CN111856185A - Embedded system and method for monitoring and improving electric energy quality of wind turbine generator - Google Patents
Embedded system and method for monitoring and improving electric energy quality of wind turbine generator Download PDFInfo
- Publication number
- CN111856185A CN111856185A CN202010714214.0A CN202010714214A CN111856185A CN 111856185 A CN111856185 A CN 111856185A CN 202010714214 A CN202010714214 A CN 202010714214A CN 111856185 A CN111856185 A CN 111856185A
- Authority
- CN
- China
- Prior art keywords
- current
- wind turbine
- turbine generator
- power
- voltage
- 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
- 238000000034 method Methods 0.000 title claims abstract description 67
- 238000012544 monitoring process Methods 0.000 title claims abstract description 46
- 238000001514 detection method Methods 0.000 claims abstract description 21
- 238000004891 communication Methods 0.000 claims abstract description 11
- 238000013528 artificial neural network Methods 0.000 claims description 20
- 230000009466 transformation Effects 0.000 claims description 18
- 230000001052 transient effect Effects 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 238000001228 spectrum Methods 0.000 claims description 11
- 230000003750 conditioning effect Effects 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 9
- 239000002245 particle Substances 0.000 claims description 7
- 238000010521 absorption reaction Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 6
- 238000002955 isolation Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 230000008602 contraction Effects 0.000 claims description 4
- 238000007493 shaping process Methods 0.000 claims description 4
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000000737 periodic effect Effects 0.000 claims description 3
- 238000010183 spectrum analysis Methods 0.000 claims description 3
- 230000006872 improvement Effects 0.000 claims description 2
- 238000012800 visualization Methods 0.000 abstract description 5
- 230000008859 change Effects 0.000 description 4
- 238000010248 power generation Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000003990 capacitor Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Eletrric Generators (AREA)
Abstract
The invention relates to an embedded system for monitoring and improving the electric energy quality of a wind turbine generator, which comprises a main control module, a signal acquisition module, a frequency detection module, a power control module, a communication module, an upper computer, a storage module and a wind speed sensor, wherein one end of the signal acquisition module, one end of the frequency detection module and one end of the power control module are respectively connected with the existing wind turbine generator, the other end of the signal acquisition module, one end of the frequency detection module and one end of the power control module are respectively connected with the main control module, the storage module and the wind speed sensor are respectively connected with the main control module, and the. The invention also relates to a method for monitoring and improving the electric energy quality of the wind turbine generator, which is embedded in the embedded system and used for monitoring and improving the electric energy quality of the wind turbine generator. Compared with the prior art, the method has the advantages of high reliability, good real-time performance, realization of visualization of power quality monitoring, strong practicability and the like.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to an embedded system and a method for monitoring and improving the electric energy quality of a wind turbine generator.
Background
Wind energy has a very wide application prospect as a clean energy source, but because the stability of wind energy is extremely poor, the speed and direction change is rapid and random, how to effectively detect the problems of voltage flicker, power instability, harmonic pollution and other electric energy quality caused by unstable wind speed, how to rapidly and accurately control a wind power system to adjust and improve the electric energy quality is the primary problem to be solved in the research of wind power, so the research on monitoring the electric energy quality of a wind generating set in recent years is very popular, such as the utilization of a PLC (programmable logic controller) technology, a sensor technology, a single chip microcomputer technology and the like.
The design of monitoring, improving and visualizing the power quality of the wind driven generator by utilizing the prior art, such as a 16-bit singlechip and the like, has the defects of limited data operation and analysis capability and low system safety and instantaneity. With the continuous improvement of the data quantity calculation of modern power equipment including the real-time requirement, the data processing devices cannot adapt to the power requirement on the calculation, the reliability of monitoring the power quality of the wind generation set cannot be ensured, the overall data processing effect of the power system is poor, the data analysis and the final monitoring effect are influenced, and the power quality cannot be effectively adjusted and improved, so that the influence and fluctuation on a power grid are caused, and the loss which is difficult to estimate is caused.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an embedded system and a method for monitoring and improving the electric energy quality of a wind turbine generator, which have the advantages of high reliability, visualization of electric energy quality monitoring, strong practicability and good real-time performance.
The purpose of the invention can be realized by the following technical scheme:
an embedded system for monitoring and improving the power quality of a wind turbine, the system is connected with an existing wind turbine, and the embedded system comprises: the device comprises a main control module, a signal acquisition module, a frequency detection module, a power control module, a communication module, an upper computer, a storage module and a wind speed sensor; one end of the signal acquisition module, one end of the frequency detection module and one end of the power control module are respectively connected with the existing wind turbine generator, and the other end of the signal acquisition module, the frequency detection module and the power control module are respectively connected with the main control module; the storage module and the wind speed sensor are respectively connected with the main control module; the main control module is communicated with an upper computer through a communication module.
Preferably, the main control module is an FPGA chip.
Preferably, the signal acquisition module comprises a voltage sensor, a current sensor, a signal conditioning circuit and a voltage and current sampling circuit; one end of the voltage sensor and one end of the current sensor are respectively connected with the existing wind turbine generator, and the other ends of the voltage sensor and the current sensor are respectively connected with the input end of the signal conditioning circuit; the output end of the signal conditioning circuit is connected with the input end of the voltage and current sampling circuit; and the output end of the voltage and current sampling circuit is connected with the main control module.
Preferably, the frequency detection module comprises an isolation transformer, a filter shaping circuit, a schmitt trigger, a double-D trigger and a phase inverter which are connected in sequence; the primary side of the isolation transformer is connected with the existing wind turbine generator; and the output end of the phase inverter is connected with the main control module.
