CN115912331B - Power adjusting method of intelligent wind-solar complementary power generation system - Google Patents
Power adjusting method of intelligent wind-solar complementary power generation system Download PDFInfo
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Abstract
The invention relates to the technical field of wind and light prediction and power regulation, and discloses a power regulation method of an intelligent wind and light complementary power generation system, which comprises the following steps: acquiring historical power generation data, and establishing an RBF neural network prediction model aiming at short-term wind and light prediction; establishing a distributed energy model and a waste battery charging and discharging model; optimizing the RBF neural network prediction model by using a wild horse search algorithm; short-term wind and light prediction is carried out according to the optimized model, and a wind and light prediction result is obtained; and carrying out intelligent regulation and control on the output power of the distributed energy system according to the wind-light prediction result. Compared with the prior art, the method has the advantages that the RBF neural network prediction model is optimized by using the wild Ma Sousuo algorithm, so that the model error is smaller, the wind and solar clean energy and the waste battery are fully utilized, and the economic cost and the environmental cost are greatly saved.
Description
Technical Field
The invention relates to the technical field of wind and light prediction and power regulation, in particular to a power regulation method of an intelligent wind and light complementary power generation system.
Background
Under the dual pressures of energy shortage and environmental pollution, the renewable energy-based power generation technology is rapidly developed, and photovoltaic and wind power are widely applied with the advantages of safety, cleanness, abundance and the like. As representative of clean energy sources, they have advantages not comparable to other energy sources, but also have outstanding limitations such as low energy density, instability, regional variability. Because of the difference of regions and terrains, the wind speed, the wind energy capacity, the illumination time and the illumination intensity are different. The power generation and supply in two energy forms have instability and intermittence, and direct grid connection can influence the quality of electric energy and threaten safe and stable operation.
The traditional short-term wind and light prediction mostly adopts meteorological information conditions (such as air pressure and the like) and physical quantities (such as unit states and the like) of surrounding and self environments as input parameters, and combines the historical generated energy to train and optimize a neural network, so that the aim of accurate prediction is achieved. However, conventional neural networks are prone to localized optima leading to prediction failure. Moreover, the number of hidden layers and hidden layer nodes of the neural network used for traditional prediction is not easy to determine, and the training period is too long and the accuracy is low.
The traditional battery capacity is lower than 80%, namely the battery is regarded as scrapped, but the battery still has a larger utilization space, the traditional battery recycling industry disassembles and recycles the battery, heavy metals such as mercury and the like can be generated in the process, and the concept of green development is not met.
Disclosure of Invention
The invention aims to: aiming at the problems in the prior art, the invention provides the power regulating method of the intelligent wind-solar complementary power generation system, and the RBF neural network prediction model is optimized by using the wild Ma Sousuo algorithm, so that the model error is smaller, the wind-solar clean energy and the waste battery are fully utilized, and the economic cost and the environmental cost are greatly saved.
The technical scheme is as follows: the invention provides a power regulation method of an intelligent wind-solar complementary power generation system, which comprises the following steps:
step 1: acquiring historical power generation data and influence parameters thereof, and establishing an RBF neural network prediction model aiming at short-term wind and light prediction;
step 2: establishing a distributed energy model and a waste battery charging and discharging model;
step 3: optimizing the RBF neural network prediction model by using a wild horse search algorithm;
step 4: carrying out short-term wind and light prediction according to the optimized model in the step 3 to obtain a wind and light prediction result;
step 5: and (3) intelligently regulating and controlling the output power of the distributed energy system according to the wind-solar prediction result in the step (4).
Further, the modeling of the RBF neural network prediction model in the step 1 is divided into the following steps:
step 1.1: determining 8 nodes of an input layer, x 1 、x 2 、x 3 、x 4 、x 5 、x 6 、x 7 、x 8 Respectively corresponding to solar radiation quantity, inclination angle, component shielding, wind vector, wind density, air pressure, humidity and unit state; determining 1 node of an output layer, wherein f (x) is the predicted generated energy; the RBF neural network prediction model formula is as follows:
wherein q is the number of hidden layer neurons, and w j Is the weight corresponding to the j-th hidden layer neuron, c j Is the center corresponding to the j-th hidden layer neuron,c is a radial basis function i Is the center point of the ith neuron, σ is the width of the gaussian kernel, ||x i -c i I is the sample point x i To the central point c i European distance,/, of->And (3) a radial basis function of the corresponding position of the center corresponding to the j-th hidden layer neuron of all input parameters.
