CN117039926A - Wind power plant multi-cluster available inertial measurement unit and method - Google Patents
Wind power plant multi-cluster available inertial measurement unit and method Download PDFInfo
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Abstract
The invention discloses a wind farm multi-cluster available inertial measurement unit and a wind farm multi-cluster available inertial measurement unit, wherein the method comprises the following steps: taking the instantaneous wind speed, the dominant wind direction position and the relative position vertical to the dominant wind direction as clustering indexes of the wind power plant partition, searching density peak points by using a density peak clustering algorithm, and carrying out clustering division on the wind power units; describing a non-stationary wind speed time sequence by adopting an ARIMA-GARCH model, establishing a joint distribution function by utilizing a Copula function, and calculating to obtain a predicted wind speed confidence interval; and evaluating the probability of different running states of the fan by using a delayed response model based on the wind speed and the rotating speed of the fan rotor in the hidden Markov process, searching an optimal path of an observation time sequence generated by a hidden state sequence by using a Viterbi algorithm, thereby obtaining a state sequence of a fan rotating speed interval with the maximum probability, and determining the available moment of inertia of the fan rotor. According to the method, the uncertainty of the wind speed and the delay response of the wind turbine rotor are considered, and the accuracy of inertial evaluation of the wind power plant is improved.
Description
Technical Field
The invention relates to the technical field of large wind farm control, in particular to a method and a device for evaluating available inertial quantity of multiple clusters of a wind farm.
Background
In recent years, in order to cope with the shortage of energy and environmental problems, wind power generation has been rapidly developed as an important clean energy power generation mode in response to the trend of green low-carbon development. With more and more wind generating sets replacing thermal power generating sets, the problem of safe and stable operation of a power system is increasingly outstanding. Conventional synchronous motors ensure stable operation of the grid by providing the system with a usable inertia to resist frequency changes caused by disturbances. However, since the wind turbine generator is decoupled from the power grid through the current transformer, inertia cannot be directly provided for the system through internal rotating components such as the synchronous motor in the conventional control scheme. Therefore, as the permeability of wind power increases, the inertia of the system is continuously reduced, and the system stability is weakened or even crashed due to insufficient inertia. In order to enable the wind turbine to provide a certain inertial support for the system, control strategies such as virtual inertial control and the like are widely studied in the industry so as to increase the capacity of the wind turbine to participate in system frequency adjustment.
However, an accurate method for evaluating available inertia of each unit of a wind farm is not available at present, so that reference can be provided for frequency modulation output scheduling of each unit after disturbance, and theoretical support can be provided for setting of frequency modulation control strategies of the units according to available inertia reserves in a system. The traditional thermal power generating unit can directly utilize the kinetic energy of the rotor to carry out inertial support, and the support capacity evaluation of the traditional thermal power generating unit can be obtained through a rotor motion equation and unit equipment parameters. However, in combination with an inertial control strategy, an additional control link or a pre-reserve, the inertial bearing capacity of the wind turbine cannot be obtained directly from the rotor equation of motion. Most of the existing methods ignore the real-time running state and distribution differences of wind turbines in an electric field. In addition, the wind turbine rotor has an inertial response, and there is a time lag between the wind turbine rotational speed and the wind speed. The effectiveness of the evaluation result is seriously affected by the delayed response of wind resource distribution in the wind power plant, the predicted wind speed and the predicted error are in strong positive correlation. This makes it impossible for the wind turbine to provide sufficient inertial support power when participating in frequency regulation.
Disclosure of Invention
The invention aims to: in order to solve the problems of the prior art and realize effective wind farm available inertial measurement evaluation, the invention provides a wind farm multi-cluster available inertial measurement evaluation method taking the uncertainty of wind speed and the delayed response of a wind turbine rotor into consideration, and the accuracy of the inertial evaluation result of the whole wind farm is improved through wind speed modeling of ARIMA-GARCH and Copula functions and rotor speed modeling of a hidden Markov model.
The invention also provides a device, equipment and a computer storage medium for evaluating the available inertial quantity of the wind farm multi-cluster.
The technical scheme is as follows: in order to achieve the aim of the invention, the invention provides a multi-cluster available inertial quantity evaluation method for a wind farm, which comprises the following steps:
step S1, taking instantaneous wind speed, dominant wind direction position and relative position vertical to dominant wind direction as clustering indexes of wind power plant partitions, searching density peak points by using a density peak clustering algorithm, carrying out clustering division on wind power units, and enabling fans in the same cluster to be equivalent to a single wind power unit to form a plurality of clusters;
s2, describing a non-stationary wind speed time sequence with aggregation and time-varying effects by adopting an ARIMA-GARCH model, taking a predicted wind speed and a predicted error fitted by the ARIMA-GARCH model as edge distribution functions, establishing a nonlinear related joint distribution function between the two variables by utilizing a Copula function, obtaining a predicted error condition distribution function under a specified speed condition through calculation, and obtaining a predicted wind speed confidence interval of a fan through known predicted wind speed and the conditional distribution of the predicted error;
and S3, evaluating the probability of different running states of the fan by using a delayed response model based on the wind speed and the fan rotor rotating speed of the hidden Markov process, searching an optimal path of an observation time sequence generated by a hidden state sequence by using a Viterbi algorithm, thereby obtaining a state sequence of a fan rotating speed interval with the maximum probability, and calculating the available rotating inertia of the fan rotor according to the obtained fan rotating speed sequence.