Preferably, the power control module comprises a network side and rotor side PWM converter, an SVPWM inverter control unit, an IGBT drive unit and an FPGA main control unit which are connected in sequence; the input ends of the network side PWM converters and the rotor side PWM converters are respectively connected with the existing power grid and the generators of the existing wind turbine generator; and the output end of the IGBT driving unit is connected with the main control module.
A method for monitoring and improving the power quality of a wind turbine generator for the embedded system comprises the following steps:
step 1: acquiring a predicted wind speed by adopting a wind speed prediction sub-method according to the wind speed and wind direction samples uploaded by the main control module;
step 2: acquiring voltage and current signals of the rotor and stator sides of the existing wind turbine generator, and acquiring frequency through a frequency detection module;
and step 3: analyzing the quality of the electric energy output by the existing wind turbine generator at the current moment by adopting a harmonic distortion analysis sub-method and a transient disturbance quantity monitoring sub-method;
and 4, step 4: acquiring rotating speed data of a generator rotor of an existing wind turbine generator;
and 5: calculating the power of a generator in the current existing wind turbine;
step 6: judging whether the current power of the generator meets the load requirement, if so, executing a step 8, otherwise, executing a step 7;
and 7: performing power control by using a power control sub-method, and then executing the step 8;
and 8: acquiring wind speed prediction data;
and step 9: judging whether the absorption power of the unit needs to be adjusted, if so, adjusting the absorption power of the unit, and then executing the step 10, otherwise, directly executing the step 10;
step 10: and finishing the calculation of the current round, acquiring the wind speed data of the current moment again, judging whether the difference value of the wind speed and the wind speed prediction data of the previous moment is larger than a preset error, if so, returning to the step 1, and then performing the calculation of the next round, otherwise, returning to the step 2, and performing the calculation of the next round.
Preferably, the wind speed predictor method comprises:
step 1-1: acquiring voltage and current at the output end of the stator side of the generator of the existing wind turbine generator and voltage and current signals at the rotor side, which are acquired by a signal acquisition module, and acquiring wind speed sample data acquired by a wind speed sensor;
step 1-2: carrying out normalization processing on the data to obtain a sample training set;
the normalization treatment specifically comprises the following steps:
wherein v isiThe original value of the wind speed sample data; v. ofmaxAnd vminRespectively a maximum value and a minimum value in the wind speed sample data; v. ofi' is the output value after wind speed normalization;
step 1-3: initializing an improved PSO algorithm;
step 1-4: calculating initial fitness, wherein the calculation method of the fitness comprises the following steps:
wherein N is the sample volume; v. ofi *The wind speed is a predicted value;
step 1-5: carrying out iteration optimization and updating the particle speed and position, wherein the specific method comprises the following steps:
wherein D is 1,2,3, …, D; 1,2,3, …, N; k is the current iteration number; v. ofidIs the current speed; c. C1And c2Is an acceleration factor, and c1And c2Are all larger than zero; r is1And r2Is a random function, and the value ranges are all [0,1 ]]；
And
the position and velocity of k +1 iterations for the ith particle in D dimension respectively; z is a contraction factor; phi is the total acceleration factor; genThe total number of iterations; k is a shrinkage coefficient;
step 1-6: calculating fitness, and updating individual, global optimum fitness and optimal solution;
step 1-7: judging whether the fitness meets the requirement or whether the iteration times reach the maximum iteration times, if so, executing the step 1-8, otherwise, returning to the step 1-5;
step 1-8: and training the LSSVM by using the global optimal solution as a parameter of the LSSVM, so as to obtain wind speed prediction data.
Preferably, the harmonic distortion analysis method includes:
step 2-1: denoising voltage and current signals at the sides of the rotor and the stator by using an FIR filter;
step 2-2: inputting data in a bit reverse order mode, and outputting the data obtained by using a windowing FFT method according to a natural order;
step 2-3: calculating the total harmonic distortion rate by the following method:
wherein, UnIs the effective value of the nth harmonic voltage; i isnIs the effective value of the nth harmonic current; THDuIs the voltage total harmonic distortion rate; THDiIs the current total harmonic distortion rate; u shape1Is the effective value of the fundamental voltage; i is1Is the effective value of the fundamental current;
step 2-4: and judging whether the total harmonic distortion rate exceeds a preset value, if so, cutting the existing wind turbine generator out of the power grid, and otherwise, ending the cycle.
Preferably, the transient disturbance amount monitoring sub-method includes:
step 3-1: judging whether the voltage and current signals of the rotor and stator sides obtained in the step 2 exceed preset values, if so, cutting the fan out of a power grid, and ending the circulation of the current round, otherwise, executing the step 3-2;
step 3-2: denoising the voltage and current signal data on the stator side by using an FIR filter;
step 3-3: adopting a Hilbert-Huang transform detection algorithm HHT to analyze the data obtained in the step 3-2, wherein the HHT algorithm comprises an EMD method and a Hilbert spectrum analysis method, firstly decomposing a given signal into a plurality of intrinsic mode functions IMFs by using the EMD method, then carrying out Hilbert transform on each IMF, and obtaining characteristic parameters to obtain a Hilbert spectrum of a corresponding original signal, and the specific steps are as follows:
decomposing the empirical mode to obtain an inherent mode function group:
wherein s isi(t) is each natural modal component in the original signal; d (t) is the DC component in the original signal; r (t) is a residual function data sequence obtained by decomposition;
and then performing Hilbert transformation on all IMFs to obtain an analytic expression of each IMF of the transformation:
wherein, aiFor IMF amplitude, the above equation is a Hilbert spectrum, which can be written as:
integrating the above formula in the time domain to obtain the corresponding Hilbert marginal spectrum:
wherein the instantaneous frequency is:
step 3-4: judging whether transient disturbance including voltage swell disturbance, voltage sag disturbance, frequency fluctuation disturbance and the like occurs, if so, executing the step 3-5, otherwise, returning to the step 3-1, and executing the next round of circulation;
step 3-5: judging whether the transient disturbance is periodic disturbance, if so, executing the step 3-6, otherwise, returning to the step 3-1, and executing the next round of circulation;
step 3-6: calculating the disturbance quantity;
step 3-7: and judging whether the disturbance quantity exceeds a grid-connected standard, if so, switching the fan out of the power grid, otherwise, returning to the step 3-1, and executing the next cycle.
