoltaic power generation system parameters and a maximum power point in the matrix of photovoltaic array output characteristics.
Calculating the output power of the photovoltaic inverter by considering the capacity ratio and the power limit value, namely
P (t) is output power without considering the capacity ratio and the power limit value of the photovoltaic power generation system; p inv And (t) the output power when the capacity ratio and the power limit value of the photovoltaic power generation system are considered.
In some embodiments, the junction temperature calculation module is configured to calculate IGBT junction temperature data according to the historical illumination intensity data, the ambient temperature data, and the photovoltaic inverter output power.
Specifically, IGBT junction temperature data are obtained through calculation of an XGboost machine learning algorithm model, wherein the input quantity of the XGboost machine learning algorithm model is historical illumination intensity data, environmental temperature data and photovoltaic inverter output power, and the output quantity of the XGboost machine learning algorithm model is IGBT junction temperature data.
To more clearly understand the present invention, the XGBoost machine learning algorithm is introduced first. The XGboost machine learning algorithm is trained by using annual illumination intensity data, environmental temperature data and photovoltaic inverter output power of a photovoltaic power generation system as input of an XGboost model and using IGBT junction temperature obtained by calculating an IGBT junction temperature value as output. The XGboost machine learning algorithm adopts a decision tree as a base learner, a plurality of weak learners are constructed, a model is continuously trained along the direction of reducing gradient in the iterative learning process, a second-order Taylor series is utilized to expand a loss function, and a regular term is added into a target function to solve an overall optimal solution so as to control the accuracy and the complexity of the model. The method has the advantages of over-fitting prevention, high speed, multi-thread parallel processing and the like, and is one of the most successful machine learning methods at present.
The XGboost algorithm, as a supervised ensemble learning algorithm, is understood to be a summation model of a plurality of decision trees, assuming that the model has k decision trees, i.e.
F＝{f t (x i )＝ω q(xi) } (15)
Wherein k is the number of trees; f. of t Is a function in the function space F;
is a model predicted value; x is the number of i Is the input ith data; q (x) i ) Representing a sample x i Falling to the corresponding leaf node; omega q(xi) Representing a sample x i Leaf node weights that fall on corresponding leaf nodes.
Illustratively, in the present invention
Predicting an IGBT junction temperature model; sample x i The data of the illumination intensity, the data of the ambient temperature and the output power of the photovoltaic inverter are obtained.
The XGboost algorithm adopts an addition model and a forward distribution algorithm, each iteration does not affect the model, namely the original model is kept unchanged, and a new function is added into the model.
The XGboost objective function is divided into two parts, wherein the first part is a loss difference function, and the second part is a regularization term, as shown in a formula (17-18)
Wherein, the first and the second end of the pipe are connected with each other,
the difference value between the predicted value and the true value is obtained;
the complexity of the tree is controlled as a regular term to prevent overfitting; t is the number of leaf nodes; gamma is a penalty function of T; λ is a regularization penalty term coefficient; omega is leaf node weight; n is the number of samples.
The optimization objective is that the function objective reaches a minimum, i.e.
When the t-th tree is trained, the objective function is:
rewriting an objective function to traverse about leaf nodes
According to
Specifically, the junction temperature of the IGBT is calculated by using an XGboost machine learning algorithm based on photovoltaic power generation data and the output power of a photovoltaic inverter, and the method comprises the following steps of using a second-order Taylor series to express an objective function:
wherein the content of the first and second substances,
the difference value between the predicted value and the true value of the IGBT junction temperature is obtained; t is the number of leaf nodes; gamma is a penalty function for T; λ is a regularization penalty term coefficient; omega is leaf node weight; n is the number of samples;
simplifying the objective function by removing constant terms, i.e.
Wherein, the first and the second end of the pipe are connected with each other,
when in use
When the utility model is used, the water is discharged,
the minimum value is obtained as:
in some embodiments, after the XGBoost algorithm is used to calculate the IGBT junction temperature data, the life estimation module calculates the IGBT life based on the IGBT junction temperature data.
As shown in fig. 6, the method of calculating the lifetime of the IGBT based on the IGBT junction temperature data may include steps S201 to S203.
S201: and extracting IGBT junction temperature cycle information in the IGBT junction temperature data by a rain flow counting method.
The rain flow counting method is the most common data counting method in the current engineering application, carries out mathematical statistics on the time history of the stress borne by the device by utilizing the memory characteristic of materials, has a mechanical concept, and is widely applied to the calculation of the accumulated fatigue life of the device. And extracting IGBT junction temperature cycle information in the IGBT junction temperature data by a rain flow counting method.
