CN103603776A - Wind turbine generator set blade-pitch variation safety prediction algorithm - Google Patents
Wind turbine generator set blade-pitch variation safety prediction algorithm Download PDFInfo
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Abstract
In a wind turbine generator set blade-pitch variation safety prediction algorithm, the icing possibility of blades is predicted through a BP (back-propagation) three-layered neural network. The BP three-layered neural network comprises an input layer, a hidden layer and an output layer. The input layer comprises eight input nodes including the wind speed x1, the air temperature x2, the air humidity x3, the blade-pitch variation angle x4, the impeller rotation speed x5, the blade spatial location x6, the fan vibration value x7 and the blade material coefficient x8; the hidden layer comprises three nodes of z1, z2 and z3; the output layer comprises one output node, namely the icing speed v. By the wind turbine generator set blade-pitch variation safety prediction algorithm, the service life of blades of the wind turbine generator set is prolonged and the power generation efficiency of the wind turbine generator set is improved.
Description
Technical field
The invention belongs to wind power technology field, be specifically related to a kind of prediction algorithm of wind generating set pitch control Security.
Background technique
Wind energy is as a kind of clean renewable energy sources, can solve to a great extent non-renewable energy resources environmental pollution problem, especially in fossil energy, face exhaustion, greenhouse gas emission day by day increases, had a strong impact in the situation of global climate, the application and development of wind technology has extremely been paid close attention in countries in the world.There are the grass resources of the length and breadth of land and very long shore line in China, and wind energy content is abundant, but because the feature of wind energy resources self has determined that the physical environment of wind field is generally all more severe, the more north that is distributed in severe cold and moist coastal region.
When wind power generating set is moved as humid air, rainfall or ice and snow weather under low temperature environment, will there is freezing phenomenon.The attached ice of wind power generating set blade not only can produce ice and carry, and can affect the life-span of blade.If the operation of blade band ice, more can produce very large harm to unit: (1), if cause unit directly to be shut down because blade has ice, will make the long-term unit generation amount in low temperature area greatly reduce; (2) after the attached ice of blade, because the attached ice thickness in each cross section of blade differs, can directly affect the load of wind power generating set and exert oneself, the generating efficiency of unit is reduced; (3) the attached ice of blade, not only can produce potential safety hazard to blower fan self, also can threaten to resources such as field personnel, local resident and livestocks.
Summary of the invention
For addressing the above problem, the invention provides a kind of prediction algorithm of wind generating set pitch control Security, be intended to extend unit lodicule working life, improve unit generation efficiency.
Mentality of designing of the present invention is: utilization of the present invention to wind speed, wheel speed, blade in spatial position, become the collection of the series of parameters such as propeller angle, air temperature, humidity of the air, fan vibration numerical value and blade material coefficient, by adopting BP three-layer neural network technology to predict the attached ice possibility of blade.
Technological scheme of the present invention is specific as follows:
A prediction algorithm for wind generating set pitch control Security, adopts BP three-layer neural network to predict the possibility of the attached ice of blade, and described BP three-layer neural network comprises input layer, hidden layer and output layer; Input layer comprises 8 input nodes, is respectively: wind speed x1, air temperature x2, humidity of the air x3, change propeller angle x4, wheel speed x5, spatial position x6, the fan vibration numerical value x7 of blade, blade material coefficient x8; Hidden layer includes 3 node z1-z3; Output layer comprises 1 output node: icing rate v; Input node, the function relation of hidden node and output node is as follows:
X
irepresent the integrated value that may affect icing data of unit collection, f1, f2 is the intrinsic parameter of neuron network, and n is constant, and i is a certain parameter, and the weights between input layer and hidden layer are h, the weights of hidden layer and output layer are w, z
kfor hidden node, v is icing rate, and k is three blade numberings; Weight w, f1, the f2 of described input layer and weights h, hidden layer and the output layer between hidden layer draw by software emulation;
The job step of described BP three-layer neural network is:
First BP three-layer neural network is tested, when performance and the error of BP three-layer neural network all converges to after certain standard, utilized the BP three-layer neural network of having tested to carry out attached ice prediction; Then using new ice-formation condition as input, utilize the icing thickness of every blade of BP three-layer neural network prediction; Finally when v value surpasses limit value vmax certain hour t
1after, can think that fan blade surface freezes, icing thickness runs up to certain hour t
2after, unit carries out relevant deicing action.Described deicing action can select any known mode to carry out.But preferably use the change oar Self-Protection Subsystem in embodiment to carry out deicing.
Beneficial effect of the present invention is: a kind of prediction algorithm of wind generating set pitch control Security, when having solved wind power generating set and having moved under rugged environment, blade working life of causing the problem such as short, unit generation Efficiency Decreasing; Guaranteed the safety of field personnel, local resident and livestock; Improved the safety and reliability of wind power generating set.