Preferably, the power control sub-method includes:
step 4-1: controlling the power of the unit by using a power control model;
the power control model comprises a PID sub-model and a coordinate transformation sub-model based on a neural network;
the PID submodel based on the neural network is an incremental PID control algorithm which selects the neural network as a training model:
u(k)＝u(k-1)+Δu(k)
Δu(t)＝kp(e(k)-e(k-1))+kie(k)+kd(e(k)-2e(k-1)+e(k-2))
wherein u (k-1) is the state at the previous moment; u (k) is the state at the current time; k is a radical ofp、kiAnd kdProportional coefficient, integral coefficient and differential coefficient respectively;
the training by using the neural network comprises the following steps:
s1: determining the node numbers N and Q of an input layer and an implicit layer, giving initial values of weighting coefficients of all layers, selecting a learning rate eta and an inertia coefficient alpha, and setting k to be 1;
s2: calculating the error of the current moment:
e(k)＝rin(k)-yout(k)
wherein rin (k) is a desired amount; yout (k) is the amount of output;
s3: calculating the input and output of each layer of neuron of the neural network model, and selecting the final output of the neural network as kp、kiAnd kd；
S4: calculating u (k);
s5: performing neural network learning, modulating a weighting coefficient, and realizing self-adaptive adjustment of PID control parameters;
s5: setting k to k +1, returning to S1 and repeating the execution;
the 2s/2r transformation matrix of the coordinate transformation submodel is specifically as follows:
wherein theta is an included angle between an alpha axis and a d axis;
the inverse matrix of the 2s/2r transformation matrix is specifically:
step 4-2: and recalculating the active power and the reactive power, judging whether the preset threshold is met, if so, ending the circulation of the current round, otherwise, returning to the step 4-1.
Compared with the prior art, the invention has the following advantages:
firstly, the reliability of power quality monitoring is high, and simultaneously, the power quality is effectively adjusted and improved: the embedded system for monitoring and improving the power quality of the wind turbine generator adopts the FPGA as a processor, has the capability of high-performance parallel algorithm, and improves the reliability and the real-time performance of the power quality monitoring; meanwhile, the quality of the electric energy is effectively improved by monitoring the transient disturbance quantity, analyzing harmonic distortion and controlling power, and the influence and fluctuation on the power grid are reduced.
Secondly, realizing the visualization of the power quality monitoring: the embedded system for monitoring and improving the power quality of the wind turbine generator is provided with the upper computer, and data related to the power quality monitoring is visually processed in the upper computer, so that the visualization of the power quality monitoring is realized.
Thirdly, the practicability is strong: the embedded system and the method for monitoring and improving the power quality of the wind turbine generator can effectively detect the power quality problems such as voltage change, power instability, harmonic pollution and the like caused by unstable wind speed, can quickly and accurately control the wind turbine generator system to adjust and improve the power quality, and reduce economic loss and casualties caused by the power quality problems.
Fourthly, good instantaneity: the embedded system for monitoring and improving the electric energy quality of the wind turbine generator takes the FPGA chip as the main control module, and monitors and improves the electric energy quality of the wind turbine generator by utilizing the advantages of high parallel computing capability, high speed and good real-time performance.
Drawings
FIG. 1 is a schematic diagram of an embedded system according to the present invention;
FIG. 2 is a schematic flow chart of a method for monitoring and improving power quality according to the present invention;
FIG. 3 is a flow chart of a wind speed predictor method according to the present invention;
FIG. 4 is a schematic flow chart of a harmonic distortion analysis sub-method of the present invention;
FIG. 5 is a schematic flow chart of a transient disturbance amount monitoring sub-method according to the present invention;
FIG. 6 is a flow chart of a power control sub-method of the present invention;
FIG. 7 is a control block diagram of the neural network based PID submodel of the present invention.
The reference numbers in the figures indicate:
1. the wind power generation system comprises an existing wind power generation set, a main control module, a signal acquisition module, a frequency detection module, a power control module, a communication module, a host computer, a storage module, a wind speed sensor, a voltage sensor, a current sensor, a signal conditioning circuit, a voltage and current sampling circuit, a transformer, a filtering and shaping circuit, a Schmidt trigger, a double-D.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
An embedded system for monitoring and improving the power quality of a wind turbine generator is structurally shown in fig. 1 and comprises: the device comprises a main control module 2, a signal acquisition module 3, a frequency detection module 4, a power control module 5, a communication module 6, an upper computer 7, a storage module 8 and a wind speed sensor 9. One end of the signal acquisition module 3, one end of the frequency detection module 4 and one end of the power control module 5 are respectively connected with the existing wind turbine generator set 1, the other end of the signal acquisition module is respectively connected with the main control module 2, the storage module 8 and the wind speed sensor 9 are respectively connected with the main control module 2, and the main control module 2 is communicated with the upper computer 7 through the communication module 6.
The main modules are described in detail below:
one, main control module 2
In the hardware design, the main control module is a circuit which forms the minimum system operation and mainly comprises a configuration unit, a power supply unit and an external memory unit. The chip of the main control module 2 adopts the FPGA with the model of SPARTAN-6 series xc6slx25-3csg324 of Xilinx company, the configuration mode adopts the JTAG mode, the power supply lead-in pins are a phase-locked loop power supply, a core power supply and an I/O port power supply, and a filter capacitor is added nearby to ensure stable power supply. Because the internal space of the FPGA is deficient, a Flash and an SDRAM are added as an expansion chip.