The invention also provides a wind farm multi-cluster available inertial measurement unit, which comprises:
the wind power plant cluster partitioning module is configured to search density peak points by using a density peak clustering algorithm by taking the instantaneous wind speed, the dominant wind direction position and the relative position vertical to the dominant wind direction as clustering indexes of wind power plant partitions, and perform clustering partition on wind power units, wherein fans in the same cluster are equivalent to a single wind power unit to form a plurality of clusters;
the cluster fan modeling module is configured to describe a non-stationary wind speed time sequence with aggregation and time-varying effects by adopting an ARIMA-GARCH model, take the predicted wind speed and the predicted error fitted by the ARIMA-GARCH model as edge distribution functions, establish a nonlinear related joint distribution function between the two variables by utilizing a Copula function, obtain a predicted error condition distribution function under a specified speed condition through calculation, and obtain a predicted wind speed confidence interval of the fan through known predicted wind speed and the conditional distribution of the predicted error;
the fan available inertia evaluation module is configured to evaluate the probability of different running states of the fan by using a delayed response model based on the wind speed and the fan rotor rotating speed of the hidden Markov process, search an optimal path of an observation time sequence generated by a hidden state sequence by using a Viterbi algorithm, so as to obtain a state sequence of a fan rotating speed interval with the highest probability, and calculate the fan rotor available rotating inertia according to the obtained fan rotating speed sequence.
The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps of the wind farm multi-cluster available inertial measurement method as described above.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a method of wind farm multi-cluster available inertial measurement unit assessment as described above.
The beneficial effects are that:
1. according to the method, aiming at the space-time distribution characteristics of the wind turbines, the density peak clustering algorithm is utilized to perform clustering partition on the units of the wind power plant, the influence of the flow tail effect in different time periods is considered, the instantaneous wind speed, the dominant wind direction position and the wind turbine relative position perpendicular to the dominant wind direction are selected as clustering indexes of wind power plant partitioning, and the evaluation calculation complexity is reduced.
2. According to the invention, ARIMA-GARCH (autoregressive integral moving average-generalized autoregressive conditional heteroscedastic) and Copula functions are adopted to model the distribution characteristics and prediction errors of wind turbine predicted wind speed, so that the influence of excessive errors on wind speed prediction is avoided, and the accuracy of wind speed prediction is improved.
3. The invention provides a wind speed and rotor speed delay response model based on a hidden Markov model, and an operation state sequence with the maximum probability of the wind turbine is obtained, so that the accuracy of an inertia evaluation result is improved.
Drawings
FIG. 1 is a flow chart of a method for multi-cluster available inertial measurement unit assessment of a wind farm;
FIG. 2 is a decision diagram intent of a density peak clustering algorithm;
FIG. 3 is a schematic illustration of a Markov chain transfer process for wind turbine rotational speed;
FIG. 4 is a density peak clustering decision graph and clustering results;
FIG. 5 is a graph of a fifth fan prediction error fit for an example of the present invention;
FIG. 6 is a predicted wind speed for a 95% confidence interval for fan number five of an example of the present invention;
FIG. 7 is a fan rotor speed condition within a 95% confidence interval for fan number five of the present example;
FIG. 8 is an estimate of the available inertial energy of a wind farm of an example of the invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, the method for evaluating available inertial quantity of multiple clusters in a wind farm provided by the invention comprises the following steps:
step S1, taking instantaneous wind speed, dominant wind direction position and relative position vertical to dominant wind direction as clustering indexes of wind power plant partitions, searching density peak points by using a density peak clustering algorithm, carrying out clustering division on wind power units, and enabling fans in the same cluster to be equivalent to a single wind power unit to form a plurality of clusters;
s2, describing a non-stationary wind speed time sequence with aggregation and time-varying effects by adopting an ARIMA-GARCH model, taking a predicted wind speed and a predicted error fitted by the ARIMA-GARCH model as edge distribution functions, establishing a nonlinear related joint distribution function between the two variables by utilizing a Copula function, obtaining a predicted error condition distribution function under a specified speed condition through calculation, and obtaining a predicted wind speed confidence interval of a fan through known predicted wind speed and the conditional distribution of the predicted error;
and S3, evaluating the probability of different running states of the fan by using a delayed response model based on the wind speed and the fan rotor rotating speed of the hidden Markov process, searching an optimal path of an observation time sequence generated by a hidden state sequence by using a Viterbi algorithm, thereby obtaining a state sequence of a fan rotating speed interval with the maximum probability, and calculating the available rotating inertia of the fan rotor according to the obtained fan rotating speed sequence.
A detailed description is given below of specific implementation of each step. In the following description, wind turbines and fans are referred to as fan units, which may be used interchangeably. Inertia, may be used interchangeably herein.
