A method and system for judging the influence of wind farm group output on power system operation

Abstract

The present application relates to a kind of wind farm group output influence on power system service judging method and system, the method includes: the operation data of wind farm group in identification period is processed to abnormal data, and the output sequence of wind farm group in identification period is obtained;The output sequence of wind farm group in the identification period is Z transformed;The output sequence of wind farm group in the identification period after Z transformation is sampled, and according to sampling point, the influence of wind farm group output on power system peak shaving and frequency modulation service in identification period is judged.The technical scheme provided by the present application is based on linear frequency modulation Z transformation, processes the output convergence data of wind farm group, and is sampled, according to the sensitive frequency band where sampling point is located, judges the influence of wind farm group output on power system service.The present application can be applied to the fluctuation analysis of large-scale wind farm power output convergence data, and provides technical support for its research on the influence on power system peak shaving and frequency modulation.

Description

Technical Field

[0001] The present invention belongs to the field of business impact of wind power output, and specifically relates to a method and system for judging the impact of wind farm group output on power system business. Background Art

[0002] The large-scale development and utilization of renewable energy is a crucial way to alleviate the global fossil energy crisis and reduce environmental pollution. Wind energy, a clean energy source with commercial potential, has seen its share of power systems steadily increase in recent years. In the future, traditional synchronous generators, primarily based on thermal power, will be replaced by wind turbines, resulting in a high wind power penetration rate in power grids.

[0003] Due to the randomness and volatility of wind turbine output power, when the wind power penetration rate in the system increases, the impact of wind turbine output power fluctuations on system frequency stability cannot be ignored, which in turn affects the power system business. Wind turbine output power fluctuations are different from traditional step-type power disturbances. Their impact on system frequency characteristics depends not only on the amplitude of the power fluctuations, but also on the frequency of the power fluctuations. When the power fluctuation frequency is near the system's natural frequency, the system frequency response is more intense. This patent proposes a method and system for determining the impact of wind farm group output on power system business.

[0004] Studying the impact of wind power output on system frequency in high-wind power penetration systems can help summarize the frequency response mechanisms of complex power systems and provide corresponding data support for smoothing wind power fluctuations in the system. This is of great significance for ensuring the safe and stable operation of future power systems with high wind power penetration. Therefore, it is necessary to provide a method and system for determining the impact of wind farm group output on power system operations. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of the present invention is to provide a method and system for judging the impact of wind farm group output on power system business, pre-processing the collected wind turbine output data, and then aggregating the output power data of all wind turbines, analyzing the wind farm group output based on linear frequency modulation Z transform, and judging the impact of wind farm group output on power system business.

[0006] The purpose of the present invention is achieved by adopting the following technical solutions:

[0007] A method for determining the impact of wind farm group output on power system services is improved in that the method comprises:

[0008] Perform abnormal data processing on the operating data of the wind farm group within the identification period and obtain the output sequence of the wind farm group within the identification period;

[0009] Performing a Z transform on the output sequence of the wind farm group within the identification period;

[0010] The output sequence of the wind farm group within the identification period after Z transformation is sampled, and the impact of the wind farm group output within the identification period on the peak regulation and frequency regulation of the power system is judged according to the sampling points.

[0011] Preferably, the operating data of the wind farm group includes: wind speed, wind turbine speed and wind turbine output.

[0012] Preferably, the abnormal data processing of the operation data of the wind farm group within the identification period includes:

[0013] The operating data of the wind farm group within the identification period are subjected to preliminary elimination of abnormal data, deep elimination using the quartile method, and completion of missing data.