S202: and calculating the cycle failure number through a Bayer life model based on the IGBT junction temperature cycle information.
The Bayer life model is obtained by carrying out a large number of power cycle tests on IGBT modules with different power levels in the fourth generation of Infineon company, and the Bayer life model has the advantages of multiple contained variables, comprehensive considered parameters and higher calculation precision.
Illustratively, the cycle failure number is calculated based on the IGBT low-frequency junction temperature cycle information and the bayer life model, and includes:
by passing
Calculating the number of cyclic failures N f (ii) a Wherein, delta T j For IGBT junction temperature fluctuations, T jmin Minimum junction temperature, t, of IGBT on I is the current flowing through each bonding wire for heating time; d is the diameter of the bonding wire; v is a blocking voltage; a and beta 1-6 Are bayer life model parameters.
S203: and calculating the annual damage of the IGBT according to the cycle failure number, and further calculating to obtain the service life of the IGBT.
Based on the cycle failure number and the Miner criterion, calculating to obtain the annual damage of the IGBT in the photovoltaic inverter, wherein the method comprises the following steps:
by passing
The cumulative annual damage LC of the power device is calculated.
Illustratively, the accumulated damage of the IGBT fundamental frequency junction temperature is affected by the sampling period, and if the sampling period is 1 h/sampling point, the lifetime damage degree per hour is an accumulated unit of the annual lifetime damage degree, which can be obtained by a Miner linear accumulated damage criterion:
specifically, the expression of the lifetime of the IGBT is:
step S103: and initializing a population, wherein the individuals in the population are parameters of the photovoltaic power generation system.
And setting the maximum iteration times, the initial population, the cross probability and the variation probability of the differential evolution algorithm. Population initialization is to generate NP real number parameter vectors with dimension D, and the initial population conforms to uniform probability distribution:
X i (0)＝(x i,1 (0),x i,2 (0),x i,3 (0),...,x i,D (0)) (31)
i＝1,2,3,...,NP (32)
the j dimension value of the ith individual is as follows:
X i,j (0)＝L j_min +rand(0,1)·(L j_max -L j_min ) (33)
i＝1,2,3,...,NP (34)
j＝1,2,3,…,D (35)
wherein L is j_min ，L j_max Respectively, the minimum value and the maximum value of the ith individual in the jth dimension, namely, the minimum value and the maximum value of the ith group capacity ratio and the power limit value in the jth dimension.
Step S104: and constructing a fitness function through the IGBT life estimation model, optimizing the population through differential evolution by taking the fitness function as a basis, and determining the optimal individual, wherein the optimal individual represents the optimal parameter of the photovoltaic power generation system.
In some embodiments, the IGBT lifetime is obtained by an IGBT lifetime estimation model. And constructing a fitness function based on the IGBT service life and the net increase power generation amount, wherein the net increase power generation amount is calculated by initializing the known parameters of the photovoltaic power generation system in the population.
The fitness function is:
fitness＝LC damage /F j (36)
the fitness value of each individual is calculated by a fitness function. Wherein LC damage Is IGBT annual damage which is the reciprocal of IGBT life, F j The net generated energy is generated after the capacity ratio and the limited power are considered by the photovoltaic system.
In some embodiments, outputting the optimal capacity fraction and power limit of the photovoltaic power generation system based on the fitness value using a differential evolution algorithm includes: carrying out differential evolution algorithm variation operation, differential evolution algorithm cross operation and differential evolution algorithm selection operation; meeting the requirement of preset individual fitness when performing differential evolution algorithm selection operation; and judging whether the maximum iteration number is reached or not, and outputting the capacity ratio and the power limit value of the optimal photovoltaic power generation system.
Illustratively, performing a differential evolution algorithm mutation operation includes:
v i,G+1 ＝x r1,G +f·(x r2,G -x r3,G ) (36)
wherein f is a mutation operator, and is usually set between 0 and 2; population individual r 1 ，r 2 ，r 3 Are different from each other, r 1 ，r 2 ，r 3 Are different from i; the population number NP must be greater than 4.
Illustratively, to increase the diversity of the interference vectors, a crossover operation is introduced. Performing a differential evolution algorithm crossover operation includes:
wherein cr ∈ [0,1] is the crossover probability.