In step S1, in order to establish speed models of different wind turbines in a wind power plant, the invention provides a clustering division method based on density peaks in consideration of space-time distribution characteristics of the wind turbines. Because the wind turbines with different geographic positions and vertical heights are obviously different in running states due to the influence of the flow tail effect in different time periods, the relative positions of the fans in the main wind direction, the relative positions of the fans in the direction perpendicular to the main wind direction and the instantaneous wind speed are selected as clustering indexes of the wind power plant partitions.
The density peak clustering algorithm (Density peak clustering, DPC) can automatically discover cluster centers and achieve efficient clustering of different types of data by quickly searching for density peak points. According to clustering index data of fans in a wind power plant, calculating local density and clustering center distance of each data point through a DPC algorithm. Definition of the cutoff distance d c To control the size of the cluster partition, then the local density ρ i Indicating that the Euclidean distance with the ith fan is smaller than the cut-off distance d c Fan number, cluster center distance delta i Representing the minimum value of the distance between fan i and the fan with the higher local density. When the DPC algorithm is used for clustering, the local density of the clustering center points is larger than that of surrounding neighbor points, and meanwhile, the distance between the clustering center points and the points with higher density is relatively larger.
Local density ρ i Is defined as follows:
wherein n is the number of fans of the wind power plant; gamma (x) is an indication function, gamma (x) =1 when x is not less than 0, and gamma (x) =0 when x is less than 0; d, d ij (i, j=1, 2, … …, n) is the euclidean distance between fan i and fan j, defined as:
wherein x is ik 、x jk (k=1, 2,3, indicating the number of cluster indices) is the kth cluster index data value of fan i and fan j. From the above, only the truncated distance parameter d needs to be defined in the DPC algorithm c And control falling to d c The average data points in the range area can realize the effective clustering of the wind turbine generator, and especially under the condition of numerous wind turbine generator sets in the wind power plant, the efficiency drop caused by repeated iteration is avoidedLow problems.
Cluster center distance delta between fan i and fan j with local density greater than it and nearest to it i The definition is as follows:
and for the fan with the largest local density, the cluster center distance is defined as:
when DPC algorithm is adopted to perform clustering division, the local density of the clustering center is larger than that of the adjacent points, and the distance between the clustering center and the points with higher density is larger. Local density ρ of each fan according to the above definition i And cluster center distance delta i A decision diagram for each wind turbine may be drawn, as shown in fig. 2. In the decision diagram ρ i And delta i The larger points are considered to be density peak points, which are generally located at the upper right. And taking the density peak point as the center of each cluster, and dividing other data points into clusters closest to the density peak point according to Euclidean distance. For some delta i Larger, ρ i Relatively small outliers, which can be considered noise points, form a single cluster.
Since DPC algorithms require normalization of data during computation, the contribution of each index to euclidean distance is the same. However, in actual wind farm clustering, different types of indexes have significant differences in the influence of clustering results. Therefore, subjective weights of the clustering indexes can be redistributed by adopting an Analytic Hierarchy Process (AHP) and consistency test, the rationality of weight distribution is ensured, and the influence of different dimension indexes on the clustering result can be reflected. Fans in the same cluster (cluster) may be equivalent to a single wind park. By taking the average value of all the units in the cluster, the position coordinates and the instantaneous rotating speed of the equivalent units can be obtained, so that a multi-cluster wind power plant is formed.
In step S2, the invention models the distribution characteristics and the prediction errors of the wind turbine predicted wind speed by adopting ARIMA-GARCH (autoregressive integral moving average-generalized autoregressive conditional heteroscedastic) and Copula functions, and tries to avoid the influence of excessive errors on wind speed prediction.
The invention uses the ARIMA-GARCH model to describe a time series of non-stationary wind speeds with aggregation and time varying effects. Since the time series of wind speed prediction errors is generally non-stationary, d-order differentiation should be performed first, and then an ARIMA (p, d, q) model as shown in equation (5) should be built for the differentiated stationary time series:
wherein y is t For the interpreted variables, representing the prediction error value, μ is a constant, p and q are the order of the autoregressive and moving average models, γ, respectively i 、θ i Coefficients, ε, of the autoregressive and moving average terms, respectively t Is a disturbance item which is independently and uniformly distributed. And (3) fitting the historical predicted value of the wind speed and the corresponding predicted error by using an ARIMA model shown in the formula (5) to obtain the edge distribution characteristic of the predicted error. Considering prediction error sample data for different time periods, the disturbance term ε in equation (5) t And an explanatory variable gamma t There is a correlation whose variance is no longer constant but time-varying, so a GARCH model is introduced to describe the disturbance term epsilon t Thereby solving the problem of time sequence variation of disturbance term variance in ARIMA model. Taking the formula (5) as a conditional mean model of the GARCH model, the GARCH model expression is as follows:
wherein,variance of disturbance term of formula (5), α i ,β i Autoregressive of disturbance termCoefficients and variance terms autoregressive coefficients. The GARCH model is developed by fitting the historical conditional variance +.>And disturbance sequence square term->And performing linear combination to obtain the conditional variance of the disturbance term at the current moment.