[0014] Furthermore, the preliminary elimination of abnormal data from the operating data of the wind farm group within the identification period includes:

[0015] If the i-th wind turbine is idling and not connected to the grid at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and P t i =0, the operating data corresponding to the i-th fan at the t-th moment is retained;

[0016] If the i-th wind turbine is connected to the grid for power generation at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and 0<P t i <P i,r,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0017] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P i,r,rated <P t i ≤P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0018] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and Pt i =P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0019] Otherwise, the operating data corresponding to the i-th wind turbine at time t is eliminated;

[0020] Where, is the wind speed of the i-th wind turbine at the t-th time in the identification period, is the fan speed of the i-th fan at time t in the identification period, P t i To identify the fan output of the i-th fan at time t within the identification cycle, is the rated cut-in wind speed of the i-th wind turbine, v i,r,rated is the wind speed corresponding to the rated rotor speed of the i-th wind turbine, v i,rated is the rated wind speed of the i-th wind turbine, is the cut-out wind speed of the i-th wind turbine, is the minimum rotor speed of the i-th wind turbine, is the rated rotor speed of the i-th wind turbine, P i,r,rated is the wind turbine output corresponding to the rated wind turbine speed of the i-th wind turbine, P i,rated is the rated fan output of the i-th fan.

[0021] Furthermore, the quartile method is used to perform deep elimination on the operating data of the wind farm group within the identification period, including:

[0022] Arrange the wind turbine output at time t in the operating data of the wind farm group within the identification period after the abnormal data are initially eliminated in ascending order to obtain a set P1;

[0023] The quartiles of the set P1 are determined as follows:

[0024] M1 is an odd number, is an odd number

[0025] M1 is an odd number, Even number

[0026] M1 is an even number, is an odd number

[0027] M1 is an even number, Even number

[0028] Where M1 is the number of wind turbine outputs in the set P1, P1 I is the first quartile of the set P1, P1 IIis the second quartile of the set P1, P1 III is the third quartile of the set P1, P1 X is the output of the X-th wind turbine in the set P1, where X∈[1,M1]; a and b are the first and second quartile parameters, respectively, a∈[0,1], b∈[0,1], a+b=1;

[0029] The interquartile range D1 of the set P1 is determined as follows:

[0030] D1=P1 III -P1 I

[0031] After the abnormal data are initially eliminated, the wind turbine output at the t-th moment in the operation data of the wind farm group within the identification period is [P1 I -1.5D1,P1 III +1.5D1] outside the range.

[0032] Furthermore, the step of completing missing data of the wind farm group's operating data within the identification period includes:

[0033] If the wind turbine output P of the hth wind turbine at the tth time in the operation data of the wind farm group within the identification period is t h is eliminated, the average fan output of the k fans before and after the tth moment is added to the fan output P of the hth fan at the tth moment t h The location in the operational data of the wind farm group during the identification period;

[0034] The eliminated data P is determined as follows: t h :

[0035]

[0036] Among them, 1 <k<min(h,M2-h)。

[0037] Preferably, the obtaining of the output sequence of the wind farm group within the identification period includes:

[0038] The output sequence P of the wind farm group within the identification period is determined as follows: T :

[0039] P T =(P(1),P(2),…P(t)…,P(T))

[0040] Where T is the total number of moments in the identification period, P(t) is the wind farm group output at moment t, t = 1, 2, ..., T;

[0041] The wind farm group output P(t) at the time t is determined by the following formula:

[0042]

[0043] Where, P t i is the wind turbine output of the i-th wind turbine at the t-th moment after the abnormal data is processed, and M is the number of wind turbines in the wind farm group.

[0044] Furthermore, performing a Z transform on the output sequence of the wind farm group within the identification period includes:

[0045] The output sequence P(z) of the wind farm group within the identification period after Z transformation is determined as follows:

[0046]

[0047] Specifically, sampling the output sequence of the wind farm group within the identification period after the Z transform, and judging the impact of the wind farm group output on the peak regulation and frequency regulation of the power system based on the sampling points, includes:

[0048] For P(z) along the unit circle on the Z plane, the pth sampling point Z of P(z) is p for:

[0049]

[0050] Where θ0 is the starting frequency of P(z), is the frequency calculation step, N is the number of sampling points;

[0051] If the sampling point of P(z) is within [0Hz-0.0002Hz], the output of the wind farm group has an impact on the medium- and long-term peak-shaving business of the power system;

[0052] If the sampling point of P(z) is within [0.0002Hz-0.001Hz], the output of the wind farm group has an impact on the short-term peak-shaving business of the power system;

[0053] If the sampling point of P(z) is within [0.001Hz-0.15Hz], the output of the wind farm group has an impact on the secondary frequency regulation business of the power system;

[0054] If the sampling point of P(z) is within [0.15 Hz-0.5 Hz], the output of the wind farm group has an impact on the primary frequency regulation business of the power system.

