CN201723364U - Wind power station fan load index predicting device - Google Patents

Abstract

The utility model relates to a wind power station fan load index predicting device, which belongs to the technical field of wind power generation. The device comprises a sensor, a data collection chip, a central processing unit, an industrial computer and a wireless communication module, wherein all components are connected, the output end of the sensor is connected with the input end of the data collection chip, the output end of the data collection chip is connected with the input end of the central processing unit, and the output end of the central processing unit is connected with the input end of the industrial computer and the wireless communication module. A wind power station fan load index predicting method of the utility model is realized through two parts: a controller and a mathematical model, wherein the controller comprises the sensor, the data collection chip, the central processing unit, the industrial computer and the wireless communication module. The utility model has the advantages that the self state monitoring of the fan in the predicting process can be realized, and the establishment of a vector machine model is supported through the least square method, so the utility model has the advantages of high precision, good accuracy and high prediction efficiency of the fan load index prediction.

Description

A kind of wind electric field blower load factor prediction unit

Technical field

The utility model belongs to technical field of wind power generation, particularly a kind of wind electric field blower load factor prediction unit.

Background technique

At present, Large-scale Wind Turbines is generally horizontal axis wind-driven generator, it is by wind wheel, step-up gear, generator, yaw device, control system, parts such as pylon are formed, wind acts on the blade with the certain speed and the angle of attack, make blade produce running torque and rotate, the energy of wind is transformed into mechanical energy, wind is big more, the energy that wind wheel is accepted wind is also big more, wind wheel changes just soon more, the effect of wind wheel is that wind energy is converted to mechanical energy, it is made up of the blade of aeroperformance excellence, unit is generally 2-3 blade and is contained on the wheel hub, the wind wheel that slowly runs by transmission system by the step-up gear speedup, give generator with transmission of power, above-mentioned these parts all are installed on the plane, cabin, whole cabin is lifted by tall and big built, because wind direction often changes, when blower fan when the wind wheel imbalance fault occurring, even wind-force is enough big, blower fan does not change yet, and when controller of fan broke down, blower fan then can not autostop, because weather conditions such as temperature, pressure etc. are to the influence of blower fan, blower fan can not well be moved, influence generating effect;

The power prediction of wind energy turbine set is by the various weather of locality, the influence of weather conditions, requires the maximum output of blower fan, and this Forecasting Methodology is that very big error is arranged.Because there is not to consider at the state of blower fan in such cases the output power of the wind-power electricity generation of yet just having no idea to predict accurately.

Summary of the invention

In order to overcome the deficiency of prior art, the purpose of this utility model provides a kind of wind electric field blower load factor prediction unit, in order to effectively utilize wind energy, wind plant must be arranged, the wind direction signals that it records according to wind transducer is by controller control yaw motor, the pinion rotation of gearwheel interlock on driving and the pylon, because the prediction of blower fan load factor, thereby more effectively mechanical energy is converted into electric energy.

The utility model wind electric field blower load factor prediction unit: include sensor, data acquisition chip, central processing unit (CPU), process control machine and wireless communication module, each parts connects: the output terminal of sensor connects the input end of data acquisition chip, the output terminal of data acquisition chip connects the input end of central processing unit (CPU), and the output terminal of central processing unit (CPU) connects the input end of process control machine and wireless communication module.

Adopt wind electric field blower load factor prediction unit to carry out forecast method, utilize the method for least squares supporting vector machine model to predict, comprise the steps:

Step 1, employing wind energy turbine set load factor prediction unit are gathered group of motors wing setting angle degree, engine bearing vibration frequency, tower bar perpendicularity and the engine torque of wind energy turbine set as input quantity;

Step 2, set up forecast sample, form training sample set;

With wind-powered electricity generation unit wing setting angle degree, engine bearing vibration frequency, tower bar perpendicularity and engine torque as input quantity; If input, output sample File are { x _k, y _k(k=1,2 ..., N), N is a natural number; Wherein, x _kBe n dimension input vector, x _k∈ R ⁿ, R ⁿRepresent the real number amount of multidimensional; y _kBe wind electric field blower load factor, y _k∈ R ⁿLinear equation in feature space can be expressed as following form:

y _k＝ω ^Tφ(x)+b (1)

In the formula (1),

: R → R ^NhThe input space is mapped as the mapping function of high-dimensional feature space;

ω is the weight vector of hyperplane, ω ∈ R ⁿB is an Offset;

Step 3, will import sample data collection input method of least squares supporting vector machine model;

Method of least squares supporting vector machine model Select Error e _kQuadratic sum be loss function, it is optimized for