Further, the predicted wind speed and the predicted error fitted by the ARIMA-GARCH model are used as edge distribution functions, and a Copula function is utilized to establish a joint distribution function of nonlinear correlation between the two variables. The Copula function is defined as:
F(x 1 ,x 2 ,…,x n )＝C(F 1 (x 1 ),F 2 (x 2 ),…,F n (x n )) (7)
wherein F (x) 1 ,x 2 ,…,x n ) For a joint distribution function of n variables, F 1 (x 1 ),F 2 (x 2 ),…,F n (x n ) For the edge distribution function of each variable, C (-) is Copula probability distribution function, f (x) 1 ,x 2 ,…,x n ) Is an n-element joint probability density function, c (·) is a Copula probability density function, f i (x i ) As variable x i Is a function of the edge density of the substrate. The joint distribution of the predicted wind speed and the prediction error can be expressed as:
wherein v representsThe predicted wind speed, e represents the prediction error,the correlation coefficient of the Copula function can be calculated by a maximum likelihood method.
After the Copula function is adopted to establish the combined distribution of two variables with strong correlation of the predicted wind speed and the predicted error, the velocity v=v can be obtained through calculation i A conditional distribution function of prediction error under conditions, where v i Represents any wind speed:
the wind speed v=v will be predicted i The wind speed v can be obtained by bringing the wind energy into the wind velocity v 11 i And (3) obtaining an accurate predicted wind speed value by using the predicted wind speed-predicted error. The predicted wind speed confidence interval of the fan can be obtained through known predicted wind speed and the conditional distribution of the prediction error:
wherein,representing the predicted wind speed interval of the equivalent fan j at time i, < > in->Representing the upper boundary of the interval>Representing the lower boundary of the interval.
In step S3, the operation states of the wind turbine include a start-up region, an MPPT (maximum power point tracking) region, a constant rotation speed region, and a constant power region according to the instantaneous wind speed division. The fan is firstly started from a stop state, and the wind speed reaches the cut-in wind speed v 0 The corresponding rotor speed is omega 0 . As wind speed increases to a minimum v at which grid connection is possible min The rotation speed of the rotor reaches omega min . Then the fan enters an MPPT area, and the maximum wind energy utilization rate is realized through the operation of a power tracking curve, and the wind speed is in [ v ] min ，v c ) Within the interval. When the wind speed reaches v c At the time, the rotor speed is omega c The fan operates in a constant speed mode. When the wind speed increases to rated v n When the rotor reaches the maximum rotation speed omega n The fan is in a constant power area. When the wind speed is greater than the cut-out wind speed v max And when the fan stops running. However, due to the inertial delay response of the wind turbine rotor, the rotor speed cannot respond in time as the wind speed changes. That is, the actual rotor speed is not only related to the current instantaneous wind speed, but is also affected by the historical wind speed.
The invention utilizes a delayed response model based on the wind speed and the rotation speed of the fan rotor of the hidden Markov process (Hidden Markov Chain, HHM) to evaluate the probability of different running states of the fan. The hidden Markov model is a time sequence statistical analysis model and consists of a Markov process and a general stochastic process. The markov process describes a state transition relationship, and if the state at time t+1 is related to the state at time t only, then the stochastic process is referred to as the markov process. Let s= { S 1 ,s 2 ,…,s n If the state set is a set of fan running state sequences, the state set of all possible values of S is called a state space X= { χ 1 ,χ 2 ,…,χ N }. The subscript N indicates that N states are included in this set of fan operating state spaces. If the state space X satisfies:
P(S n+1 ＝χ n+1 |S i ＝χ j ,i＝1,2,…,n；j＝1,2,…,N)＝P(S n+1 ＝χ n+1 |S n ＝χ j ) (13)
s is a markov chain that takes on the value of X in the state space, where P (x|y) represents the probability that X will occur with the current fan operating state being Y. In the wind speed-fan rotational speed hysteresis modeling process of the wind farm, a state space X of rotational speed operation conditions comprises a starting region (χ 1 ) MPPT area(χ 2 ) Constant rotation speed region (χ) 3 ) And constant power region (χ) 4 ) The transition process between the different states is shown in fig. 3.
The hidden markov process, which consists of a markov chain and a generally random process, estimates the distribution of unobservable system states (i.e., hidden states) from the observed data sequence. In the hidden markov model, the state transition matrix a= [ a ] ij ]Representing the transition probability from state i to state j, transition probability matrix b= [ B ] i (h)]Represented in state x=x i Time observation state v=o h Is the initial state probability matrix pi= [ pi = [ pi ] i ]Representing the probability that the initial state is i.