Secondly, a signal acquisition module 3
The signal acquisition module 3 comprises a voltage sensor 301, a current sensor 302, a signal conditioning circuit 303 and a voltage and current sampling circuit 304, one end of the voltage sensor 301 and one end of the current sensor 302 are respectively connected with the existing wind turbine generator 1, the other end of the voltage sensor 301 and the other end of the current sensor 302 are respectively connected with the input end of the signal conditioning circuit 303, the output end of the signal conditioning circuit 303 is connected with the input end of the voltage and current sampling circuit 304, and the output end of the voltage and current sampling circuit 304 is connected with the main control module 2.
The voltage and current sampling circuit 304 adopts two ADS7864 chips of TI company to sample voltage and current signals including the stator side and the rotor side.
Frequency detection module 4
The frequency detection module 4 comprises an isolation transformer 401, a filter shaping circuit 402, a schmitt trigger 403, a double-D trigger 404 and a phase inverter 405 which are connected in sequence, wherein the primary side of the isolation transformer is connected with the existing wind turbine generator 1, and the output end of the phase inverter 405 is connected with the main control module 2.
Fourth, power control module 5
The wind power generation system comprises a network side and rotor side PWM converter 501, an SVPWM inverter control unit 502, an IGBT drive unit 503 and an FPGA main control unit which are sequentially connected, wherein the input ends of the network side and rotor side PWM converter 501 are respectively connected with an existing power grid and a generator of an existing wind turbine generator 1, and the output end of the IGBT drive unit 503 is connected with a main control module 2.
Fifth, communication module 6
The network communication between the FPGA and the upper computer is realized by adopting an SMSC company LAN91C111 chip, and the data exchange is finished by an Ethernet communication protocol standard IEEE 802.3.
Sixth, upper computer 7
The data related in the embodiment can be displayed in the upper computer 7, and the upper computer 7 realizes the visualization of the power quality monitoring.
Seventhly, the wind speed sensor 9
And a wind cup type photoelectric sensor is adopted to detect the wind speed, and the model is STM 2.
The embodiment also relates to a method for monitoring and improving the power quality of a motor set, the flow of which is shown in fig. 2, and the method comprises the following steps:
step 1: acquiring a predicted wind speed by adopting a wind speed predictor method according to the wind speed and wind direction samples uploaded by the main control module 2;
step 2: acquiring voltage and current signals of the rotor and stator sides of the existing wind turbine generator 1, and acquiring frequency through a frequency detection module 4;
and step 3: analyzing the quality of the electric energy output by the existing wind turbine generator 1 at the current moment by adopting a harmonic distortion analysis sub-method and a transient disturbance quantity monitoring sub-method;
and 4, step 4: acquiring rotating speed data of a generator rotor of an existing wind turbine generator 1;
and 5: calculating the power of a generator in the current existing wind turbine generator 1;
step 6: judging whether the current power of the generator meets the load requirement, if so, executing a step 8, otherwise, executing a step 7;
and 7: performing power control by using a power control sub-method, and then executing the step 8;
and 8: acquiring wind speed prediction data;
and step 9: judging whether the absorption power of the unit needs to be adjusted, if so, adjusting the absorption power of the unit, and then executing the step 10, otherwise, directly executing the step 10;
step 10: and finishing the calculation of the current round, acquiring the wind speed data of the current moment again, judging whether the difference value of the wind speed and the wind speed prediction data of the previous moment is larger than a preset error, if so, returning to the step 1, and then performing the calculation of the next round, otherwise, returning to the step 2, and performing the calculation of the next round.
The respective sub-methods are described in detail below:
wind speed forecasting sub-method
The flow of the wind speed predictor method is shown in FIG. 3, and comprises the following steps:
step 1-1: acquiring voltage and current at the output end of the generator side of the existing wind turbine generator 1 and voltage and current signals at the rotor side acquired by a signal acquisition module 3, and acquiring wind speed sample data acquired by a wind speed sensor 9;
step 1-2: carrying out normalization processing on the data to obtain a sample training set;
the normalization treatment specifically comprises the following steps:
wherein v isiIs windThe original value of the speed sample data; v. ofmaxAnd vminRespectively a maximum value and a minimum value in the wind speed sample data; v. ofi' is the output value after wind speed normalization;
step 1-3: initializing an improved PSO algorithm;
step 1-4: calculating initial fitness, wherein the calculation method of the fitness comprises the following steps:
wherein N is the sample volume; v. ofi *The wind speed is a predicted value;
step 1-5: carrying out iteration optimization and updating the particle speed and position, wherein the specific method comprises the following steps:
wherein D is 1,2,3, …, D; 1,2,3, …, N; k is the current iteration number; v. ofidIs the current speed; c. C1And c2Is an acceleration factor, and c1And c2Are all larger than zero; r is1And r2Is a random function, and the value ranges are all [0,1 ]]；
And
the position and velocity of k +1 iterations for the ith particle in D dimension respectively; z is a contraction factor; phi is the total acceleration factor; genThe total number of iterations; k is a shrinkage coefficient;
a contraction factor z is introduced into the PSO algorithm, and the change of the week factor can ensure the convergence of the PSO algorithm and is not influenced by a speed boundary, so that the global search speed of the population is increased, and the local search capability of the particles is enhanced;
step 1-6: calculating fitness, and updating individual, global optimum fitness and optimal solution;
step 1-7: judging whether the fitness meets the requirement or whether the iteration times reach the maximum iteration times, if so, executing the step 1-8, otherwise, returning to the step 1-5;
step 1-8: and training the LSSVM by using the global optimal solution as a parameter of the LSSVM, so as to obtain wind speed prediction data.