[0055] A system for determining the impact of wind farm group output on power system services is improved in that the system comprises:

[0056] A processing unit, configured to perform abnormal data processing on the operating data of the wind farm group within the identification period;

[0057] An acquisition unit, used for acquiring the output sequence of the wind farm group within the identification period;

[0058] a transformation unit, configured to perform a Z transformation on the output sequence of the wind farm group within the identification period;

[0059] The judgment unit is used to sample the output sequence of the wind farm group within the identification period after the Z transformation, and judge the impact of the wind farm group output within the identification period on the peak regulation and frequency regulation business of the power system according to the sampling points.

[0060] Preferably, the operating data of the wind farm group includes: wind speed, wind turbine speed and wind turbine output.

[0061] Preferably, the processing unit includes:

[0062] A preliminary elimination module is used to perform preliminary elimination of abnormal data from the operating data of the wind farm group within the identification period;

[0063] The deep elimination module is used to perform quartile deep elimination on the operating data of the wind farm group within the identification period;

[0064] The completion module is used to complete the missing data of the wind farm group's operating data within the identification period.

[0065] Furthermore, the preliminary elimination module is specifically used to:

[0066] If the i-th wind turbine is idling and not connected to the grid at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and P t i =0, the operating data corresponding to the i-th fan at the t-th moment is retained;

[0067] If the i-th wind turbine is connected to the grid for power generation at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and 0<P t i <P i,r,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0068] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P i,r,rated <P t i ≤P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0069] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P t i =P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0070] Otherwise, the operating data corresponding to the i-th wind turbine at time t is eliminated;

[0071] Where, is the wind speed of the i-th wind turbine at the t-th time in the identification period, is the fan speed of the i-th fan at time t in the identification period, P t i To identify the fan output of the i-th fan at time t within the identification cycle, is the rated cut-in wind speed of the i-th wind turbine, v i,r,rated is the wind speed corresponding to the rated rotor speed of the i-th wind turbine, v i,rated is the rated wind speed of the i-th wind turbine, is the cut-out wind speed of the i-th wind turbine, is the minimum rotor speed of the i-th wind turbine, is the rated rotor speed of the i-th wind turbine, P i,r,rated is the wind turbine output corresponding to the rated wind turbine speed of the i-th wind turbine, P i,rated is the rated fan output of the i-th fan.

[0072] Furthermore, the depth culling module is specifically used to:

[0073] Arrange the wind turbine output at time t in the operating data of the wind farm group within the identification period after the abnormal data are initially eliminated in ascending order to obtain a set P1;

[0074] The quartiles of the set P1 are determined as follows:

[0075] M1 is an odd number, is an odd number

[0076] M1 is an odd number, Even number

[0077] M1 is an even number, is an odd number

[0078] M1 is an even number, Even number

[0079] Where M1 is the number of wind turbine outputs in the set P1, P1 I is the first quartile of the set P1, P1 II is the second quartile of the set P1, P1 III is the third quartile of the set P1, P1 X is the output of the X-th wind turbine in the set P1, where X∈[1,M1]; a and b are the first and second quartile parameters, respectively, a∈[0,1], b∈[0,1], a+b=1;

[0080] The interquartile range D1 of the set P1 is determined as follows:

[0081] D1=P1 III -P1 I

[0082] After the abnormal data are initially eliminated, the wind turbine output at the t-th moment in the operation data of the wind farm group within the identification period is [P1 I -1.5D1,P1 III +1.5D1] outside the range.