Accompanying drawing explanation
The present invention has accompanying drawing 3 width.
Fig. 1 is BP three-layer neural network topological diagram;
Fig. 2 is BP three-layer neural network workflow diagram;
Fig. 3 becomes oar Self-Protection Subsystem control logic figure in embodiment;
Fig. 4 becomes oar Self-Protection Subsystem figure in embodiment.
Embodiment
Below in conjunction with the present embodiment, the present invention is further described:
A prediction algorithm for wind generating set pitch control Security, adopts BP three-layer neural network to predict the possibility of the attached ice of blade, and described BP three-layer neural network comprises input layer, hidden layer and output layer; Input layer comprises 8 input nodes, is respectively: wind speed x1, air temperature x2, humidity of the air x3, change propeller angle x4, wheel speed x5, spatial position x6, the fan vibration numerical value x7 of blade, blade material coefficient x8; Hidden layer includes 3 node z1-z3; Output layer comprises 1 output node: icing rate v; Input node, the function relation of hidden node and output node is as follows:
X
irepresent the integrated value that may affect icing data of unit collection, f1, f2 is the intrinsic parameter of neuron network, and n is constant, and i is a certain parameter, and the weights between input layer and hidden layer are h, the weights of hidden layer and output layer are w, z
kfor hidden node, v is icing rate, and k is three blade numberings; Weight w, f1, the f2 of described input layer and weights h, hidden layer and the output layer between hidden layer draw by software emulation;
The job step of described BP three-layer neural network is:
First BP three-layer neural network is tested, when performance and the error of BP three-layer neural network all converges to after certain standard, utilized the BP three-layer neural network of having tested to carry out attached ice prediction; Then using new ice-formation condition as input, utilize the icing thickness of every blade of BP three-layer neural network prediction; Finally when v value surpasses limit value vmax certain hour t
1after, can think that fan blade surface freezes, icing thickness runs up to certain hour t
2after, unit carries out relevant deicing action.Described deicing action can select any known mode to carry out.But preferably use change oar Self-Protection Subsystem to carry out deicing.
Described change oar Self-Protection Subsystem, comprising: the motor of being with the main control PLC of the logic that opens ice, the change oar frequency variator of being with the prediction algorithm that opens ice, electric current device, drive blade rotation; Described band the open ice input end of change oar frequency variator of prediction algorithm of main control PLC and the band of logic that opens ice is connected, be with the output terminal of the change oar frequency variator of the prediction algorithm that opens ice to be connected with driving the motor of blade rotation, described current transformer is connected with being with the change oar frequency variator of the prediction algorithm that opens ice.
The specific works step of this system is:
According to the prediction algorithm that becomes oar Security, when the blade thickness that freezes has reached the scope of automatic de-icing, or automatic de-icing is when arrive cycle time, and system enters initiatively deicing step;
Described active deicing step comprises:
(1) whether the current transformer in blower fan detects blower fan in generator operation state, if now blower fan is in low wind outage state, by the open ice change oar frequency variator of prediction algorithm of band, makes to drive the motor of blade rotation to accelerate to the racing speed of permission;
(2) in running order if the current transformer in blower fan detects blower fan, by the open ice change oar Frequency Converter Control of prediction algorithm of band, drive the motor of blade rotation to enter emergency feathering state, the open ice main control PLC of logic of band records the vibration values in cabin in feathering process by control with the change oar frequency variator of the prediction algorithm that opens ice;
(3) after feathering completes, if the vibration values of record is less than the critical value of regulation, think that this feathering deicing is invalid, repeat (1) (2) process, if be still less than the critical value of regulation in triplicate, alerts triggered, directly enters initiatively collision block link;
Described active collision block link specifically comprises:
The band motor that the main control PLC of the logic change oar frequency variator by the controls band prediction algorithm that opens ice will drive blade rotation that opens ice switches to speed control mode, with increasing velocity change oar;
When blade move angle is greater than after 6 degree, band opens ice the main control PLC of the logic motor by the change oar Frequency Converter Control drive blade rotation of the controls band prediction algorithm that opens ice with 0.3deg/s speed shock collision block, when blade rotational speed is less than 0.05deg/s and torque and is greater than 50Nm and continues 3s, think that blade has arrived collision block stop position, stops becoming oar;
(4) remove related data and remove warning, finishing deicing.