Harmonic distortion analysis sub-method
The flow of the harmonic distortion analysis sub-method is shown in fig. 4, and includes:
step 2-1: voltage or current signals obtained by sampling contain high-frequency interference noise or high-frequency harmonic components, so that a finite-length digital FIR filter is designed by a ROM lookup table method to complete signal denoising;
step 2-2: inputting data in a bit reverse order mode, and outputting the data obtained by using a windowing FFT method according to a natural order;
the method of adopting Fourier windowing truncation is to carry out finite length truncation on a time domain signal, change the finite length truncation into a finite length discrete sequence, so that the finite length discrete sequence can be analyzed by using discrete Fourier transform, select a window function, effectively reduce the occurrence of frequency spectrum leakage effect, obtain harmonic wave components of the signal through the frequency spectrum of the signal, take out data in a bit-reversal mode after the signal is denoised, send the data into a windowing FFT module for operation, and output the sequence to be a natural sequence;
step 2-3: calculating the total harmonic distortion rate by the following method:
wherein, UnIs the effective value of the nth harmonic voltage; i isnIs the effective value of the nth harmonic current; THDuIs the voltage total harmonic distortion rate; THDiIs the current total harmonic distortion rate; u shape1And I1The effective value of the fundamental voltage and the effective value of the fundamental current are respectively;
step 2-4: and judging whether the total harmonic distortion rate exceeds a preset value, if so, switching the existing wind turbine generator 1 out of the power grid, and otherwise, ending the cycle.
Method for monitoring transient disturbance quantity
The flow of the transient disturbance amount monitoring sub-method is shown in fig. 5, and includes:
step 3-1: judging whether the voltage and current signals of the rotor and stator sides obtained in the step 2 exceed preset values, if so, cutting the fan out of a power grid, and ending the circulation of the current round, otherwise, executing the step 3-2;
step 3-2: denoising the voltage and current signal data on the stator side by using an FIR filter;
step 3-3: adopting a Hilbert-Huang transform detection algorithm HHT to analyze the data obtained in the step 3-2, wherein the HHT algorithm comprises an EMD method and a Hilbert spectrum analysis method, firstly decomposing a given signal into a plurality of intrinsic mode functions IMFs by using the EMD method, then carrying out Hilbert transform on each IMF, and obtaining characteristic parameters to obtain a Hilbert spectrum of a corresponding original signal, and the specific steps are as follows:
decomposing the empirical mode to obtain an inherent mode function group:
wherein s isi(t) is each natural modal component in the original signal; d (t) is the DC component in the original signal; r (t) is a residual function data sequence obtained by decomposition;
and then performing Hilbert transformation on all IMFs to obtain an analytic expression of each IMF of the transformation:
wherein, aiFor IMF amplitude, the above equation is a Hilbert spectrum, which can be written as:
integrating the above formula in the time domain to obtain the corresponding Hilbert marginal spectrum:
wherein the instantaneous frequency is:
step 3-4: judging whether transient disturbance including voltage swell disturbance, voltage sag disturbance and frequency fluctuation disturbance occurs, if so, executing the step 3-5, otherwise, returning to the step 3-1, and executing the next round of circulation;
step 3-5: judging whether the transient disturbance is periodic disturbance, if so, executing the step 3-6, otherwise, returning to the step 3-1, and executing the next round of circulation;
step 3-6: calculating the disturbance quantity;
step 3-7: and judging whether the disturbance quantity exceeds a grid-connected standard, if so, switching the fan out of the power grid, otherwise, returning to the step 3-1, and executing the next cycle.
Fourthly, power control sub-method
The flow of the power control sub-method is shown in fig. 6, and includes:
step 4-1: controlling the power of the unit by using a power control model;
the power control model comprises a PID sub-model based on a neural network and a coordinate transformation sub-model, and a control block diagram of the PID sub-model based on the neural network is shown in FIG. 7.
The PID submodel based on the neural network is specifically an incremental PID control algorithm which selects the neural network as a training model:
u(k)＝u(k-1)+Δu(k)
Δu(t)＝kp(e(k)-e(k-1))+kie(k)+kd(e(k)-2e(k-1)+e(k-2))
wherein u (k-1) is the state at the previous moment; u (k) is the state at the current time; k is a radical ofp、kiAnd kdProportional coefficient, integral coefficient and differential coefficient respectively;
the training by using the neural network comprises the following steps:
s1: determining the node numbers N and Q of an input layer and an implicit layer, giving initial values of weighting coefficients of all layers, selecting a learning rate eta and an inertia coefficient alpha, and setting k to be 1;
s2: calculating the error of the current moment:
e(k)＝rin(k)-yout(k)
wherein rin (k) is a desired amount; yout (k) is the amount of output;
s3: calculating the input and output of each layer of neuron of the neural network model, and selecting the final output of the neural network as kp、kiAnd kd；
S4: calculating u (k);
s5: performing neural network learning, modulating a weighting coefficient, and realizing self-adaptive adjustment of PID control parameters;
s5: setting k to k +1, returning to S1 and repeating the execution;
the 2s/2r transformation matrix of the coordinate transformation submodel is specifically as follows:
wherein theta is an included angle between an alpha axis and a d axis;
the inverse matrix of the 2s/2r transformation matrix is specifically:
step 4-2: and recalculating the active power and the reactive power, judging whether the preset threshold is met, if so, ending the circulation of the current round, otherwise, returning to the step 4-1.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An embedded system for monitoring and improving the electric energy quality of a wind turbine, which is connected with an existing wind turbine (1), is characterized in that the embedded system comprises: the device comprises a main control module (2), a signal acquisition module (3), a frequency detection module (4), a power control module (5), a communication module (6), an upper computer (7), a storage module (8) and an air speed sensor (9); one end of the signal acquisition module (3), one end of the frequency detection module (4) and one end of the power control module (5) are respectively connected with the existing wind turbine generator (1), and the other end of the signal acquisition module is respectively connected with the main control module (2); the storage module (8) and the wind speed sensor (9) are respectively connected with the main control module (2); the main control module (2) is communicated with the upper computer (7) through the communication module (6).