Illustratively, to decide whether an individual in the selected population can become an individual in the next generation population, the trial vector is compared to the current objective vector, and if the emerging objective function is minimized, the vector with the smallest objective function will emerge in the next generation. The test vector is compared to only one individual, not all individuals. The differential evolution algorithm selection operation comprises the following steps:
wherein, the fitness value X i(g+1) Is better than or equal to X i(g) 。
And finally judging whether the maximum iteration frequency is reached, finishing the iteration operation when the maximum iteration frequency is reached, outputting the optimal capacity ratio of the photovoltaic power generation system obtained by a differential evolution algorithm and taking the one-year task section of Malaysia as an example, considering the service life of an IGBT (insulated gate bipolar transistor) in the photovoltaic inverter and the net generated energy of the photovoltaic system, and designing the optimal capacity ratio and the power limit value of the photovoltaic system in the area, wherein the IGBT module adopts an FF100R12RT4 module of Infineon company, and the photovoltaic inverter is in a two-stage three-phase mode.
Example 1, annual light intensity and ambient temperature data were collected in malaysia, and annual light intensity and ambient temperature data in malaysia are shown in fig. 7 and 8.
And calculating to obtain the maximum output power of the photovoltaic array according to a photovoltaic power generation engineering calculation model and a short-step traversal search photovoltaic array maximum output power numerical calculation method.
The maximum iteration number of the differential evolution algorithm is set to be 200, the number of initial populations is 50, the dimensionality is 2 dimensions, the cross probability is 0.2, and the variation probability is 0.35.
With R s ＝1.3，K s =0.9 as example, the invention providesThe effect of the limiting strategy is shown in fig. 9, and it can be seen that the limiting power control strategy provided by the present invention is effective.
And calculating according to the capacity ratio and the power limit value to obtain the output power of the photovoltaic inverter. Then, inputting the extracted annual environmental temperature data and illumination intensity data and the calculated photovoltaic inverter output power data, and obtaining IGBT junction temperature T by using a trained XGboost model j . The main parameters of the IGBT model and the XGboost model adopted in the operation are shown in the table 1.
TABLE 1 IGBT Module and XGboost model Main parameters
And obtaining IGBT low-frequency junction temperature cycle information by using a rain flow counting method, and obtaining cycle failure number by using a Bayer life model through calculation, wherein Bayer model parameters are shown in a table 2.
TABLE 2 Bayer Life model parameters
And estimating the service life of the IGBT according to the calculated annual damage of the photovoltaic inverter IGBT, calculating a fitness value according to the estimated service life of the IGBT and net power generation amount, and then performing differential evolution algorithm variation, intersection and selection operations.
And finally, based on the fitness value, outputting the optimal capacity ratio and the power limit value of the photovoltaic power generation system by using a differential evolution algorithm: judging whether the maximum iteration times is reached, and if the maximum iteration times is not reached, repeating the operation; and if the maximum iteration times are reached, ending the iteration operation, and outputting the capacity ratio and the power limit value of the optimal photovoltaic power generation system obtained by the differential evolution algorithm. The fitness curve of the differential evolution algorithm is shown in FIG. 10, where the optimal capacity-ratio and the power limit are R s ＝1.309841，K s ＝0.8517895。
According to the power control method of the microgrid distributed photovoltaic power generation system, the net generated energy is calculated through the capacity ratio and the power limit value data, the junction temperature of the IGBT is calculated by utilizing the XGboost machine learning algorithm, and the service life of the IGBT is estimated; under the condition that the service life of the IGBT and the net increase power generation amount are considered, the optimal capacity ratio and the power limit value of the photovoltaic power generation system are obtained based on the differential evolution algorithm, junction temperature calculation time consumption of the IGBT can be reduced, the reliability evaluation process of the photovoltaic inverter is simplified, and an effective optimization method is provided for determining optimal parameters of the capacity ratio and the power limit value of the photovoltaic system.
Fig. 11 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 11, the terminal device 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82, such as a photovoltaic power generation system parameter tuning program, stored in the memory 81 and operable on the processor 80. The processor 80, when executing the computer program 82, implements the steps in the above-described photovoltaic power generation system parameter tuning method embodiment, such as the steps S101 to S103 shown in fig. 1.
Illustratively, the computer program 82 may be partitioned into one or more modules/units, which are stored in the memory 81 and executed by the processor 80 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 82 in the terminal device 8.