According to different intervals of the fan rotating speed operation, a corresponding wind speed interval O= { O can be obtained 1 ,o 2 ,o 3 ,o 4 ,o 5 ,o 6 O, where o 1 The wind speed is in a section (0, v) smaller than the cut-in wind speed 0 ) At this time, the fan is not connected to the grid for power generation; o (o) 2 Indicating the wind speed at [ v ] 0 ,v min ) In the interval, the rotating speed of the fan is in a starting area; o (o) 3 Indicating the wind speed at [ v ] min ,v c ) In the interval, the fan works in the MPPT stage; o (o) 4 Indicating the wind speed at [ v ] c ,v n ) Within the interval o 5 Indicating that the wind speed reaches the rated wind speed v n But does not reach the interval of the wind speed cut by the fan, the fan rotor works in a constant rotating speed area and a constant power area respectively; o (o) 6 Showing a wind speed higher than the cut-out wind speed, i.e. interval v n , + -infinity a) of the above-mentioned components, at this point the fan will cease to operate. According to historical statistical state transition data, a state transition matrix and a transition probability matrix in a fan rotor rotating speed delay response model based on the HMM are obtained, wherein the state transition matrix and the transition probability matrix are shown in a formula (14):
wherein A is a state transition matrix of a fan rotor operation interval (or called a fan rotating speed operation interval), and a ij (i=1, 2,3,4; j=1, 2,3, 4) means that the fan rotor operation interval is defined byThe transition probability from state i to state j, B represents the dependency relationship between the observed state (wind speed interval of wind farm) and the hidden state (rotating speed operation interval of fan), namely the transition probability matrix, B i (o h ) (i=1, 2,3,4; h=1, 2,3,4,5, 6) indicates that the fan speed interval is in state χ i The wind speed interval is in state o h Is a probability of (2). Meanwhile, an initial state probability matrix can be obtained according to historical data, and the initial state in the HMM refers to probability distribution of each hidden state (namely fan operation working condition) of the system when time step t=1. This probability distribution represents the likelihood that the system will be in each hidden state at the beginning. Wherein pi (i) represents that the system is in state χ at the initial time i Is a probability of (2).
Further, a Viterbi algorithm (Viterbi Algorithm) is adopted to find an optimal path for generating an observation time sequence from the hidden state sequence, so that a state sequence of a fan rotating speed interval with the highest probability is obtained. According to the dynamic programming principle, if the optimal path passes through a certain observation state o at the time t j Then for the slave initial state v 1 To the end point v n For all possible paths, through the observation state o j Is necessarily optimal. For this purpose, two intermediate variables δ are defined t (i) Sum phi t (i) Wherein delta t (i) At the time t, the fan rotating speed interval state is χ i Probability maximum value, ψ, of all individual paths of (a) t (i) The state corresponding to the fan rotating speed interval at the previous moment (namely the moment t-1) when the maximum probability path is obtained is represented, and the expressions are as follows:
to obtain the optimal path with the highest probability, delta is needed t (i) And performing a plurality of iterations, wherein the iteration formula is as follows:
from equation (15) and equation (16), from the initial time t=1 of the wind speed sequence, a probability value of each evolution path (i.e., fan rotor rotation speed mode) can be obtained through recursive iterative calculation, and the path with the maximum probability value is the optimal solution. The modal sequence of the rotor speed of the wind turbine is:
in the formula (17), pi is the maximum probability value in the section search path of the fan rotor, and arg (·) represents the section state where the fan rotating speed is obtained by taking the corresponding independent variable. And carrying out HMM modeling on the delayed response of the wind speed and the rotating speed of the fan, and carrying out Viterbi algorithm search, and obtaining the state of the probability maximum operation interval where the rotating speed of the fan is located according to the predicted wind speed interval sequence.
The support inertia provided by a wind power plant is related to the mode of operation and control strategy. Since the rotor speed limit is minimal, the fan rotor usable moment of inertia E can be expressed as:
wherein J is the rotational inertia omega of the fan rotor r Is the actual rotation speed omega of the fan rotor min The lowest rotation speed of the fan participating in inertia support is provided.
When the fan operates in the MPPT area, if the variable pitch angle control is not adopted, the conversion efficiency of wind energy is as follows:
wherein omega t The rotation angular velocity of the fan impeller is represented, R is the radius of the fan blade, v represents the current wind speed, C p The wind energy conversion rate is related to the rotation angular velocity omega t And a nonlinear function of wind speed v. According to aerodynamics, the maximum power P captured by the wind turbine generator set at the moment opt Expressed as:
wherein ρ is air density, and S is wind sweeping area. Lambda (lambda) opt Is the optimal tip speed ratio which is equal to the rotation angular velocity omega t Variable related to wind speed v, when fan is operated in MPPT zone, beta=0, lambda opt Can be calculated from the following formula:
as can be seen from formulas (19) and (20), if the fan adopts overspeed control to realize load shedding D in the MPPT region, the power of the wind turbine is as follows:
the relation between the power of the wind turbine and the rotating speed of the fan can be further obtained:
wherein omega dload Representing the rotor speed when the fan is running down, C p_de C for the wind energy utilization coefficient during load shedding p_de Obtained by the following formula:
therefore, the energy that wind turbine generator system operation can use inertial support when MPPT region is:
when the fan operates in the constant speed region, the rotation speed of the rotor is constantω c However, as the wind speed increases, the rotation speed of the fan rotor still has a small range of variation, and according to the fan power curve relationship, the approximate linearization rotation speed expression at the moment can be obtained as follows:
the energy of the fan inertia support in the constant rotation speed area is as follows:
when the wind speed is further increased and the fan enters the constant power zone, the rotating speed of the rotor reaches the maximum rotating speed omega n The energy of the available inertial support of the fan at this time is:
the available inertia of the wind turbine generator is changed along with the change of the operation working condition, in a starting area, the fan cannot contribute to the system inertia support due to the minimum speed limit and the protection mechanism, in an MPPT area, a constant rotation speed area and a constant power area, the fan can utilize the kinetic energy of a rotor or provide power for standby to provide a certain inertia support for the system, and the energy of the available inertia support is respectively expressed as:
the superscript k denotes the kth cluster.