[0083] Furthermore, the completion module is specifically used to:

[0084] If the wind turbine output P of the hth wind turbine at the tth time in the operation data of the wind farm group within the identification period is t h is eliminated, the average fan output of the k fans before and after the tth moment is added to the fan output P of the hth fan at the tth moment t h The location in the operational data of the wind farm group during the identification period;

[0085] The eliminated data P is determined as follows: t h :

[0086]

[0087] Among them, 1 <k<min(h,M2-h)。

[0088] Preferably, the acquisition unit is specifically used to:

[0089] The output sequence P of the wind farm group within the identification period is determined as follows: T :

[0090] P T =(P(1),P(2),…P(t)…,P(T))

[0091] Where T is the total number of moments in the identification period, P(t) is the wind farm group output at moment t, t = 1, 2, ..., T;

[0092] The wind farm group output P(t) at the time t is determined by the following formula:

[0093]

[0094] Where, P t i is the wind turbine output of the i-th wind turbine at the t-th moment after the abnormal data is processed, and M is the number of wind turbines in the wind farm group.

[0095] Furthermore, the transformation unit is specifically configured to:

[0096] The output sequence P(z) of the wind farm group within the identification period after Z transformation is determined as follows:

[0097]

[0098] Specifically, the judgment unit is specifically used to:

[0099] For P(z) along the unit circle on the Z plane, the pth sampling point Z of P(z) is p for:

[0100]

[0101] Where θ0 is the starting frequency of P(z), is the frequency calculation step, N is the number of sampling points;

[0102] If the sampling point of P(z) is within [0Hz-0.0002Hz], the output of the wind farm group has an impact on the medium- and long-term peak-shaving business of the power system;

[0103] If the sampling point of P(z) is within [0.0002Hz-0.001Hz], the output of the wind farm group has an impact on the short-term peak-shaving business of the power system;

[0104] If the sampling point of P(z) is within [0.001Hz-0.15Hz], the output of the wind farm group has an impact on the secondary frequency regulation business of the power system;

[0105] If the sampling point of P(z) is within [0.15 Hz-0.5 Hz], the output of the wind farm group has an impact on the primary frequency regulation business of the power system.

[0106] Compared with the closest prior art, the present invention has the following beneficial effects:

[0107] Based on the operating principles of wind farm wind turbines and the statistical laws of wind turbine power output, the technical solution provided by this invention preprocesses collected wind turbine output data and aggregates all wind turbine output data to obtain aggregated wind farm output data. This aggregated wind farm output data is processed and sampled using a linear frequency modulation Z transform. The impact of wind farm output on power system services is determined based on the sensitive frequency bands of the sampling points.

[0108] The present invention can be applied to the fluctuation analysis of the aggregated data of power output of large-scale wind farms, and provide technical support for studying their impact on peak and frequency regulation of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 This is a flow chart of a method for determining the impact of wind farm group output on power system services in an embodiment of the present invention;

[0110] Figure 2 The figure is a structural diagram of a system for determining the impact of wind farm group output on power system services according to an embodiment of the present invention. DETAILED DESCRIPTION

[0111] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0112] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0113] The present invention provides a method for determining the impact of wind farm group output on power system services, such as Figure 1 As shown, the method includes:

[0114] Step 101: Process abnormal data of the wind farm group's operating data within the identification period;

[0115] Step 102: Obtain the output sequence of the wind farm group within the identification period;

[0116] Step 103: Perform Z-transform on the output sequence of the wind farm group within the identification period;

[0117] Step 104: Sampling the output sequence of the wind farm group within the identification period after the Z transformation, and judging the impact of the wind farm group output within the identification period on the peak regulation and frequency regulation of the power system based on the sampling points.

[0118] The operation data of the wind farm group include wind speed, wind turbine speed and wind turbine output.

[0119] Furthermore, the step 101 includes:

[0120] The operating data of the wind farm group within the identification period are subjected to preliminary elimination of abnormal data, deep elimination using the quartile method, and completion of missing data.