Step 1.2: according to the step 1.1, establishing a wind-light prediction model mean square error, wherein the formula is as follows:
when E is minimum, the prediction model result is optimal; wherein e i For the error when the ith sample is input, f (x) is the predicted power generation amount, and y is the actual power generation amount;
step 1.3: the neuron linear weight formula of the output layer is established as follows:
wherein eta is learning efficiency, E is mean square error,as a radial basis function, w is the total weight of the hidden layer;
step 1.4: the hidden layer neuron central point formula is established as follows:
step 1.5: the gaussian kernel width formula for creating hidden layers is as follows:
further, the modeling of the distributed energy source model and the waste battery charge and discharge model in the step 2 is divided into the following steps:
step 2.1: a photovoltaic cell model is established, and the formula is as follows:
wherein ,ISC Is short-circuit current, V oc Is the open circuit voltage, I m Is the output current at the maximum power point, V m Is the output voltage at the maximum power point;
step 2.2: a fan battery model is established, and the formula is as follows:
wherein ,Pm Is the mechanical output of the turbine, ρ is the air density, R is the blade radius, C p Is the coefficient of performance of the turbine, the beta blade pitch angle, V is the wind speed, the ratio of the tip speed of the lambda rotor blade to the wind speed;
step 2.3: establishing a waste battery model, wherein the formula is as follows:
V T ＝E m -I batt R int
V out ＝N s V T
wherein ,VT Is a single module voltage, E m Is the open circuit voltage, I batt Cell current, R int Internal resistance of battery, I in Battery pack current, N p Number of parallel battery cells, V out Battery pack voltage, ns number of battery cells in series, SOC is battery state of charge, cap batt Battery capacity.
Further, the specific steps of optimizing the RBF neural network model by using the wild Ma Sousuo algorithm in the step 3 are as follows:
step 3.1: initializing parameters, and inputting an amount affecting the accuracy of a prediction model: weight w, center of basis function c, gaussian kernel width sigma;
step 3.2: initializing a population: setting the population number as N, the grouping number as G, and the maximum iteration number as Maxiter;
step 3.3: creating a group, randomly selecting a leader, finding all members to adapt, and adopting the following iterative formula:
wherein iter is the current iteration number, maxiter is the maximum iteration number of the algorithm, and TDR gradually decreases from 1 to 0 along with the increase of the calculated iteration number;
step 3.4: the adaptive mechanism Z formula for the horse group is as follows:
wherein P is a vector consisting of 0 and 1,is [0,1 ]]Inner random number, IDX is a vector +=0 satisfying the condition (p+=0)>Index value of (2);
step 3.5: if the random number is smaller than the cross percentage, continuing to operate; otherwise, turning to step 3.3; the average crossover operator formula is as follows:
Crossover＝Mean
wherein ,the table shows the individual positions of individuals p in group k, which are re-entered into group k after outlier>The table shows the individual position of the individual q in group i, which is re-entered into group i after outlier +.>The table indicates the individual locations of group j that are re-entered after individual z in group j is outlier;
the leader is the center of the pasture area, the group members search around the center (pasture), and the search formula is as follows:
wherein ,is the current location of a member within the group, starlion j Is the leader's position, Z is the adaptive structure, R is [ -2,2]Uniform random number of range, +.>New positions of group members at grazing;
step 3.7: the leader mainly brings the members to a more suitable habitat, which is used if the current group is dominant; if another team is dominant, they must leave the place, the location update formula is as follows:
wherein ,is the next position of the leader of group i, WH is the position of the puddle (habitat),is the current location of the group i leader;
step 3.8: at the later stage of the algorithm, selecting a leader according to the fitness; if the fitness of one member is better than that of the group leader, the positions of the group leader and the corresponding group leader are changed, and the formula is as follows:
step 3.9: if the termination criterion is met, the process goes to step 3.10; otherwise, turning to step 3.3;
step 3.10: and outputting the obtained optimal solution.
Further, in the step 5, wind-light prediction data in the step 4 are used for obtaining a generating capacity trend and power prediction in a short period of wind and light, battery parameters of the waste battery pack and wind-light prediction data are monitored in real time, and when the power generation trend is predicted to be increased in the short period, the distributed energy system starts to charge the waste battery pack to consume power of the power grid; when the power generation trend is predicted to be increased and reduced in a short period, the waste battery pack starts to discharge, and the power of the power grid is balanced.