In order to verify the performance and effectiveness of the multi-cluster available inertial measurement unit assessment method, taking a certain wind farm in China as an example, the actual measurement data of 42 fans of the wind farm are utilized to carry out verification experiments by using MATLAB/Simulink on a computer with an Intel (R) Core (TM) i5-9400F (2.90 GHz) processor and 8gb memory. The radius of the fan blade is 34m, the cut-in wind speed is 3m/s, the cut-out wind speed is 25m/s, the rated wind speed is 13m/s, the rated voltage is 690V, the rated power is 2WM, the inertia time constant is 0.54s, and the rated rotor rotating speed is 17.5RPM.
Firstly, five clustering partitions can be obtained according to a density peak clustering decision diagram of fans in a wind power plant, which is shown in fig. 4, and each partition is respectively equivalent to one fan to obtain five fans. And fitting the wind speeds of the five fans and the prediction error sequence or the first-order difference sequence by using an ARIMA model. And then carrying out ARCH effect test on the established ARIMA model by considering fluctuation aggregation in the wind speed prediction error, describing disturbance items in a time sequence, and fitting a No. five fan prediction error result for the ARIMA-GARCH model in FIG. 5. And describing the joint distribution of the predicted wind speed and the predicted error by using a Copula function, and obtaining a conditional distribution function of the wind speed predicted error at a certain wind speed through a formula (10) and a formula (11). The predicted wind speed within the 95% confidence interval (for example, wind turbine number 5) is shown in fig. 6. It can be seen that the wind speed predicted by the gummel-Copula function fits well with the actual wind speed with little fluctuation, and the error is guaranteed to be within the 95% confidence interval. Modeling a delayed response between wind speed and rotor speed by using a hidden Markov model, and finding an optimal path for generating an observation time sequence from a hidden state sequence by using a Viterbi algorithm. The historical sequence with the maximum probability of the running state of the No. 5 equivalent unit water turbine is shown in fig. 7. As can be seen from fig. 7, the running state of the rotor of the wind turbine is consistent with the change rule of the wind speed in fig. 5. In the early and late stages, the wind speed is relatively low and the fan is mainly operated in the MPPT mode. In the middle of the higher average wind speed, fans tend to run at constant speed or constant power. The wind speed drops from 10 meters/second to about 2 meters/second for 3 months and 6 am and then suddenly increases to a higher speed value. If the delayed response between wind speed and turbine rotor speed is not considered, then the wind speed during this period is below the grid-tie speed threshold, and the turbine rotor is considered to be in a start-up or shut-down state. However, because of the time lag response of the wind turbine rotor, the rotor speed cannot be suddenly changed. As can be seen from fig. 7, the rotor speed is still operating in the MPPT mode, although the wind speed is relatively low. Thus, the system can still efficiently serve its inertial support purpose with the available inertial power provided by the rotor speed. Finally, the available inertial quantity of each fan is then evaluated by the formula (29), and the total available inertial quantity of all equivalent wind turbines is shown in fig. 8. From the result, the total effective inertia is basically consistent with the wind speed change trend, which indicates that the wind speed has a larger influence on the output power and the inertial supporting capability of the wind farm. Furthermore, it can be seen from the partial enlarged view of fig. 8 that the upper boundary of the available inertia, which is indicated by the black dashed line, sometimes falls below the actual value, because the wind speed is now large, and that part of the fans have been shut down, taking into account the maximum operating wind speed, resulting in a lower upper boundary. As can be seen from fig. 8, the regulator can determine an optimal frequency modulation control scheme according to the real-time running condition of the power grid and the available inertial evaluation results of the wind farm at different confidence levels.
Based on the same technical concept as the method embodiment, the invention also provides a wind farm multi-cluster available inertial measurement unit, which comprises:
the wind power plant cluster partitioning module is configured to search density peak points by using a density peak clustering algorithm by taking the instantaneous wind speed, the dominant wind direction position and the relative position vertical to the dominant wind direction as clustering indexes of wind power plant partitions, and perform clustering partition on wind power units, wherein fans in the same cluster are equivalent to a single wind power unit to form a plurality of clusters;
the cluster fan modeling module is configured to describe a non-stationary wind speed time sequence with aggregation and time-varying effects by adopting an ARIMA-GARCH model, take the predicted wind speed and the predicted error fitted by the ARIMA-GARCH model as edge distribution functions, establish a nonlinear related joint distribution function between the two variables by utilizing a Copula function, obtain a predicted error condition distribution function under a specified speed condition through calculation, and obtain a predicted wind speed confidence interval of the fan through known predicted wind speed and the conditional distribution of the predicted error;
the fan available inertia evaluation module is configured to evaluate the probability of different running states of the fan by using a delayed response model based on the wind speed and the fan rotor rotating speed of the hidden Markov process, search an optimal path of an observation time sequence generated by a hidden state sequence by using a Viterbi algorithm, so as to obtain a state sequence of a fan rotating speed interval with the highest probability, and calculate the fan rotor available rotating inertia according to the obtained fan rotating speed sequence.
