CN107886445A - A kind of power regulating method based on the analysis of neuron big data - Google Patents
A kind of power regulating method based on the analysis of neuron big data Download PDFInfo
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Abstract
The application is related to a kind of power regulating method based on big data analysis, comprises the following steps：Including electrical energy supply system, electrical energy supply system includes photovoltaic cell, wind-driven generator, energy-storage battery, comprised the following steps：（1）Get parms（2）Establish neuron models, and normalizing parameter data；（3）, to data cleansing, determine current power value according to the data after cleaning, the prediction electricity consumption data in next cycle determined by the self-learning capability of neural network model；（4）, according to the prediction electricity consumption data and current power value in next cycle, calculate power adjusting benchmark step-length, and according to the power adjusting benchmark step-length of calculatingAdjust power output.The photovoltaic cell that the application can control the size huge according to historical data coordinates control power output, improves power regulated efficiency, effectively extends photovoltaic cell service life.
Description
Technical field
The application is related to big data analysis technical field, more particularly to the power adjustment side based on the analysis of neuron big data
Method.
Background technology
In the prior art, big data analysis method generally comprises two classes：Built first, relying on expertise and carrying out manual analysis
Mould, second,, can be from the data of magnanimity by big data analysis method based on artificial intelligence approach, such as neural net method
The real-time dynamic of object is obtained, accurately to be controlled the current data of target according to mass data.
With developing rapidly for photovoltaic cell technology, extensive use is socially started using photovoltaic generation, especially
Present photovoltaic cell starts to be applied in family, and What is more, and being layed in roof by the photovoltaic cell of small-sized tile enters
Row generates electricity, however, the photovoltaic power output of enormous amount, with illumination, load, power demand is difficult to accomplish that power is steady
Adjustment.
The content of the invention
The application proposes the method that power adjustment can be stably carried out according to big data analysis and Control photovoltaic cell, and it can
Improve power adjustment efficiency, meanwhile, prevent extend photovoltaic cell service life.
To solve above-mentioned technical problem：The application proposes a kind of power regulating method based on big data analysis, including electricity
Energy supply system, electrical energy supply system include photovoltaic cell, wind-driven generator, energy-storage battery, comprised the following steps：
（1）, obtain k-th of photovoltaic cell DC voltage, the output of h-th wind-driven generator AC voltage
Wherein, k, h span are 1n to the voltage Ucell of Uac (h) and energy-storage battery；
（2）, neuron models are established with each photovoltaic cell, wind-driven generator, energy-storage battery respectively；With whole photovoltaic cell,
Whole wind-driven generator, whole energy-storage battery establish neural column model, setting photovoltaic cell, whole wind-driven generator, whole storage
The output voltage of energy battery is Udc, Uac, Uzcell, and whole electrical energy supply system and power network, load are coordinated and establish entirely god
Through network model, by step（1）In, Uac (h), Ucell be converted to standard data, by the reference format number
According to being written in the neural network model；
（3）, to data cleansing, determine current power value according to the data after cleaning, pass through the self study energy of neural network model
Power determines the prediction electricity consumption data in next cycle；
（4）, according to the prediction electricity consumption data and current power value in next cycle, calculate power adjusting benchmark step-length, and
According to the power adjusting benchmark step-length of calculatingAdjust power output.
The described power regulating method based on big data analysis, wherein, the step（4）In also include by reversely passing
Broadcast power adjusting benchmark step-length of the algorithm to above-mentioned calculatingIt is whether correct；
The step（4）Specifically include：
(a）, calculate that electrical energy supply system is actual issues command value initial value；
(b）, according to electrical energy supply system output voltage values and rate of change in power adjustment procedure, adjust power adjustment step；
(c）, obtain that electrical energy supply system is actual issues command value, and according to command value scope, judge Power Control
Whether terminate.
The described power regulating method based on big data analysis, wherein, in the step (4), power adjusting benchmark step
It is longCalculation formula it is as follows：
In formula,For electrical energy supply system set value of the power,The moment is issued for electrical energy supply system set value of the power
Actual power, N for regulation step number, can according to be actually needed setting,For regulation coefficient, according to whole electrical energy supply system
After middle photovoltaic cell, wind-driven generator, the history data of energy-storage battery and current operating data carry out big data analysis
Setting,For bearing power coefficient, the ratio of past 10 days same period bearing power and current power,For illumination system
Number, determined according to the intensity of illumination of detection,For wind factor, determined according to the current wind-force size of detection.
The described power regulating method based on big data analysis, wherein, the regulation coefficientCalculation formula such as
Under：
Wherein,For current input electrical energy supply system quantity,Thrown by the past 10 days same period
The quantity of the electrical energy supply system entered.