2. The embedded system for monitoring and improving the power quality of the wind turbine generator set according to claim 1, wherein the main control module (2) is an FPGA chip.
3. The embedded system for monitoring and improving the power quality of a wind turbine generator according to claim 1, wherein the signal acquisition module (3) comprises a voltage sensor (301), a current sensor (302), a signal conditioning circuit (303) and a voltage and current sampling circuit (304); one end of the voltage sensor (301) and one end of the current sensor (302) are respectively connected with the existing wind turbine generator (1), and the other ends of the voltage sensor and the current sensor are respectively connected with the input end of the signal conditioning circuit (303); the output end of the signal conditioning circuit (303) is connected with the input end of the voltage and current sampling circuit (304); the output end of the voltage and current sampling circuit (304) is connected with the main control module (2).
4. The embedded system for monitoring and improving the power quality of the wind turbine generator set according to claim 1, wherein the frequency detection module (4) comprises an isolation transformer (401), a filter shaping circuit (402), a schmitt trigger (403), a double-D trigger (404) and an inverter (405) which are connected in sequence; the primary side of the isolation transformer is connected with the existing wind turbine generator (1); the output end of the phase inverter (405) is connected with the main control module (2).
5. The embedded system for monitoring and improving the power quality of the wind turbine generator set according to claim 1, wherein the power control module (5) comprises a grid-side and rotor-side PWM converter (501), an SVPWM inverter control unit (502) and an IGBT drive unit (503) which are connected in sequence; the input ends of the network side and rotor side PWM converters (501) are respectively connected with the existing power grid and the generator of the existing wind turbine generator (1); the output end of the IGBT driving unit (503) is connected with the main control module (2).
6. A method for wind turbine generator power quality monitoring and improvement for an embedded system according to claim 1, comprising:
step 1: acquiring predicted wind speed by adopting a wind speed prediction sub-method according to the wind speed and wind direction samples uploaded by the main control module (2);
step 2: acquiring voltage and current signals of the rotor and stator sides of the existing wind turbine generator (1), and acquiring frequency through a frequency detection module (4);
and step 3: analyzing the quality of the electric energy output by the existing wind turbine generator (1) at the current moment by adopting a harmonic distortion analysis sub-method and a transient disturbance quantity monitoring sub-method;
and 4, step 4: acquiring rotating speed data of a generator rotor of an existing wind turbine generator (1);
and 5: calculating the power of a generator in the existing wind turbine generator (1);
step 6: judging whether the current power of the generator meets the load requirement, if so, executing a step 8, otherwise, executing a step 7;
and 7: performing power control by using a power control sub-method, and then executing the step 8;
and 8: acquiring wind speed prediction data;
and step 9: judging whether the absorption power of the unit needs to be adjusted, if so, adjusting the absorption power of the unit, and then executing the step 10, otherwise, directly executing the step 10;
step 10: and finishing the calculation of the current round, acquiring the wind speed data of the current moment again, judging whether the difference value of the wind speed and the wind speed prediction data of the previous moment is larger than a preset error, if so, returning to the step 1, and then performing the calculation of the next round, otherwise, returning to the step 2, and performing the calculation of the next round.
7. The method for monitoring and improving the power quality of the wind turbine generator as set forth in claim 6, wherein the wind speed predictor method comprises:
step 1-1: acquiring voltage and current at the output end of the generator stator side and voltage and current signals at the rotor side of the existing wind turbine generator (1) acquired by a signal acquisition module (3), and acquiring wind speed sample data acquired by a wind speed sensor (9);
step 1-2: carrying out normalization processing on the data to obtain a sample training set;
the normalization treatment specifically comprises the following steps:
wherein v isiThe original value of the wind speed sample data; v. ofmaxAnd vminRespectively a maximum value and a minimum value in the wind speed sample data; v. ofi' is the output value after wind speed normalization;
step 1-3: initializing an improved PSO algorithm;
step 1-4: calculating initial fitness, wherein the calculation method of the fitness comprises the following steps:
wherein N is the sample volume; v. ofi *The wind speed is a predicted value;
step 1-5: carrying out iteration optimization and updating the particle speed and position, wherein the specific method comprises the following steps:
wherein D is 1,2,3, …, D; 1,2,3, …, N; k is the current iteration number; v. ofidIs the current speed; c. C1And c2Is an acceleration factor, and c1And c2Are all larger than zero; r is1And r2Is a random function, and the value ranges are all [0,1 ]]；
And
respectively the ith particle in D dimensionPosition and speed of line k +1 iterations; z is a contraction factor; phi is the total acceleration factor; genThe total number of iterations; k is a shrinkage coefficient;
step 1-6: calculating fitness, and updating individual, global optimum fitness and optimal solution;
step 1-7: judging whether the fitness meets the requirement or whether the iteration times reach the maximum iteration times, if so, executing the step 1-8, otherwise, returning to the step 1-5;
step 1-8: and training the LSSVM by using the global optimal solution as a parameter of the LSSVM, so as to obtain wind speed prediction data.