[0121] Specifically, the preliminary elimination of abnormal data from the operating data of the wind farm group within the identification period includes:

[0122] If the i-th wind turbine is idling and not connected to the grid at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and P t i =0, the operating data corresponding to the i-th fan at the t-th moment is retained;

[0123] If the i-th wind turbine is connected to the grid for power generation at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and 0<P t i <P i,r,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0124] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P i,r,rated <P t i ≤P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0125] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P t i =P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0126] Otherwise, the operating data corresponding to the i-th wind turbine at time t is eliminated;

[0127] Where, is the wind speed of the i-th wind turbine at the t-th time in the identification period, is the fan speed of the i-th fan at time t in the identification period, P t i To identify the fan output of the i-th fan at time t within the identification cycle, is the rated cut-in wind speed of the i-th wind turbine, v i,r,rated is the wind speed corresponding to the rated rotor speed of the i-th wind turbine, v i,rated is the rated wind speed of the i-th wind turbine, is the cut-out wind speed of the i-th wind turbine, is the minimum rotor speed of the i-th wind turbine, is the rated rotor speed of the i-th wind turbine, P i,r,rated is the wind turbine output corresponding to the rated wind turbine speed of the i-th wind turbine, P i,rated is the rated fan output of the i-th fan.

[0128] Specifically, the quartile method deep elimination of the operating data of the wind farm group within the identification period includes:

[0129] Arrange the wind turbine output at time t in the operating data of the wind farm group within the identification period after the abnormal data are initially eliminated in ascending order to obtain a set P1;

[0130] The quartiles of the set P1 are determined as follows:

[0131] M1 is an odd number, is an odd number

[0132] M1 is an odd number, Even number

[0133] M1 is an even number, is an odd number

[0134] M1 is an even number, Even number

[0135] Where M1 is the number of wind turbine outputs in the set P1, P1 I is the first quartile of the set P1, P1 II is the second quartile of the set P1, P1 III is the third quartile of the set P1, P1 Xis the output of the X-th wind turbine in the set P1, where X∈[1,M1]; a and b are the first and second quartile parameters, respectively, a∈[0,1], b∈[0,1], a+b=1;

[0136] The interquartile range D1 of the set P1 is determined as follows:

[0137] D1=P1 III -P1 I

[0138] After the abnormal data are initially eliminated, the wind turbine output at the t-th moment in the operation data of the wind farm group within the identification period is [P1 I -1.5D1,P1 III +1.5D1] outside the range.

[0139] Specifically, the missing data completion for the operation data of the wind farm group within the identification period includes:

[0140] If the wind turbine output P of the hth wind turbine at the tth time in the operation data of the wind farm group within the identification period is t h is eliminated, the average fan output of the k fans before and after the tth moment is added to the fan output P of the hth fan at the tth moment t h The location in the operational data of the wind farm group during the identification period;

[0141] The eliminated data P is determined as follows: t h :

[0142]

[0143] Among them, 1 <k<min(h,M2-h)。

[0144] Furthermore, after abnormal data processing is performed on the operating data of the wind farm group within the identification period, step 102 includes:

[0145] The output sequence P of the wind farm group within the identification period is determined as follows: T :

[0146] P T =(P(1),P(2),…P(t)…,P(T))

[0147] Where T is the total number of moments in the identification period, P(t) is the wind farm group output at moment t, t = 1, 2, ..., T;

[0148] The wind farm group output P(t) at the time t is determined by the following formula:

[0149]

[0150] Where, P t i is the wind turbine output of the i-th wind turbine at the t-th moment after the abnormal data is processed, and M is the number of wind turbines in the wind farm group.

[0151] Specifically, after obtaining the output sequence of the wind farm group within the identification period, step 103 includes:

[0152] The output sequence P(z) of the wind farm group within the identification period after Z transformation is determined as follows:

[0153]

[0154] Specifically, the random volatility of wind power output is manifested in the frequency domain as frequent changes in wind power output in each frequency band. The time scale of peak regulation and frequency regulation of the power system is between 2s and 60min, so the sensitive frequency band of wind power output fluctuation is 0.0002Hz to 0.5Hz. For practical application, the sensitive frequency band is subdivided, and each frequency band corresponds to a power system business, as shown in Table 1; the greater the amplitude of wind power output fluctuation, the greater the maximum frequency difference of the system frequency response. In order to evaluate the frequency response characteristics of the power system and determine the impact of the wind farm group output on the power system business within the identification period, step 104 includes:

[0155] For P(z) along the unit circle on the Z plane, the pth sampling point Z of P(z) is p for:

[0156]