The described power regulating method based on big data analysis, wherein,
Step (a）In, electrical energy supply system is actual to issue command value initial valueCalculation formula is as follows:
In formula,For the real output of initial time electrical energy supply system,For power adjusting benchmark step-length.
The described power regulating method based on big data analysis, wherein,
The step（b）In, power adjustment stepCalculation formula it is as follows：
In formula,For DC voltage gain coefficient；Function is calculated for the out-of-limit degree of DC voltage, is returned
Return the value not less than 0；Walked for kth and calculate gained outlet side voltage change ratio；For outlet side voltage change ratio
Maximum.
Wherein, outlet side voltage out-of-limit degree calculates functionIt is expressed as follows：
In formula,ForkDC voltage sampled value when step calculates,For outlet side upper voltage limit value,For outlet side voltage lower limit value.
The application can carry out mass data analysis according to historical data, and it is steady to reach the current photovoltaic cell DC side of control
The purpose of power output.
Brief description of the drawings
Fig. 1 is the application electric power supply system overall structure diagram.
Fig. 2 is the application big data analysis method schematic diagram.
Embodiment
The application is described in further detail below in conjunction with the accompanying drawings, it is necessary to it is pointed out here that, implement in detail below
Mode is served only for that the application is further detailed, it is impossible to the limitation to the application protection domain is interpreted as, the field
Technical staff can make some nonessential modifications and adaptations to the application according to above-mentioned application content.
As shown in figure 1, be the application electric power supply system overall structure diagram, including multiple photovoltaic cells, wind-force hair
Motor, battery, the photovoltaic cell, wind-driven generator, battery output connection AD/DC, DC/DC module, big data analyzer
Power and the data of whole photovoltaic system direct current output for being exported to each photovoltaic DC are acquired, line number of going forward side by side
According to analysis, analytical structure is sent to control device, it is described to control each photovoltaic cell and AC/DC, DC/DC in control device
Module.
As shown in Fig. 2 it is the application big data analysis method schematic diagram.
A kind of power regulating method based on big data analysis, including electrical energy supply system, electrical energy supply system include light
Battery, wind-driven generator, energy-storage battery are lied prostrate, is comprised the following steps：
（1）, obtain k-th of photovoltaic cell DC voltage, the output of h-th wind-driven generator AC voltage
Wherein, k, h span are 1n to the voltage Ucell of Uac (h) and energy-storage battery；
（2）, neuron models are established with each photovoltaic cell, wind-driven generator, energy-storage battery respectively；With whole photovoltaic cell,
Whole wind-driven generator, whole energy-storage battery establish neural column model, setting photovoltaic cell, whole wind-driven generator, whole storage
The output voltage of energy battery is Udc, Uac, Uzcell, and whole electrical energy supply system and power network, load are coordinated and establish entirely god
Through network model, by step（1）In, Uac (h), Ucell be converted to standard data, by the reference format number
According to being written in the neural network model；
（3）, to data cleansing, determine current power value according to the data after cleaning, pass through the self study energy of neural network model
Power determines the prediction electricity consumption data in next cycle；
（4）, according to the prediction electricity consumption data and current power value in next cycle, calculate power adjusting benchmark step-length, and
According to the power adjusting benchmark step-length of calculatingAdjust power output.
The described power regulating method based on big data analysis, wherein, the step（4）In also include by reversely passing
Broadcast power adjusting benchmark step-length of the algorithm to above-mentioned calculatingIt is whether correct；
The step（4）Specifically include：
(a）, calculate that electrical energy supply system is actual issues command value initial value；
(b）, according to electrical energy supply system output voltage values and rate of change in power adjustment procedure, adjust power adjustment step；
(c）, obtain that electrical energy supply system is actual issues command value, and according to command value scope, judge Power Control
Whether terminate.
The described power regulating method based on big data analysis, wherein, in the step (4), power adjusting benchmark step
It is longCalculation formula it is as follows：
In formula,For electrical energy supply system set value of the power,The moment is issued for electrical energy supply system set value of the power
Actual power, N for regulation step number, can according to be actually needed setting,For regulation coefficient, according to whole electrical energy supply system
After middle photovoltaic cell, wind-driven generator, the history data of energy-storage battery and current operating data carry out big data analysis
Setting,For bearing power coefficient, the ratio of past 10 days same period bearing power and current power,For illumination system
Number, determined according to the intensity of illumination of detection,For wind factor, determined according to the current wind-force size of detection.
The described power regulating method based on big data analysis, wherein, the regulation coefficientCalculation formula such as
Under：
Wherein,For current input electrical energy supply system quantity,Thrown by the past 10 days same period
The quantity of the electrical energy supply system entered.