It should be understood that the multi-cluster available inertial measurement unit for a wind farm in the embodiment of the present invention may implement all the technical solutions in the above method embodiments, and the functions of each functional module may be specifically implemented according to the methods in the above method embodiments, and the specific implementation process may refer to the relevant descriptions in the above embodiments, which are not repeated herein.
The present invention also provides a computer device comprising: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processors implement the steps of the wind farm multi-cluster available inertial measurement method as described above.
The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a method of wind farm multi-cluster available inertial measurement unit assessment as described above.
It will be appreciated by those skilled in the art that embodiments of the invention may be provided as a method, apparatus, computer device, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention is described with reference to flow charts of methods according to embodiments of the invention. It will be understood that each flow in the flowchart, and combinations of flows in the flowchart, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows.
Claims (10)
1. The multi-cluster available inertial measurement unit of the wind farm is characterized by comprising the following steps of:
taking the instantaneous wind speed, the dominant wind direction position and the relative position vertical to the dominant wind direction as clustering indexes of wind power plant partitions, searching density peak points by using a density peak clustering algorithm, carrying out clustering division on wind power units, and enabling fans in the same cluster to be equivalent to a single wind power unit to form a plurality of clusters;
describing a non-stationary wind speed time sequence with aggregation and time-varying effects by adopting an ARIMA-GARCH model, taking the predicted wind speed and the predicted error fitted by the ARIMA-GARCH model as edge distribution functions, establishing a nonlinear related joint distribution function between the two variables by utilizing a Copula function, obtaining a predicted error condition distribution function under a specified speed condition through calculation, and obtaining a predicted wind speed confidence interval of the fan through known predicted wind speed and the conditional distribution of the predicted error;
and evaluating the probability of different running states of the fan by using a delayed response model based on the wind speed and the fan rotor rotating speed of the hidden Markov process, searching an optimal path of an observation time sequence generated by the hidden state sequence by using a Viterbi algorithm, thereby obtaining a state sequence of a fan rotating speed interval with the maximum probability, and calculating the available moment of inertia of the fan rotor according to the obtained fan rotating speed sequence.
2. The method of claim 1, wherein searching for density peak points using a density peak clustering algorithm, the clustering of wind turbines comprising:
calculating the local density and the cluster center distance of each data point through a density peak clustering algorithm, wherein the local density rho i Indicating that the Euclidean distance with the ith fan is smaller than the cut-off distance d c Fan number, cluster center distance delta i A minimum value representing the distance between the fan i and the fan with higher local density;
according to ρ i And delta i Acquiring peak density points, taking the peak density points as the center of each cluster, and dividing other data points into clusters closest to the peak density points according to Euclidean distance; outliers are considered noise points, forming a single cluster.
3. The method of claim 1, wherein the ARIMA-GARCH model fitting the predicted wind speed and the prediction error comprises:
using ARIMA modelsFitting the historical predicted value of wind speed and the corresponding prediction error, wherein y t For the interpreted variables, representing the prediction error value, μ is a constant, p and q are the order of the autoregressive and moving average models, γ, respectively i 、θ i Coefficients, ε, of the autoregressive and moving average terms, respectively t For independent and equidistributed disturbance items, a GARCH model is adopted to describe disturbance item epsilon t Is expressed as:
wherein the method comprises the steps ofFor disturbance term epsilon in ARIMA model t Variance of alpha i ,β i The disturbance term autoregressive coefficients and the variance term autoregressive coefficients are respectively.
4. A method according to claim 3, wherein the Copula function establishes a joint distribution function of nonlinear correlations between two variables comprising:
wherein F (v, e) represents the joint distribution function of the variables v and e, C (·) is a Copula function, F (v) and F (e) represent the edge distribution functions of the variables v and e, respectively, v represents the predicted wind speed, e represents the prediction error,for the Copula function correlation coefficient, f (v, e) is the joint probability density function, c (·) is the Copula probability density function, and f (v) and f (e) are the edge density functions of variables v and e, respectively.