The beneficial effects are that:
1. according to the invention, by using the RBF neural network prediction model, the wind-solar power generation in a short period in the future can be predicted by locally generating historical power data in a chemical plant, so that accurate and reasonable data support is provided for the regulation and control of distributed energy power; compared with the traditional neural network model, the RBF neural network model has the advantages of better generalization capability, stronger optimizing capability and no sinking into local optimization. The model is optimized by using a wild Ma Sousuo algorithm, so that the model error is smaller; the accurate wind-solar prediction data enables the control system to adjust the power of the distributed power supply more accurately, improves the use efficiency of wind-solar, and reduces the economic cost.
2. In addition, the invention uses the waste battery pack to assist the distributed energy system in power adjustment, and the waste battery pack charges Gu Shi and discharges when the distributed energy system peaks, so that redundant energy is well consumed. Has better economic benefit and accords with the design concept of green and environment-friendly.
Drawings
FIG. 1 is a block diagram of the structure of the present invention;
FIG. 2 is a flow chart of RBF neural network prediction according to the present invention;
FIG. 3 is a topology of an RBF neural network of the present invention;
FIG. 4 is a flowchart of the wild Ma Sousuo algorithm of the present invention;
FIG. 5 is a flowchart of a wild Ma Sousuo algorithm optimized RBF neural network model of the present invention
FIG. 6 is a flow chart of a wind and solar control system according to the present invention
FIG. 7 is a view showing the RBF neural network prediction error map of the present invention
FIG. 8 is a graph of the prediction error of the RBF neural network optimized by the wild Ma Sousuo algorithm of the present invention
FIG. 9 is a comparative error plot of RBF neural network predictions optimized by the RBF neural network prediction and the wild horse search algorithm of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
The invention discloses a power regulation method of an intelligent wind-solar complementary power generation system, which is arranged on power regulation equipment shown in figure 1, wherein the power regulation equipment comprises an RBF neural network prediction model, a control system, a distributed energy system, a waste battery pack and the like; the control system may be a central processing unit or a server terminal.
The power regulation method of the intelligent wind-solar complementary power generation system comprises the following steps:
step 1: historical power generation data are acquired, an RBF neural network prediction model aiming at short-term wind and light prediction is established, a specific flow is shown in fig. 2, and an RBF neural network topological structure is shown in fig. 3.
Step 1.1: determining 8 nodes of an input layer, x 1 、x 2 、x 3 、x 4 、x 5 、x 6 、x 7 、x 8 Respectively corresponding to the solar radiation quantity, the inclination angle, the shielding of the assembly, the wind speed, the wind direction, the wind density, the air pressure and the humidity; determining 1 node of an output layer, wherein f (x) is the predicted generated energy; RBF neural network prediction model formula is as followsThe predictive model formula is as follows:
wherein q is the number of hidden layer neurons, and w j Is the weight corresponding to the j-th hidden layer neuron, c j Is the center corresponding to the j-th hidden layer neuron,c is a radial basis function i Is the center point of the ith neuron, σ is the width of the gaussian kernel. ||x i -c i I is the sample point x i To the central point c i European distance,/, of->And (3) a radial basis function of the corresponding position of the center corresponding to the j-th hidden layer neuron of all input parameters.
Step 1.2: according to the step 1.1, establishing a wind-light prediction model mean square error, wherein the formula is as follows:
and when E is minimum, the prediction model result is optimal. Wherein e i To input an error at the time of the i-th sample, f (x) is a predicted power generation amount, and y is an actual power generation amount.
Step 1.3: the neuron linear weight formula of the output layer is established as follows:
wherein eta is learning efficiency, E is mean square error,and w is the total weight of the hidden layer as a radial basis function.
Step 1.4: the hidden layer neuron central point formula is established as follows:
step 1.5: the gaussian kernel width formula for creating hidden layers is as follows:
step 2: and establishing a distributed energy model and a waste battery charging and discharging model.
Step 2.1: a photovoltaic cell model is established, and the formula is as follows:
wherein ,ISC Is short-circuit current, V oc Is the open circuit voltage, I m Is the output current at the maximum power point, V m Is the output voltage at the maximum power point;
step 2.2: a fan battery model is established, and the formula is as follows:
wherein ,Pm Is the mechanical output of the turbine, ρ is the air density, R is the blade radius, C p Is the coefficient of performance of the turbine, the beta blade pitch angle, V is the wind speed, the ratio of the tip speed of the lambda rotor blade to the wind speed;
step 2.3: establishing a waste battery model, wherein the formula is as follows:
V T ＝E m -I batt R int
V out ＝N s V T
wherein ,VT Is a single module voltage, E m Is the open circuit voltage, I batt Cell current, R int Internal resistance of battery, I in Battery pack current, N p Number of parallel battery cells, V out Battery pack voltage, ns number of battery cells in series, SOC is battery state of charge, cap batt Battery capacity.