The terminal device 8 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 80, a memory 81. Those skilled in the art will appreciate that fig. 11 is merely an example of a terminal device 8 and does not constitute a limitation of terminal device 8 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The processor 80 may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 81 may be an internal storage unit of the terminal device 8, such as a hard disk or a memory of the terminal device 8. The memory 81 may also be an external storage device of the terminal device 8, such as a plug-in hard disk provided on the terminal device 8, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card (FlashCard), and the like. Further, the memory 81 may also include both an internal storage unit and an external storage device of the terminal device 8. The memory 81 is used for storing the computer program and other programs and data required by the terminal device. The memory 81 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention. For the specific working processes of the units and modules in the system, reference may be made to the corresponding processes in the foregoing method embodiments, which are not described herein again.
In the above embodiments, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described or recited in any embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A parameter setting method for a photovoltaic power generation system is characterized by comprising the following steps:
collecting historical illumination intensity data and environmental temperature data of the photovoltaic power generation system;
establishing an IGBT life estimation model, wherein the input quantity of the IGBT life estimation model comprises the historical illumination intensity data, the environmental temperature data and the photovoltaic power generation system parameters, and the output quantity is the IGBT life and the net power generation amount;
initializing a population, wherein individuals in the population are parameters of the photovoltaic power generation system;
and constructing a fitness function through the IGBT life estimation model, optimizing the population through differential evolution by taking the fitness function as a basis, and determining an optimal individual, wherein the optimal individual represents the optimal parameter of the photovoltaic power generation system.
2. The method for tuning parameters of a photovoltaic power generation system according to claim 1, wherein the IGBT life estimation model includes a data processing module, a photovoltaic array maximum power calculation module, a photovoltaic power generation system parameter collection module, a photovoltaic inverter output power calculation module, a junction temperature calculation module, and a life estimation module;
the data processing module is used for processing the historical illumination intensity data and the environmental temperature data, extracting data and uploading the data to the photovoltaic array maximum power calculating module and the junction temperature calculating module.
3. The photovoltaic power generation system parameter setting method according to claim 2, wherein the photovoltaic array maximum power calculation module is configured to determine a maximum power point in a photovoltaic array output characteristic matrix;
the determining a maximum power point in a matrix of output characteristics of a photovoltaic array comprises:
inputting the historical illumination intensity data and the environmental temperature data into a photovoltaic power generation engineering calculation model, and calculating to obtain a photovoltaic array output characteristic matrix;
and traversing and searching the output characteristic matrix of the photovoltaic array by a short-step traversal search method, and determining the maximum power point.
4. The method according to claim 2, wherein the photovoltaic power generation system parameter collecting module is configured to receive the photovoltaic power generation system parameter and transmit the photovoltaic power generation parameter to the photovoltaic inverter output power calculating module;
and inputting the parameters of the photovoltaic power generation system into the IGBT life estimation model for fitness calculation, wherein the parameters of the photovoltaic power generation system are individuals in the initialization population.
5. The photovoltaic power generation system parameter setting method according to claim 3, wherein the photovoltaic inverter output power calculation module is configured to determine a photovoltaic inverter output power according to the photovoltaic power generation system parameter and the maximum power point in the photovoltaic array output characteristic matrix;
the output power of the photovoltaic inverter is as follows:
wherein P (t) is output power without considering the capacity ratio and the power limit value of the photovoltaic power generation system, and P inv (t) is the output power when the capacity-ratio and power limit of the photovoltaic power generation system are taken into consideration, P limit Is the power limit, R s Is the volume ratio of the photovoltaic power generation system.
6. The method for setting parameters of a photovoltaic power generation system according to claim 5, wherein the junction temperature calculation module is configured to calculate IGBT junction temperature data according to historical illumination intensity data, ambient temperature data, and the photovoltaic inverter output power.
7. The method for tuning parameters of a photovoltaic power generation system according to claim 6, wherein said calculating IGBT junction temperature data from said historical illumination intensity data and ambient temperature data and said photovoltaic inverter output power comprises:
and calculating to obtain the IGBT junction temperature data through an XGboost machine learning algorithm model, wherein the input quantity of the XGboost machine learning algorithm model is historical illumination intensity data, environmental temperature data and the output power of the photovoltaic inverter, and the output quantity of the XGboost machine learning algorithm model is the IGBT junction temperature data.
8. The photovoltaic power generation system parameter tuning method of claim 7, wherein the lifetime estimation module is configured to calculate an IGBT lifetime based on the IGBT junction temperature data;
the calculating the IGBT lifetime based on the IGBT junction temperature data comprises:
extracting IGBT junction temperature cycle information in the IGBT junction temperature data through a rain flow counting method;
calculating the cycle failure number through a Bayer life model based on the IGBT junction temperature cycle information;
and calculating the annual damage of the IGBT according to the cycle failure number, and further calculating to obtain the service life of the IGBT.