5. The method of claim 1, wherein the delayed response model of wind speed and fan rotor speed based on a hidden markov process is expressed as:
let s= { S 1 ,s 2 ,…,s n If the state set is a set of fan running state sequences, the state set of all possible values of S is called a state space X= { χ 1 ,χ 2 ,…,χ N -if state space X satisfies: p (S) n+1 ＝χ n+1 |S i ＝χ j ,i＝1,2,…,n；j＝1,2,…,N)＝P(S n+1 ＝χ n+1 |S n ＝χ j ) S is a Markov chain which takes a value in a state space X, wherein P (XY) represents the probability of X under the condition that the current fan running state is Y, and the state space X of the rotating speed running working condition comprises a starting region (χ) in the wind speed-fan rotating speed hysteresis modeling process of the wind power plant 1 ) MPPT region (χ) 2 ) Constant rotation speed region (χ) 3 ) And constant power region (χ) 4 )；
According to the historical statistical state transition data, a state transition matrix A of a fan rotor operation interval and a transition probability matrix B between a wind speed interval of a wind power plant and a fan rotating speed operation interval are obtained as follows:
wherein a is ij (i=1, 2,3,4; j=1, 2,3, 4) represents the probability of transition of the operating interval of the fan rotor from state i to state j, b i (o h ) (i=1, 2,3,4; h=1, 2,3,4,5, 6) indicates that the fan speed interval is in state χ i The wind speed interval is in state o h Wherein the correspondence between the fan rotation speed interval and the wind speed interval is expressed as o= { O 1 ,o 2 ,o 3 ,o 4 ,o 5 ,o 6 O, where o 1 The wind speed is in a section (0, v) smaller than the cut-in wind speed 0 ) At this time, the fan is not connected to the grid for power generation; o (o) 2 Indicating the wind speed at [ v ] 0 ,v min ) In the interval, the rotating speed of the fan is in a starting area; o (o) 3 Indicating the wind speed at [ v ] min ,v c ) In the interval, the fan works in the MPPT stage; o (o) 4 Indicating the wind speed at [ v ] c ,v n ) Within the interval o 5 Indicating that the wind speed reaches the rated wind speed v n But does not reach the interval of the wind speed cut by the fan, the fan rotor works in a constant rotating speed area and a constant power area respectively; o (o) 6 Showing a wind speed higher than the cut-out wind speed, i.e. interval v n , + -infinity a) of the above-mentioned components, at this point the fan will cease to operate.
6. The method of claim 5, wherein finding an optimal path for generating an observation time series from a hidden state series using a viterbi algorithm comprises:
defining two intermediate variables delta t (i) Sum phi t (i) Wherein delta t (i) At the time t, the fan rotating speed interval state is χ i Probability maximum value, ψ, of all individual paths of (a) t (i) The corresponding states of the fan rotating speed interval at the previous moment when the maximum probability path is obtained are represented as follows:
for delta t (i) And performing multiple iterations to obtain an optimal path with the highest probability, wherein the iteration formula is as follows:
the path with the maximum probability value is the optimal solution, and the modal sequence of the rotating speed of the rotor of the wind turbine is as follows:
and n is the probability maximum value in the interval search path of the fan rotor, and arg (·) represents the interval state where the fan rotating speed is obtained by taking the corresponding independent variable.
7. The method of claim 1, wherein calculating a usable moment of inertia of the fan rotor comprises:
under the working condition that the starting area and the wind speed are larger than the cut-out wind speed, the fan cannot contribute to the inertia support of the system, and in the MPPT area, the constant rotation speed area and the constant power area, the fan provides a certain inertia support for the system, and the energy of the available inertia support is respectively expressed as:
ω n is the maximum rotation speed omega of the fan rotor c For the constant rotation speed omega of the rotor when the fan operates in the constant rotation speed zone min For the lowest rotating speed of the fan participating in inertia support, J is the rotating inertia of a fan rotor, f (omega) represents a relation function of the power of the wind turbine generator and the rotating speed of the fan, and the upper mark k represents the kth cluster.
8. A wind farm multi-cluster available inertial measurement unit, comprising:
the wind power plant cluster partitioning module is configured to search density peak points by using a density peak clustering algorithm by taking the instantaneous wind speed, the dominant wind direction position and the relative position vertical to the dominant wind direction as clustering indexes of wind power plant partitions, and perform clustering partition on wind power units, wherein fans in the same cluster are equivalent to a single wind power unit to form a plurality of clusters;
the cluster fan modeling module is configured to describe a non-stationary wind speed time sequence with aggregation and time-varying effects by adopting an ARIMA-GARCH model, take the predicted wind speed and the predicted error fitted by the ARIMA-GARCH model as edge distribution functions, establish a nonlinear related joint distribution function between the two variables by utilizing a Copula function, obtain a predicted error condition distribution function under a specified speed condition through calculation, and obtain a predicted wind speed confidence interval of the fan through known predicted wind speed and the conditional distribution of the predicted error;
the fan available inertia evaluation module is configured to evaluate the probability of different running states of the fan by using a delayed response model based on the wind speed and the fan rotor rotating speed of the hidden Markov process, search an optimal path of an observation time sequence generated by a hidden state sequence by using a Viterbi algorithm, so as to obtain a state sequence of a fan rotating speed interval with the highest probability, and calculate the fan rotor available rotating inertia according to the obtained fan rotating speed sequence.
9. A computer device, comprising:
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, which when executed by the processor implement the steps of the wind farm multi-cluster availability inertial measurement method of any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method for multi-cluster availability inertial measurement of a wind farm according to any of claims 1-7.
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| CN117932232B (en) * | 2024-03-21 | 2024-05-28 | 南京信息工程大学 | Wind speed prediction system based on state identification RIME-DELM multivariable time sequence prediction |
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