The described power regulating method based on big data analysis, wherein,
Step (a）In, electrical energy supply system is actual to issue command value initial valueCalculation formula is as follows:
In formula,For the real output of initial time electrical energy supply system,For power adjusting benchmark step-length.
The described power regulating method based on big data analysis, wherein,
The step（b）In, power adjustment stepCalculation formula it is as follows：
In formula,For DC voltage gain coefficient；Function is calculated for the out-of-limit degree of DC voltage, is returned
Return the value not less than 0；Walked for kth and calculate gained outlet side voltage change ratio；For outlet side voltage change ratio
Maximum.
Wherein, outlet side voltage out-of-limit degree calculates functionIt is expressed as follows：
In formula,ForkDC voltage sampled value when step calculates,For outlet side upper voltage limit value,For outlet side voltage lower limit value.
The described power regulating method based on big data analysis, wherein,
, it is necessary to adjust step-length according to gained is calculated in step (c)The scope of value is modified to it,
WhenWhen, showWithValue it is positive and negative different, reason is direct current
Side voltage out-of-limit, or voltage change ratio are out-of-limit, and now power is unfit to do further adjustment, to avoid photovoltaic power from exceeding
Setting value, then make the revised adjusting step be, Power Control adjustment process terminate.
The described power regulating method based on big data analysis, wherein,
WhenWhen, show to calculate gained regulation step-lengthValue is more than set value of the power and reality
Difference between the power of border makes the revised adjusting step be, it is necessary to be modified to regulation step-length, photovoltaic system is actual issues command value for kth step, Power Control adjustment process
Terminate.
The application can carry out mass data analysis according to historical data, and it is steady to reach the current photovoltaic cell DC side of control
The purpose of power output.
Claims (7)
1. a kind of power regulating method based on big data analysis, it is characterised in that including electrical energy supply system, electric energy supply system
System includes photovoltaic cell, wind-driven generator, energy-storage battery, comprises the following steps：
（1）, obtain k-th of photovoltaic cell DC voltage, the output of h-th wind-driven generator AC voltage
Wherein, k, h span are 1n to the voltage Ucell of Uac (h) and energy-storage battery；
（2）, neuron models are established with each photovoltaic cell, wind-driven generator, energy-storage battery respectively；With whole photovoltaic cell,
Whole wind-driven generator, whole energy-storage battery establish neural column model, setting photovoltaic cell, whole wind-driven generator, whole storage
The output voltage of energy battery is Udc, Uac, Uzcell, and whole electrical energy supply system and power network, load are coordinated and establish entirely god
Through network model, by step（1）In, Uac (h), Ucell be converted to standard data, by the reference format number
According to being written in the neural network model；
（3）, to data cleansing, determine current power value according to the data after cleaning, pass through the self study energy of neural network model
Power determines the prediction electricity consumption data in next cycle；
（4）, according to the prediction electricity consumption data and current power value in next cycle, calculate power adjusting benchmark step-length, and root
According to the power adjusting benchmark step-length of calculatingAdjust power output.
2. the power regulating method as claimed in claim 1 based on big data analysis, it is characterised in that the step（4）In also
Including the power adjusting benchmark step-length by back-propagation algorithm to above-mentioned calculatingIt is whether correct；
The step（4）Specifically include：
(a）, calculate that electrical energy supply system is actual issues command value initial value；
(b）, according to electrical energy supply system output voltage values and rate of change in power adjustment procedure, adjust power adjustment step；
(c）, obtain that electrical energy supply system is actual issues command value, and according to command value scope, judge that Power Control is
No end.
3. the power regulating method as claimed in claim 1 based on big data analysis, it is characterised in that
In the step (4), power adjusting benchmark step-lengthCalculation formula it is as follows：
In formula,For electrical energy supply system set value of the power,The moment is issued for electrical energy supply system set value of the power
Actual power, N are regulation step number, can be set according to being actually needed,For regulation coefficient, according in whole electrical energy supply system
Photovoltaic cell, wind-driven generator, the history data of energy-storage battery and current operating data are set after carrying out big data analysis
It is fixed,For bearing power coefficient, the ratio of past 10 days same period bearing power and current power,For illumination tensor,
Determined according to the intensity of illumination of detection,For wind factor, determined according to the current wind-force size of detection.
4. the power regulating method as claimed in claim 3 based on big data analysis, it is characterised in that the regulation coefficient
Calculation formula it is as follows：
Wherein,For current input electrical energy supply system quantity,Thrown by the past 10 days same period
The quantity of the electrical energy supply system entered.