Step 3: the RBF neural network prediction model is optimized by using a wild horse search algorithm, and the flow is shown in fig. 4.
Step 3.1: initializing parameters, and inputting an amount affecting the accuracy of a prediction model: weight w, center of basis function c, gaussian kernel width sigma;
step 3.2: initializing a population: setting the population number as N, the grouping number as G, and the maximum iteration number as Maxiter;
step 3.3: creating a group, randomly selecting a leader, finding all members to adapt, and adopting the following iterative formula:
wherein iter is the current iteration number, maxiter is the maximum iteration number of the algorithm, and TDR gradually decreases from 1 to 0 along with the increase of the calculated iteration number;
step 3.4: the adaptive mechanism Z formula for the horse group is as follows:
wherein P is a vector consisting of 0 and 1,is [0,1 ]]Inner random number, IDX is a vector +=0 satisfying the condition (p+=0)>Index value of (2);
step 3.5: if the random number is smaller than the cross percentage, continuing to operate; otherwise, turning to step 3.3; the average crossover operator formula is as follows:
Crossover＝Mean
wherein ,the table shows the individual positions of individuals p in group k, which are re-entered into group k after outlier>The table shows the individual position of the individual q in group i, which is re-entered into group i after outlier +.>The table indicates the individual locations of group j that are re-entered after individual z in group j is outlier;
the leader is the center of the pasture area, the group members search around the center (pasture), and the search formula is as follows:
wherein ,is the current location of a member within the group, starlion j Is the leader's position, Z is the adaptive structure, R is [ -2,2]Uniform random number of range, +.>New positions of group members at grazing;
step 3.7: the leader mainly brings the members to a more suitable habitat, which is used if the current group is dominant; if another team is dominant, they must leave the place, the location update formula is as follows:
wherein ,is the next position of the leader of group i, WH is the position of the puddle (habitat),current bit being the leader of group iPlacing;
step 3.8: at the later stage of the algorithm, selecting a leader according to the fitness; if the fitness of one member is better than that of the group leader, the group leader and the position of the corresponding member are changed according to the following formula: :
step 3.9: if the termination criterion is met, the process goes to step 3.10; otherwise, turning to step 3.3;
step 3.10: and outputting the obtained optimal solution.
Step 4: and 3, carrying out short-term wind and light prediction according to the optimized model in the step 3, and obtaining a wind and light prediction result.
Step 5: and (3) intelligently regulating and controlling the output power of the distributed energy system according to the wind-solar prediction result in the step (4), wherein the regulating and controlling flow is shown in figure 6.
Obtaining the generating capacity trend and the power prediction in the short period of wind and light through the wind and light prediction data in the step 4, monitoring the battery parameters of the waste battery pack and wind and light prediction data in real time, and when the power generation trend in the short period is predicted to be increased, starting the distributed energy system to charge the waste battery pack to consume the power of the power grid; when the power generation trend is predicted to be increased and reduced in a short period, the waste battery pack starts to discharge, and the power of the power grid is balanced.
As shown in fig. 7 and 8, the RBF neural network model not using the wild horse search algorithm has higher accuracy on prediction, and after the RBF neural network model is optimized by using the wild Ma Sousuo algorithm, the fitting degree of the model predicted value to the true value is improved, so that the model is more accurate. As shown in fig. 9, after the wild horse algorithm optimizes the RBF model, the error value in a short period is lower than 0.02, and the matching degree to the predicted value is high.
The foregoing embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the present invention and to implement the same, not to limit the scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be included in the scope of the present invention.