[0157] Where θ0 is the starting frequency of P(z), is the frequency calculation step, N is the number of sampling points;

[0158] If the sampling point of P(z) is within [0Hz-0.0002Hz], the output of the wind farm group has an impact on the medium- and long-term peak-shaving business of the power system;

[0159] If the sampling point of P(z) is within [0.0002Hz-0.001Hz], the output of the wind farm group has an impact on the short-term peak-shaving business of the power system;

[0160] If the sampling point of P(z) is within [0.001Hz-0.15Hz], the output of the wind farm group has an impact on the secondary frequency regulation business of the power system;

[0161] If the sampling point of P(z) is within [0.15Hz-0.5Hz], the output of the wind farm group has an impact on the primary frequency regulation business of the power system;

[0162] Choose appropriate θ0 and Analyze and, based on the frequency band where the sampling point of P(z) is located, determine the impact of the wind farm group output on the power system business during the identification period, as shown in Table 1:

[0163] Table 1

[0164]

[0165] A system for judging the impact of wind farm group output on power system business, such as Figure 2 As shown, the system includes:

[0166] A processing unit, configured to perform abnormal data processing on the operating data of the wind farm group within the identification period;

[0167] An acquisition unit, used for acquiring the output sequence of the wind farm group within the identification period;

[0168] a transformation unit, configured to perform a Z transformation on the output sequence of the wind farm group within the identification period;

[0169] The judgment unit is used to sample the output sequence of the wind farm group within the identification period after the Z transformation, and judge the impact of the wind farm group output within the identification period on the peak regulation and frequency regulation business of the power system according to the sampling points.

[0170] The operation data of the wind farm group include wind speed, wind turbine speed and wind turbine output.

[0171] Furthermore, the processing unit includes:

[0172] A preliminary elimination module is used to perform preliminary elimination of abnormal data from the operating data of the wind farm group within the identification period;

[0173] The deep elimination module is used to perform quartile deep elimination on the operating data of the wind farm group within the identification period;

[0174] The completion module is used to complete the missing data of the wind farm group's operating data within the identification period.

[0175] Specifically, the preliminary elimination module is specifically used to:

[0176] If the i-th wind turbine is idling and not connected to the grid at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and P ti =0, the operating data corresponding to the i-th fan at the t-th moment is retained;

[0177] If the i-th wind turbine is connected to the grid for power generation at time t, and the corresponding operating data of the i-th wind turbine at time t is and P t i satisfy: and 0<P t i <P i,r,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0178] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P i,r,rated <P t i ≤P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0179] If the operating data corresponding to the i-th fan at time t and P t i satisfy: and P t i =P i,rated When , the operating data corresponding to the i-th wind turbine at time t is retained;

[0180] Otherwise, the operating data corresponding to the i-th wind turbine at time t is eliminated;

[0181] Where, is the wind speed of the i-th wind turbine at the t-th time in the identification period, is the fan speed of the i-th fan at time t in the identification period, P t i To identify the fan output of the i-th fan at time t within the identification cycle, is the rated cut-in wind speed of the i-th wind turbine, v i,r,rated is the wind speed corresponding to the rated rotor speed of the i-th wind turbine, v i,rated is the rated wind speed of the i-th wind turbine, is the cut-out wind speed of the i-th wind turbine, is the minimum rotor speed of the i-th wind turbine, is the rated rotor speed of the i-th wind turbine, P i,r,rated is the wind turbine output corresponding to the rated wind turbine speed of the i-th wind turbine, P i,rated is the rated fan output of the i-th fan.