Claims (1)
1. a prediction algorithm for wind generating set pitch control Security, is characterized in that: adopt BP three-layer neural network to predict the possibility of the attached ice of blade, described BP three-layer neural network comprises input layer, hidden layer and output layer; Input layer comprises 8 input nodes, is respectively: wind speed x1, air temperature x2, humidity of the air x3, change propeller angle x4, wheel speed x5, spatial position x6, the fan vibration numerical value x7 of blade, blade material coefficient x8; Hidden layer includes 3 node z1-z3; Output layer comprises 1 output node: icing rate v; Input node, the function relation of hidden node and output node is as follows:
X
irepresent the integrated value that may affect icing data of unit collection, f1, f2 is the intrinsic parameter of neuron network, and n is constant, and i is a certain parameter, and the weights between input layer and hidden layer are h, the weights of hidden layer and output layer are w, z
kfor hidden node, v is icing rate, and k is three blade numberings; Weight w, f1, the f2 of described input layer and weights h, hidden layer and the output layer between hidden layer draw by software emulation;
The job step of described BP three-layer neural network is:
First BP three-layer neural network is tested, when performance and the error of BP three-layer neural network all converges to after certain standard, utilized the BP three-layer neural network of having tested to carry out attached ice prediction; Then using new ice-formation condition as input, utilize the icing thickness of every blade of BP three-layer neural network prediction; Finally when v value surpasses limit value vmax certain hour t
1after, can think that fan blade surface freezes, icing thickness runs up to certain hour t
2after, unit carries out relevant deicing action.
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| CN104005917A (en) * | 2014-04-30 | 2014-08-27 | 叶翔 | Method and system for predicting wind machine state based on Bayesian reasoning mode |
| WO2019086287A1 (en) * | 2017-10-30 | 2019-05-09 | fos4X GmbH | Method for forecasting the yield of a wind farm under icing conditions |
| CN110147811A (en) * | 2019-04-02 | 2019-08-20 | 宜通世纪物联网研究院(广州)有限公司 | Fan blade prediction method and system based on time window hybrid model |
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| CN101886617A (en) * | 2010-06-07 | 2010-11-17 | 三一电气有限责任公司 | Wind generating set and blade deicing system thereof |
| CN102003353A (en) * | 2010-12-10 | 2011-04-06 | 重庆大学 | Deicing method for blades of large-scale wind driven generator |
| WO2011117246A2 (en) * | 2010-03-23 | 2011-09-29 | Vestas Wind Systems A/S | A method for de-icing the blades of a wind turbine and a wind turbine with a de-icing system |
| CN102622482A (en) * | 2012-03-06 | 2012-08-01 | 中国科学院工程热物理研究所 | Fan optimization arrangement method based on binary particle swarm optimization (BPSO) |
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| WO2011117246A2 (en) * | 2010-03-23 | 2011-09-29 | Vestas Wind Systems A/S | A method for de-icing the blades of a wind turbine and a wind turbine with a de-icing system |
| CN101886617A (en) * | 2010-06-07 | 2010-11-17 | 三一电气有限责任公司 | Wind generating set and blade deicing system thereof |
| CN102003353A (en) * | 2010-12-10 | 2011-04-06 | 重庆大学 | Deicing method for blades of large-scale wind driven generator |
| CN102622482A (en) * | 2012-03-06 | 2012-08-01 | 中国科学院工程热物理研究所 | Fan optimization arrangement method based on binary particle swarm optimization (BPSO) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104005917A (en) * | 2014-04-30 | 2014-08-27 | 叶翔 | Method and system for predicting wind machine state based on Bayesian reasoning mode |
| WO2019086287A1 (en) * | 2017-10-30 | 2019-05-09 | fos4X GmbH | Method for forecasting the yield of a wind farm under icing conditions |
| CN111295600A (en) * | 2017-10-30 | 2020-06-16 | 福斯4X股份有限公司 | Method for forecasting the output of a wind farm in icing conditions |
| CN110147811A (en) * | 2019-04-02 | 2019-08-20 | 宜通世纪物联网研究院(广州)有限公司 | Fan blade prediction method and system based on time window hybrid model |
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Effective date of registration: 20190614 Address after: 116601 Room 1505, No. 9-1 Haifu Road, Dalian Free Trade Zone, Liaoning Province Patentee after: Xiying Science and Technology (Dalian) Co., Ltd. Address before: 116600 Dalian Shanneng Science and Technology Development Co., Ltd. of No. 10 ShuangD Fifth Street, Dalian Development Zone, Liaoning Province Patentee before: Dalian Shinergy Science and Technology Development Co., Ltd. |
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Effective date of registration: 20190731 Address after: 116600 Dalian City, Liaoning Province, China (Liaoning) Free Trade Pilot Zone Dalian Economic and Technological Development Zone Life Road 39-13-2, -3, -4 Patentee after: Dalian Shangjia New Energy Technology Co., Ltd. Address before: 116601 Room 1505, No. 9-1 Haifu Road, Dalian Free Trade Zone, Liaoning Province Patentee before: Xiying Science and Technology (Dalian) Co., Ltd. |