Claims (4)
1. The power regulation method of the intelligent wind-solar complementary power generation system is characterized by comprising the following steps of:
step 1: acquiring historical power generation data and influence parameters thereof, and establishing an RBF neural network prediction model aiming at short-term wind and light prediction, wherein the RBF neural network prediction model determines 8 nodes and x of an input layer 1 、x 2 、x 3 、x 4 、x 5 、x 6 、x 7 、x 8 Respectively corresponding to solar radiation quantity, inclination angle, component shielding, wind vector, wind density, air pressure, humidity and unit state; determining 1 node of an output layer as predicted generated energy;
step 2: establishing a distributed energy model and a waste battery charging and discharging model, wherein the waste battery charging and discharging model is as follows:
V T ＝E m -I batt R int
V out ＝N s V T
wherein ,VT Is a single module voltage, E m Is the open circuit voltage, I batt Cell current, R int Internal resistance of battery, I in Battery pack current, N p Number of parallel battery cells, V out Battery pack voltage, N s The number of battery nodes in series, the SOC is the state of charge of the battery, and Cap batt A battery capacity;
step 3: optimizing the RBF neural network prediction model by using a wild horse search algorithm, wherein the weight w of the RBF neural network prediction model, the center c of the basis function and the Gaussian kernel width sigma are the quantities influencing the accuracy of the RBF neural network prediction model;
step 4: carrying out short-term wind and light prediction according to the optimized model in the step 3 to obtain a wind and light prediction result;
step 5: according to the wind-solar prediction result in the step 4, the generating capacity trend and the power prediction in a short period of wind-solar are obtained, the output power of the distributed energy system is intelligently regulated and controlled, the parameters of the waste battery pack battery and wind-solar prediction data are monitored in real time, and when the power generation trend in a short period is predicted to be increased, the distributed energy system starts to charge the waste battery pack to consume the power of the power grid; when the power generation trend is predicted to be increased and reduced in a short period, the waste battery pack starts to discharge, and the power of the power grid is balanced.
2. The power adjustment method of the intelligent wind-solar complementary power generation system according to claim 1, wherein the modeling of the RBF neural network prediction model in the step 1 is divided into the following steps:
step 1.1: determining 8 nodes of an input layer, x 1 、x 2 、x 3 、x 4 、x 5 、x 6 、x 7 、x 8 Respectively corresponding to solar radiation quantity, inclination angle, component shielding, wind vector, wind density, air pressure, humidity and unit state; determining 1 node of an output layer, wherein f (x) is the predicted generated energy; the RBF neural network prediction model formula is as follows:
wherein q is the number of hidden layer neurons, and w j Is the weight corresponding to the j-th hidden layer neuron, c j Is the center corresponding to the j-th hidden layer neuron,c is a radial basis function i Is the center point of the ith neuron, σ is the width of the gaussian kernel, ||x i -c i I is the sample point x i To the central point c i European distance,/, of->Radial basis functions at the corresponding positions of the centers corresponding to the j-th hidden layer neurons of all input parameters;
step 1.2: according to the step 1.1, establishing a wind-light prediction model mean square error, wherein the formula is as follows:
when E is minimum, the prediction model result is optimal; wherein e i For the error when the ith sample is input, f (x) is the predicted power generation amount, and y is the actual power generation amount;
step 1.3: the neuron linear weight formula of the output layer is established as follows:
wherein eta is learning efficiency, E is mean square error,as a radial basis function, w is the total weight of the hidden layer;
step 1.4: the hidden layer neuron central point formula is established as follows:
step 1.5: the gaussian kernel width formula for creating hidden layers is as follows:
3. the power adjustment method of the intelligent wind-solar complementary power generation system according to claim 1, wherein the modeling of the distributed energy source model and the waste battery charge-discharge model in the step 2 is divided into the following steps:
step 2.1: a photovoltaic cell model is established, and the formula is as follows:
wherein ,ISC Is short-circuit current, V oc Is the open circuit voltage, I m Is the output current at the maximum power point, V m Is the output voltage at the maximum power point;
step 2.2: a fan battery model is established, and the formula is as follows:
wherein ,Pm Is the mechanical output of the turbine, ρ is the air density, R is the blade radius, C p Is the coefficient of performance of the turbine, the beta blade pitch angle, V is the wind speed, the ratio of the tip speed of the lambda rotor blade to the wind speed;
step 2.3: and (5) establishing a waste battery model.