[0182] Specifically, the depth culling module is specifically used to:

[0183] Arrange the wind turbine output at time t in the operating data of the wind farm group within the identification period after the abnormal data are initially eliminated in ascending order to obtain a set P1;

[0184] The quartiles of the set P1 are determined as follows:

[0185] M1 is an odd number, is an odd number

[0186] M1 is an odd number, Even number

[0187] M1 is an even number, is an odd number

[0188] M1 is an even number, Even number

[0189] Where M1 is the number of wind turbine outputs in the set P1, P1 I is the first quartile of the set P1, P1 II is the second quartile of the set P1, P1 III is the third quartile of the set P1, P1 X is the output of the X-th wind turbine in the set P1, where X∈[1,M1]; a and b are the first and second quartile parameters, respectively, a∈[0,1], b∈[0,1], a+b=1;

[0190] The interquartile range D1 of the set P1 is determined as follows:

[0191] D1=P1 III -P1 I

[0192] After the abnormal data are initially eliminated, the wind turbine output at the t-th moment in the operation data of the wind farm group within the identification period is [P1 I -1.5D1,P1 III +1.5D1] outside the range.

[0193] Specifically, the completion module is specifically used to:

[0194] If the wind turbine output P of the hth wind turbine at the tth time in the operation data of the wind farm group within the identification period is t his eliminated, the average fan output of the k fans before and after the tth moment is added to the fan output P of the hth fan at the tth moment t h The location in the operational data of the wind farm group during the identification period;

[0195] The eliminated data P is determined as follows: t h :

[0196]

[0197] Among them, 1 <k<min(h,M2-h)。

[0198] Furthermore, the acquisition unit is specifically configured to:

[0199] The output sequence P of the wind farm group within the identification period is determined as follows: T :

[0200] P T =(P(1),P(2),…P(t)…,P(T))

[0201] Where T is the total number of moments in the identification period, P(t) is the wind farm group output at moment t, t = 1, 2, ..., T;

[0202] The wind farm group output P(t) at the time t is determined by the following formula:

[0203]

[0204] Where, P t i is the wind turbine output of the i-th wind turbine at the t-th moment after the abnormal data is processed, and M is the number of wind turbines in the wind farm group.

[0205] Specifically, the transformation unit is specifically used to:

[0206] The output sequence P(z) of the wind farm group within the identification period after Z transformation is determined as follows:

[0207]

[0208] Specifically, the judgment unit is specifically used to:

[0209] For P(z) along the unit circle on the Z plane, the pth sampling point Z of P(z) is p for:

[0210]

[0211] Where θ0 is the starting frequency of P(z), is the frequency calculation step, N is the number of sampling points;

[0212] If the sampling point of P(z) is within [0Hz-0.0002Hz], the output of the wind farm group has an impact on the medium- and long-term peak-shaving business of the power system;

[0213] If the sampling point of P(z) is within [0.0002Hz-0.001Hz], the output of the wind farm group has an impact on the short-term peak-shaving business of the power system;

[0214] If the sampling point of P(z) is within [0.001Hz-0.15Hz], the output of the wind farm group has an impact on the secondary frequency regulation business of the power system;

[0215] If the sampling point of P(z) is within [0.15 Hz-0.5 Hz], the output of the wind farm group has an impact on the primary frequency regulation business of the power system.

[0216] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0217] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0218] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for determining the impact of wind farm group output on power system services, characterized in that: The method comprises: Perform abnormal data processing on the operating data of the wind farm group within the identification period and obtain the output sequence of the wind farm group within the identification period; Performing a Z transform on the output sequence of the wind farm group within the identification period; The output sequence of the wind farm group within the identification period after Z transformation is sampled, and the impact of the wind farm group output within the identification period on the peak regulation and frequency regulation of the power system is determined based on the sampling points; The obtaining of the output sequence of the wind farm group within the identification period includes: The output sequence P of the wind farm group within the identification period is determined as follows: T : P T =(P(1),P(2),…P(t)…,P(T)) Where T is the total number of moments in the identification period, P(t) is the wind farm group output at moment t, t = 1, 2, ..., T; The wind farm group output P(t) at the time t is determined by the following formula: Where, is the wind turbine output of the i-th wind turbine at time t after the abnormal data is processed, and M is the number of wind turbines in the wind farm group; The Z-transformation of the output sequence of the wind farm group within the identification period includes: The output sequence P(z) of the wind farm group within the identification period after Z transformation is determined as follows: The step of sampling the output sequence of the wind farm group within the identification period after the Z transformation and judging the impact of the wind farm group output on the peak regulation and frequency regulation of the power system based on the sampling points includes: For P(z) along the unit circle on the Z plane, the pth sampling point Z of P(z) is p for: Where θ0 is the starting frequency of P(z), is the frequency calculation step, N is the number of sampling points; If the sampling point of P(z) is within [0Hz-0.0002Hz], the output of the wind farm group has an impact on the medium- and long-term peak-shaving business of the power system; If the sampling point of P(z) is within [0.0002Hz-0.001Hz], the output of the wind farm group has an impact on the short-term peak-shaving business of the power system; If the sampling point of P(z) is within [0.001Hz-0.15Hz], the output of the wind farm group has an impact on the secondary frequency regulation business of the power system; If the sampling point of P(z) is within [0.15 Hz-0.5 Hz], the output of the wind farm group has an impact on the primary frequency regulation business of the power system.