9. The method for tuning parameters of a photovoltaic power generation system according to claim 8, wherein the constructing a fitness function through the IGBT lifetime estimation model comprises:
obtaining the IGBT service life through the IGBT service life estimation model;
constructing a fitness function based on the IGBT service life and the net increase power generation amount, wherein the net increase power generation amount is calculated through known photovoltaic power generation system parameters in the initialization population;
the fitness function is:
fitness＝LC damage /F j
wherein LC damage Is an IGBT annual damage, said IGBT annual damage being the reciprocal of the IGBT lifetime; f j The net generated energy is generated after the capacity ratio and the limited power are considered by the photovoltaic system.
10. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method for photovoltaic power generation system parameter tuning according to any one of claims 1 to 9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210976141.1A CN115329487A (en) | 2022-08-15 | 2022-08-15 | Parameter setting method for photovoltaic power generation system and terminal equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210976141.1A CN115329487A (en) | 2022-08-15 | 2022-08-15 | Parameter setting method for photovoltaic power generation system and terminal equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115329487A true CN115329487A (en) | 2022-11-11 |
Family
ID=83924456
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210976141.1A Pending CN115329487A (en) | 2022-08-15 | 2022-08-15 | Parameter setting method for photovoltaic power generation system and terminal equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115329487A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117578597A (en) * | 2024-01-19 | 2024-02-20 | 杭州利沃得电源有限公司 | Energy-saving control method and system for photovoltaic inverter system |
-
2022
- 2022-08-15 CN CN202210976141.1A patent/CN115329487A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117578597A (en) * | 2024-01-19 | 2024-02-20 | 杭州利沃得电源有限公司 | Energy-saving control method and system for photovoltaic inverter system |
| CN117578597B (en) * | 2024-01-19 | 2024-04-05 | 杭州利沃得电源有限公司 | Energy-saving control method and system for photovoltaic inverter system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108921339B (en) | Quantile regression-based photovoltaic power interval prediction method for genetic support vector machine | |
| CN111563611A (en) | Cloud data center renewable energy space-time prediction method for orientation graph convolutional network | |
| CN108063456B (en) | Distributed photovoltaic power generation grid-connected planning method and terminal equipment | |
| CN115270640A (en) | Configuration optimization method for distributed photovoltaic power generation system of microgrid and terminal equipment | |
| Takyi-Aninakwa et al. | A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures | |
| CN115759336A (en) | Prediction method and storage medium for short-term power load prediction | |
| CN115329487A (en) | Parameter setting method for photovoltaic power generation system and terminal equipment | |
| CN116702992A (en) | Power generation power prediction method and device, electronic equipment and storage medium | |
| Vilsen et al. | Transfer learning for adapting battery state-of-health estimation from laboratory to field operation | |
| CN114154684A (en) | Short-term photovoltaic power prediction method based on data mining and multi-core support vector machine | |
| Wu et al. | Uncertain flow calculations of a distribution network containing DG based on blind number theory | |
| CN117183803A (en) | Electric automobile charging control method and device and computer equipment | |
| Kim et al. | Lstm-based multi-step soc forecasting of battery energy storage in grid ancillary services | |
| CN117289167A (en) | Battery remaining life prediction method, device and medium based on multiple neural network | |
| CN109586309B (en) | Power distribution network reactive power optimization method based on big data free entropy theory and scene matching | |
| CN115693787B (en) | Method for analyzing new energy acceptance of optical storage and distribution power grid in consideration of source load randomness | |
| Wei et al. | An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios | |
| CN111371091A (en) | Calculation method of photovoltaic power generation intelligent prediction system based on market benefit driving | |
| CN115275975B (en) | Method and device for determining matching degree of electric power data of optical storage charging station | |
| CN112581311B (en) | Method and system for predicting long-term output fluctuation characteristics of aggregated multiple wind power plants | |
| CN115173421A (en) | Probability optimal power flow calculation method based on progressive Latin hypercube sampling | |
| Pathan et al. | Data forecasting and storage sizing for PV battery system using fuzzy Markov chain model | |
| CN112818592A (en) | Photovoltaic cell parameter and power generation capacity prediction method and system based on data mining | |
| Pindaro | Controlling lithium-ion batteries through reinforcement learning | |
| Chen et al. | Short-Term Photovoltaic Power Prediction Algorithm Based on Improved BP Neural Network |
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 |