8. The method for monitoring and improving the power quality of the wind turbine generator as set forth in claim 6, wherein the harmonic distortion analysis method comprises:
step 2-1: denoising voltage and current signals at the sides of the rotor and the stator by using an FIR filter;
step 2-2: inputting data in a bit reverse order mode, and outputting the data obtained by using a windowing FFT method according to a natural order;
step 2-3: calculating the total harmonic distortion rate by the following method:
wherein, UnIs the effective value of the nth harmonic voltage; i isnIs the effective value of the nth harmonic current;THDuis the voltage total harmonic distortion rate; THDiIs the current total harmonic distortion rate; u shape1Is the effective value of the fundamental voltage; i is1Is the effective value of the fundamental current;
step 2-4: and judging whether the total harmonic distortion rate exceeds a preset value, if so, cutting the existing wind turbine generator (1) into a power grid, and then ending the cycle of the current round, otherwise, directly ending the cycle of the current round.
9. The method as claimed in claim 6, wherein the transient disturbance amount monitoring sub-method comprises:
step 3-1: judging whether the voltage and current signals of the rotor and stator sides obtained in the step 2 exceed preset values, if so, cutting the fan out of a power grid, and ending the circulation of the current round, otherwise, executing the step 3-2;
step 3-2: denoising the voltage and current signal data on the stator side by using an FIR filter;
step 3-3: adopting a Hilbert-Huang transform detection algorithm HHT to analyze the data obtained in the step 3-2, wherein the HHT algorithm comprises an EMD method and a Hilbert spectrum analysis method, firstly decomposing a given signal into a plurality of intrinsic mode functions IMFs by using the EMD method, then carrying out Hilbert transform on each IMF, and obtaining characteristic parameters to obtain a Hilbert spectrum of a corresponding original signal, and the specific steps are as follows:
decomposing the empirical mode to obtain an inherent mode function group:
wherein s isi(t) is each natural modal component in the original signal; d (t) is the DC component in the original signal; r (t) is a residual function data sequence obtained by decomposition;
and then performing Hilbert transformation on all IMFs to obtain an analytic expression of each IMF of the transformation:
wherein, aiFor IMF amplitude, the above equation is a Hilbert spectrum, which can be written as:
integrating the above formula in the time domain to obtain the corresponding Hilbert marginal spectrum:
wherein the instantaneous frequency is:
step 3-4: judging whether transient disturbance including voltage swell disturbance, voltage sag disturbance, frequency fluctuation disturbance and the like occurs, if so, executing the step 3-5, otherwise, returning to the step 3-1, and executing the next round of circulation;
step 3-5: judging whether the transient disturbance is periodic disturbance, if so, executing the step 3-6, otherwise, returning to the step 3-1, and executing the next round of circulation;
step 3-6: calculating the disturbance quantity;
step 3-7: and judging whether the disturbance quantity exceeds a grid-connected standard, if so, switching the fan out of the power grid, otherwise, returning to the step 3-1, and executing the next cycle.
10. The method for monitoring and improving the quality of the electric energy of the wind turbine generator as claimed in claim 6, wherein the power control sub-method comprises:
step 4-1: controlling the power of the unit by using a power control model;
the power control model comprises a PID sub-model and a coordinate transformation sub-model based on a neural network;
the PID submodel based on the neural network is an incremental PID control algorithm which selects the neural network as a training model:
u(k)＝u(k-1)+Δu(k)
Δu(t)＝kp(e(k)-e(k-1))+kie(k)+kd(e(k)-2e(k-1)+e(k-2))
wherein u (k-1) is the state at the previous moment; u (k) is the state at the current time; k is a radical ofp、kiAnd kdProportional coefficient, integral coefficient and differential coefficient respectively;
the training by using the neural network comprises the following steps:
s1: determining the node numbers N and Q of an input layer and an implicit layer, giving initial values of weighting coefficients of all layers, selecting a learning rate eta and an inertia coefficient alpha, and setting k to be 1;
s2: calculating the error of the current moment:
e(k)＝rin(k)-yout(k)
wherein rin (k) is a desired amount; yout (k) is the amount of output;
s3: calculating the input and output of each layer of neuron of the neural network model, and selecting the final output of the neural network as kp、kiAnd kd；
S4: calculating u (k);
s5: performing neural network learning, modulating a weighting coefficient, and realizing self-adaptive adjustment of PID control parameters;
s5: setting k to k +1, returning to S1 and repeating the execution;
the 2s/2r transformation matrix of the coordinate transformation submodel is specifically as follows:
wherein theta is an included angle between an alpha axis and a d axis;
the inverse matrix of the 2s/2r transformation matrix is specifically:
step 4-2: and recalculating the active power and the reactive power, judging whether the preset threshold is met, if so, ending the circulation of the current round, otherwise, returning to the step 4-1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010714214.0A CN111856185A (en) | 2020-07-23 | 2020-07-23 | Embedded system and method for monitoring and improving electric energy quality of wind turbine generator |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010714214.0A CN111856185A (en) | 2020-07-23 | 2020-07-23 | Embedded system and method for monitoring and improving electric energy quality of wind turbine generator |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111856185A true CN111856185A (en) | 2020-10-30 |
Family
ID=72950375
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010714214.0A Pending CN111856185A (en) | 2020-07-23 | 2020-07-23 | Embedded system and method for monitoring and improving electric energy quality of wind turbine generator |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111856185A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112986744A (en) * | 2021-04-26 | 2021-06-18 | 湖南大学 | Frequency fault tolerance detection method and system under transient fault condition of power system |