4. The power adjustment method of the intelligent wind-solar complementary power generation system according to claim 2, wherein the specific steps of optimizing the RBF neural network model by using the wild Ma Sousuo algorithm in the step 3 are as follows:
step 3.1: initializing parameters, and inputting an amount affecting the accuracy of a prediction model: weight w, center of basis function c, gaussian kernel width sigma;
step 3.2: initializing a population: setting the population number as N, the grouping number as G, and the maximum iteration number as Maxiter;
step 3.3: creating a group, randomly selecting a leader, finding all members to adapt, and adopting the following iterative formula:
wherein iter is the current iteration number, maxiter is the maximum iteration number of the algorithm, and TDR gradually decreases from 1 to 0 along with the increase of the calculated iteration number;
step 3.4: the adaptive mechanism Z formula for the horse group is as follows:
wherein P is a vector consisting of 0 and 1,is [0,1 ]]Inner random number, IDX is a vector satisfying the condition (p= 0)Index value of (2);
step 3.5: if the random number is smaller than the cross percentage, continuing to operate; otherwise, turning to step 3.3; the average crossover operator formula is as follows:
Crossover＝Mean
wherein ,indicating the individual position in group k where individual p is isolated and re-entered in group k,/->Representing the individual position of group i, re-entered after individual q is outlier, in group i,/>Representing individual locations in group j where individual z is outlier before reentering group j;
the leader is the center of the pasture area, the group members search around the center by grazing, and the search formula is as follows:
wherein ,is the current location of a member within the group, starlion j Is the leader's position, Z is the adaptive structure, R is [ -2,2]Range ofUniform random number>New positions of group members at grazing;
step 3.7: the leader mainly brings the members to a more suitable habitat, which is used if the current group is dominant; if another team is dominant, they must leave the place, the location update formula is as follows:
wherein ,is the next position of the leader of group i, WH is the position of the habitat of the puddle,/->Is the current location of the group i leader;
step 3.8: at the later stage of the algorithm, selecting a leader according to the fitness; if the fitness of one member is better than that of the group leader, the positions of the group leader and the corresponding group leader are changed, and the formula is as follows:
step 3.9: if the termination criterion is met, the process goes to step 3.10; otherwise, turning to step 3.3;
step 3.10: and outputting the obtained optimal solution.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013074695A (en) * | 2011-09-27 | 2013-04-22 | Meiji Univ | Device, method and program for predicting photovoltaic generation |
| CN106227915A (en) * | 2016-07-07 | 2016-12-14 | 南京工程学院 | Disc type solar energy heat collector exit temperature prediction method based on GA RBF |
| CN108960491A (en) * | 2018-06-15 | 2018-12-07 | 常州瑞信电子科技有限公司 | Method for forecasting photovoltaic power generation quantity based on RBF neural |
| CN112537215A (en) * | 2020-12-19 | 2021-03-23 | 郑州新开元科技有限公司 | Semi-off-grid type wind-solar complementary intelligent charging station |
| CN113555899A (en) * | 2021-07-20 | 2021-10-26 | 南京工程学院 | Coordination control method for wind-solar energy storage power generation system |
| CN113673786A (en) * | 2021-09-06 | 2021-11-19 | 上海海事大学 | Effective wind speed estimation system and method of wind driven generator based on RBF and LSTM |
-
2022
- 2022-10-30 CN CN202211352772.2A patent/CN115912331B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013074695A (en) * | 2011-09-27 | 2013-04-22 | Meiji Univ | Device, method and program for predicting photovoltaic generation |
| CN106227915A (en) * | 2016-07-07 | 2016-12-14 | 南京工程学院 | Disc type solar energy heat collector exit temperature prediction method based on GA RBF |
| CN108960491A (en) * | 2018-06-15 | 2018-12-07 | 常州瑞信电子科技有限公司 | Method for forecasting photovoltaic power generation quantity based on RBF neural |
| CN112537215A (en) * | 2020-12-19 | 2021-03-23 | 郑州新开元科技有限公司 | Semi-off-grid type wind-solar complementary intelligent charging station |
| CN113555899A (en) * | 2021-07-20 | 2021-10-26 | 南京工程学院 | Coordination control method for wind-solar energy storage power generation system |
| CN113673786A (en) * | 2021-09-06 | 2021-11-19 | 上海海事大学 | Effective wind speed estimation system and method of wind driven generator based on RBF and LSTM |
Non-Patent Citations (3)
| Title |
|---|
| Accurate Parameters Estimation of Three Diode Model of Photovoltaic Modules Using Hunter–Prey and Wild Horse Optimizers;HUSAM A. RAMADAN et al.;《IEEE Access》;第87435 - 87453页 * |
| 基于云服务平台智能微网控制策略研究;胡治辉;《中国优秀硕士学位论文全文数据库 工程科技II辑》(第8期);第C042-412页 * |
| 小型风光互补发电系统控制和能量预测技术研究;姚传安;《中国博士学位论文全文数据库工程科技Ⅱ辑》(第03期);第C042-26页 * |
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