Wherein, s.t. is a constraint conditio, and N is a natural number, and γ＞0 is the penalty coefficient factor, and e is an error, e _kBe K error, be used for the effect that the regulating and controlling error is got, can get one and trade off between training error and model complexity, so that make the function of being asked have good generalization ability, and the γ value is big more, and the regression error of model is few more;

Step 4, find the solution the method for least squares supporting vector machine model;

Determine the regression parameter a of method of least squares supporting vector machine model _k=[a ₁, a ₂..., a _N] ^TAnd b, introduce Lagrange (Lagrange) function and find the solution:

In the formula (3), a _k(k=1,2 ..., N) be the Lagrange multiplier;

Optimum a _kObtain by Michael Carruth-Ku En-Plutarch (KKT) condition with b, promptly

By variable ω and the e in the subtractive (4) _k, optimization problem is converted into finds the solution following linear equations:

$\left[\begin{array}{c}\begin{array}{cc}0& \stackrel{\‾}{1^T}\end{array}\\ \stackrel{\‾}{1}\mathrm{\Ω}+{\mathrm{\γ}}^{-2}\·1\end{array}\right]\left[\begin{array}{c}b\\ a_k\end{array}\right]=\left[\begin{array}{c}0\\ y_k\end{array}\right]---\left(5\right)$

In the formula (5), y _k=[y ₁, y ₂..., y _N] ^T

a _k=[a ₁, a ₂..., a _N] ^TI is a unit matrix; Ω is a square formation;

According to Mercer, Johnny (Mercer) condition as can be known, there is mapping

Make with kernel function K ():

Ω _k＝φ(x _k) ^Tφ(x _k+1)＝k(x _k，x _k+1)i＝1，2，…，N； (6)

The a that obtains _kBring into the numerical value of b, find the solution nuclear width cs and penalty coefficient factor gamma, promptly obtain the method for least squares supporting vector machine model by adaptive selection method;

Obtain a by formula (5) _k, behind the b, can obtain the method for least squares supporting vector machine model and be:

$y_k\left(x\right)={\mathrm{\Σ}}_{K=1}^N{a}_{K}K(x_K,x_{k+1})+b---\left(7\right)$

The kernel function that the utility model is selected for use is radially basic (RBF) function, promptly

$K=(x_k,x_{k+1})=\exp (-\frac{{\left|\right|x_k-x_{k+1}\left|\right|}^2}{{2\mathrm{\σ}}^2})---\left(8\right)$

Step 5, input sample data collection obtain the wind energy turbine set load factor;

By formula (7) method of least squares supporting vector machine model output sample File y _k, y _kBe the wind energy turbine set load factor of prediction.

Advantage of the present utility model: the utility model wind electric field blower load factor prediction unit, proposed to utilize the state of blower fan self, by sensor monitors wind-powered electricity generation unit wing setting angle degree, engine torque, engine bearing, tower bar perpendicularity, the arrestment mechanism wearing piece, the fastening piece of wind wheel rotating part, the factor that controller failure influences the wind-powered electricity generation unit is input, realize the monitoring of blower fan oneself state in the forecasting process, foundation by the method for least squares supporting vector machine model, make and predict for the blower fan load factor, accurately high, degree of accuracy is good, the forecasting efficiency height.

Description of drawings:

Fig. 1 the utility model wind-powered electricity generation unit load exponential forecasting apparatus structure is always schemed;

Fig. 2 the utility model wind-powered electricity generation unit load exponential forecasting hardware is realized block diagram;

The data capture and the transmission circuit figure of Fig. 3 the utility model wind-powered electricity generation unit load exponential forecasting terminal;

Fig. 4 (a) the utility model wind-powered electricity generation unit load index forecasting method flow chart;

Fig. 4 (b) the utility model is set up method of least squares supporting vector machine model method flow diagram;

Fig. 5 the utility model prediction load factor and actual load index curve diagram;

Embodiment:

A kind of wind electric field blower load factor of the utility model prediction unit is illustrated with accompanying drawing in conjunction with the embodiments;