| CN113902326A (en) * | 2021-10-21 | 2022-01-07 | 上海电机学院 | Biomass unit electric energy quality and unit efficiency measurement and control system based on FPGA |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102323480A (en) * | 2011-05-19 | 2012-01-18 | 西南交通大学 | A kind of power quality analysis method based on the Hilbert-Huang conversion |
| CN102522777A (en) * | 2011-12-27 | 2012-06-27 | 东方电气集团东方汽轮机有限公司 | Wind driven generator set |
| CN102664411A (en) * | 2012-03-31 | 2012-09-12 | 东北大学 | Wind power generation system with maximum power tracking and control method thereof |
| CN103400052A (en) * | 2013-08-22 | 2013-11-20 | 武汉大学 | Combined method for predicting short-term wind speed in wind power plant |
| CN103487650A (en) * | 2013-10-08 | 2014-01-01 | 水利部农村电气化研究所 | Frequency measurement device of hydroelectric generating set |
| CN105450122A (en) * | 2016-01-14 | 2016-03-30 | 重庆大学 | IGBT device junction temperature fluctuation inhibition method of doubly-fed wind turbine generator system machine-side current transformer |
| CN110197310A (en) * | 2019-06-10 | 2019-09-03 | 燕山大学 | A kind of electric charging station Optimization Scheduling based on load margin domain |
-
2020
- 2020-07-23 CN CN202010714214.0A patent/CN111856185A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102323480A (en) * | 2011-05-19 | 2012-01-18 | 西南交通大学 | A kind of power quality analysis method based on the Hilbert-Huang conversion |
| CN102522777A (en) * | 2011-12-27 | 2012-06-27 | 东方电气集团东方汽轮机有限公司 | Wind driven generator set |
| CN102664411A (en) * | 2012-03-31 | 2012-09-12 | 东北大学 | Wind power generation system with maximum power tracking and control method thereof |
| CN103400052A (en) * | 2013-08-22 | 2013-11-20 | 武汉大学 | Combined method for predicting short-term wind speed in wind power plant |
| CN103487650A (en) * | 2013-10-08 | 2014-01-01 | 水利部农村电气化研究所 | Frequency measurement device of hydroelectric generating set |
| CN105450122A (en) * | 2016-01-14 | 2016-03-30 | 重庆大学 | IGBT device junction temperature fluctuation inhibition method of doubly-fed wind turbine generator system machine-side current transformer |
| CN110197310A (en) * | 2019-06-10 | 2019-09-03 | 燕山大学 | A kind of electric charging station Optimization Scheduling based on load margin domain |
Non-Patent Citations (3)
| Title |
|---|
| 王家乐: "风力发电机组电能质量监测与改善的研究", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 纪萍, 吴静妹, 陈玲: "基于HHT 的电能质量暂态扰动信号检测技术", 《长江大学学报(自科版)》 * |
| 范曼萍，周冬: "基于改进粒子群优化LS—SVM的短期风速预测", 《电力学报》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112986744A (en) * | 2021-04-26 | 2021-06-18 | 湖南大学 | Frequency fault tolerance detection method and system under transient fault condition of power system |
| CN113902326A (en) * | 2021-10-21 | 2022-01-07 | 上海电机学院 | Biomass unit electric energy quality and unit efficiency measurement and control system based on FPGA |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111856185A (en) | Embedded system and method for monitoring and improving electric energy quality of wind turbine generator | |
| CN107769257B (en) | A kind of method for controlling frequency conversion of the photovoltaic combining inverter based on LCL filtering | |
| CN106936134B (en) | Active damping control device and control system of three-phase voltage source type current converter | |
| CN111654052B (en) | Flexible direct current converter modeling device and method based on dynamic phasor method | |
| CN112255457B (en) | Phase angle difference measuring method suitable for automatic quasi-synchronization device | |
| CN105406477A (en) | Method for parameter design of LCL filter of three-phase grid-connected system | |
| Xu et al. | Sub-synchronous frequency domain-equivalent modeling for wind farms based on rotor equivalent resistance characteristics | |
| CN111641229B (en) | Wind power generation system output monitoring method and system based on extended harmonic domain model | |
| CN117318553A (en) | Low-wind-speed permanent magnet direct-driven wind turbine control method based on TD3 and Vienna rectifier | |
| CN113078670A (en) | Method for evaluating resonance stability of receiving-end power grid under effect of hybrid cascade direct-current transmission | |
| CN103762614A (en) | Second-order internal model control method of PWM grid-connected converter current inner ring | |
| CN113241779A (en) | Stability analysis method and device for direct-drive wind power plant grid-connected system | |
| CN107359645B (en) | Zero-transition-process dynamic grid-connected system of permanent-magnet direct-drive fan | |
| CN111045329B (en) | Double-fed fan digital physical hybrid simulation method based on self-adaptive mode switching | |
| Shojaei et al. | Filters optimized tuning for wind farms reactive power calculation | |
| CN106972510A (en) | The directly driven wind-powered sub-synchronous oscillation analysis method being delayed based on net side control loop | |
| CN110244567B (en) | Rapid model prediction control method based on extended instantaneous reactive power theory | |
| CN109374970B (en) | Real-time check synchronous phasor measurement method, device, equipment and storage medium | |
| CN111884257A (en) | Direct-drive wind turbine group simulation model and data acquisition method and system thereof | |
| CN112861326A (en) | New energy power grid generator damping evaluation device and method based on measurement | |
| CN114865703B (en) | High-pass characteristic parameter identification method for direct-drive fan inverter | |
| CN114611402B (en) | Direct-drive fan dynamic fitting method based on block interconnection model and data driving | |
| Ji et al. | Improved Three-Vector-Based Model Predictive Current Control for Energy Storage Converter | |
| Kasbi et al. | Doubly fed induction generator based variable speed wind conversion system control enhancement by applying fractional order controller | |
| Zhao et al. | MATLAB Simulation Research on Harmonic Elimination Algorithm of Active Power Filter Based on Cascaded Multilevel Inverter Technology |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201030 |