5. the power regulating method as claimed in claim 2 based on big data analysis, it is characterised in that
Step (a）In, electrical energy supply system is actual to issue command value initial valueCalculation formula is as follows:
In formula,For the real output of initial time electrical energy supply system,For power adjusting benchmark step-length.
6. the power regulating method as claimed in claim 5 based on big data analysis, it is characterised in that
The step（b）In, power adjustment stepCalculation formula it is as follows：
In formula,For DC voltage gain coefficient；Function is calculated for the out-of-limit degree of DC voltage, is returned
Value not less than 0；Walked for kth and calculate gained outlet side voltage change ratio；For outlet side voltage change ratio most
Big value.
7. wherein, outlet side voltage out-of-limit degree calculates functionIt is expressed as follows：
In formula,ForkDC voltage sampled value when step calculates,For outlet side upper voltage limit value,For outlet side voltage lower limit value.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049798A (en) * | 2012-12-05 | 2013-04-17 | 浙江大学城市学院 | Short-period electric generation power forecasting method applied to photovoltaic electric generation system |
| CN103236718A (en) * | 2013-03-26 | 2013-08-07 | 东北大学 | Source-network-load automatic control system and method for intelligent microgrid |
| CN103326388A (en) * | 2013-07-04 | 2013-09-25 | 中国能源建设集团广东省电力设计研究院 | Power prediction based micro-grid energy storage system and capacity configuration method |
| CN103500365A (en) * | 2013-09-18 | 2014-01-08 | 广州供电局有限公司 | Photovoltaic power generation power prediction method and system |
| CN104281986A (en) * | 2014-11-03 | 2015-01-14 | 四川慧盈科技有限责任公司 | Micro-grid power prediction method |
| CN105680474A (en) * | 2016-02-22 | 2016-06-15 | 中国电力科学研究院 | Control method for restraining rapid power change of photovoltaic station based on energy storage system |
| CN106059448A (en) * | 2016-06-24 | 2016-10-26 | 华北电力大学 | Power coordination control method for wind and photovoltaic complementary power generation system based on supervision prediction control |
| US20160329714A1 (en) * | 2015-05-08 | 2016-11-10 | The Board Of Trustees Of The University Of Alabama | Systems and methods for providing vector control of a grid connected converter with a resonant circuit grid filter |
| CN106803135A (en) * | 2017-01-17 | 2017-06-06 | 广东工业大学 | The Forecasting Methodology and device of a kind of photovoltaic power generation system output power |
-
2017
- 2017-11-09 CN CN201711097825.XA patent/CN107886445B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103049798A (en) * | 2012-12-05 | 2013-04-17 | 浙江大学城市学院 | Short-period electric generation power forecasting method applied to photovoltaic electric generation system |
| CN103236718A (en) * | 2013-03-26 | 2013-08-07 | 东北大学 | Source-network-load automatic control system and method for intelligent microgrid |
| CN103326388A (en) * | 2013-07-04 | 2013-09-25 | 中国能源建设集团广东省电力设计研究院 | Power prediction based micro-grid energy storage system and capacity configuration method |
| CN103500365A (en) * | 2013-09-18 | 2014-01-08 | 广州供电局有限公司 | Photovoltaic power generation power prediction method and system |
| CN104281986A (en) * | 2014-11-03 | 2015-01-14 | 四川慧盈科技有限责任公司 | Micro-grid power prediction method |
| US20160329714A1 (en) * | 2015-05-08 | 2016-11-10 | The Board Of Trustees Of The University Of Alabama | Systems and methods for providing vector control of a grid connected converter with a resonant circuit grid filter |
| CN105680474A (en) * | 2016-02-22 | 2016-06-15 | 中国电力科学研究院 | Control method for restraining rapid power change of photovoltaic station based on energy storage system |
| CN106059448A (en) * | 2016-06-24 | 2016-10-26 | 华北电力大学 | Power coordination control method for wind and photovoltaic complementary power generation system based on supervision prediction control |
| CN106803135A (en) * | 2017-01-17 | 2017-06-06 | 广东工业大学 | The Forecasting Methodology and device of a kind of photovoltaic power generation system output power |
Non-Patent Citations (3)
| Title |
|---|
| S. AISSOU ET AL.: "Modeling and control of hybrid photovoltaic wind power system with battery storage", 《ENERGY CONVERSION AND MANAGEMENT》 * |
| 单茂华 等: "风光储联合并网发电站中电池储能系统控制策略研究", 《智能信息技术应用学会会议论文集》 * |
| 孔波利，等: "基于风光混合模型的短期功率预测方法研究", 《电力系统保护与控制》 * |
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