The device that this wind electric field blower load factor method is used includes sensor, data acquisition chip, central processing unit (CPU), process control machine and wireless communication module; Wherein the voltage transformer summation current transformer on the sensor is selected JDG-0.5 800/100 model and LZJC-10Q 1500/5 model respectively for use, wireless network communication module adopts H7000 series wireless communication system, process control machine adopts UNO-2100 Series PC/104+ built-in industrial control machine, central processing unit (CPU) adopts dsp chip, dsp chip is a TMS320VC5402 series fixed point type DSP digital signal processor, clock frequency is 100MHz, machine cycle is 10ns, the interface power supply is 3.8V, core power is 1.8V, data acquisition chip adopts the MAX125 data acquisition chip to sample and analog-to-digital conversion, by ± the 5V power supply, its clock pin CLK connects the active crystal oscillator of 16MHz; The datawire D0-D13 here is the B0-B13 that 14 transformational structures after the conversion are sent into DSP, and SHT11 is intelligent temperature/humidity sensor, GND: grounding end; DATA: bidirectional serial data lines; SCK: serial clock input; The VDD power end; Other blank pipe pin, the resolution of temperature value output is 12, humidity value is output as 14, as Fig. 1, Fig. 2 and shown in Figure 3;

This installs the connection of each parts: the output terminal of temperature transducer and humidity transducer is connected input end BDX and the BDR of DSP, the CH1A that voltage transducer, current sensor and shock sensor output terminal are connected data acquisition chip MAX125 holds to CHnA, output terminal CONVST, INT, RD, WR and the CS of data acquisition chip MAX125 is connected input end B14, INT, READ, WRITE and the BFSX of DSP, and the output terminal of DSP connects the input end of process control machine and wireless communication module; The electric information of wind electric field blower and mechanical information carry out synchronized sampling, maintenance, A/D via corresponding mutual inductor or sensor by sampling A and convert digital signal to, send into calculating and data processing that DSP classifies, link to each other with process control machine and deliver to wireless communication module by communication interface, for ready with the remote dispatching communication;

Utilize above-mentioned wind electric field blower load factor prediction unit to carry out forecast method, comprise the steps, shown in Fig. 4 (a):

Group of motors wing setting angle degree, engine bearing vibration frequency, tower bar perpendicularity and the engine torque of step 1, collection wind energy turbine set are as input quantity; Be that dimension is 4, gather sample value and see Table 1;

Table 1

Gather sample	The sample range of sample	The sensor output voltage scope
			Group of motors wing setting angle degree	0-90 (degree)	0-5 (volt)
The engine bearing vibration frequency	400-2500 (hertz)	0-5 (volt)
			Tower bar perpendicularity	75-90 (degree)	0-5 (volt)
Engine torque	120-150 (ox rice)	0-5 (volt)

Step 2, the analogue signal of gathering is converted into digital signal;

Digital signal input method of least squares supporting vector machine model after step 3, the conversion, the wind energy turbine set load factor that obtains predicting.

Described method of least squares supporting vector machine model is set up as follows, shown in Fig. 4 (b):

Day 24 hours load factors of predicting certain wind field are example, with the preceding 100 day data structure sample of prediction day;

1), data processing, set up training sample set and forecast sample collection, see Table 1;

2), the method for least squares supporting vector machine model is expressed as:

Method of least squares supporting vector machine model Select Error e _kQuadratic sum be loss function, it is optimized for

Wherein, s.t. is a constraint conditio, and N is a natural number, and γ＞0 is the penalty coefficient factor, and e is an error, e _kBe K error, be used for the effect that the regulating and controlling error is got, can get one and trade off between training error and model complexity, so that make the function of being asked have good generalization ability, and the γ value is big more, and the regression error of model is few more;

3), find the solution regression parameter according to the method for least squares supporting vector machine model:

a _k=[2.8712,-243.9321,22.3767,383.1647] and b=1.9514, find the solution nuclear width a=2.2347 penalty coefficient factor gamma=980.6062 by adaptive selection method, and with the learning sample of this type of sample as the method for least squares SVM prediction;

4), set up objective function, bring the learning sample data into, obtain optimal solution, establish support vector, and participate in setting up the method for least squares supporting vector machine model, determine the method for least squares supporting vector machine model according to the learning sample chosen;

Predict load factor and actual load exponential curve as shown in Figure 5, predicated error is little, and error is about 12%.

Wherein the blower fan health status of the value representative of load factor is as described in Table 2.

Table 2

Load factor

Blower fan health status

0.0-0.4	The blower fan irregular operating
		0.4-0.5	Blower fan critical operation (belonging to normal)
0.5-1.0	Blower fan normally moves

Claims (1)

1. wind electric field blower load factor prediction unit, it is characterized in that: this device includes sensor, data acquisition chip, central processing unit (CPU), process control machine and wireless communication module, each parts connects: the output terminal of sensor connects the input end of data acquisition chip, the output terminal of data acquisition chip connects the input end of central processing unit (CPU), and the output terminal of central processing unit (CPU) connects the input end of process control machine and wireless communication module.

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