2. The method according to claim 1, wherein The operating data of the wind farm group includes: wind speed, wind turbine speed and wind turbine output.

3. The method according to claim 1, wherein The abnormal data processing of the operation data of the wind farm group within the identification period includes: The operating data of the wind farm group within the identification period are subjected to preliminary elimination of abnormal data, deep elimination using the quartile method, and completion of missing data.

4. The method according to claim 3, wherein The preliminary elimination of abnormal data from the operating data of the wind farm group within the identification period includes: If the i-th wind turbine is idling and not connected to the grid at time t, and the corresponding operating data of the i-th wind turbine at time t is and satisfy: and When , the operating data corresponding to the i-th wind turbine at the t-th moment is retained; If the i-th wind turbine is connected to the grid for power generation at time t, and the corresponding operating data of the i-th wind turbine at time t is and satisfy: and When , the operating data corresponding to the i-th wind turbine at time t is retained; If the operating data corresponding to the i-th fan at time t and satisfy: and When , the operating data corresponding to the i-th wind turbine at time t is retained; If the operating data corresponding to the i-th fan at time t and satisfy: and When , the operating data corresponding to the i-th wind turbine at time t is retained; Otherwise, the operating data corresponding to the i-th wind turbine at time t is eliminated; Where, is the wind speed of the i-th wind turbine at the t-th time in the identification period, is the fan speed of the i-th fan at the t-th time in the identification cycle, To identify the fan output of the i-th fan at time t within the identification cycle, is the rated cut-in wind speed of the i-th wind turbine, v i,r,rated is the wind speed corresponding to the rated rotor speed of the i-th wind turbine, v i,rated is the rated wind speed of the i-th wind turbine, is the cut-out wind speed of the i-th wind turbine, is the minimum rotor speed of the i-th wind turbine, is the rated rotor speed of the i-th wind turbine, P i,r,rated is the wind turbine output corresponding to the rated wind turbine speed of the i-th wind turbine, P i,rated is the rated fan output of the i-th fan.

5. The method according to claim 3, wherein The quartile method deep elimination of the operating data of the wind farm group within the identification period includes: Arrange the wind turbine output at time t in the operating data of the wind farm group within the identification period after the abnormal data are initially eliminated in ascending order to obtain a set P1; The quartiles of the set P1 are determined as follows: M1 is an odd number, is an odd number M1 is an odd number, Even number M1 is an even number, is an odd number M1 is an even number, Even number Where M1 is the number of wind turbine outputs in the set P1, P1 I is the first quartile of the set P1, P1 II is the second quartile of the set P1, P1 III is the third quartile of the set P1, P1 X is the output of the X-th wind turbine in the set P1, where X∈[1,M1]; a and b are the first and second quartile parameters, respectively, a∈[0,1], b∈[0,1], a+b=1; The interquartile range D1 of the set P1 is determined as follows: D1=P1 III -P1 I After the abnormal data are initially eliminated, the wind turbine output at the t-th moment in the operation data of the wind farm group within the identification period is [P1 I -1.5D1,P1 III +1.5D1] outside the range.

6. The method according to claim 3, wherein The step of completing missing data of the wind farm group's operating data within the identification period includes: If the wind turbine output of the hth wind turbine at the tth time in the operation data of the wind farm group within the identification period is If it is eliminated, the average fan output of the k fans before and after the tth moment is added to the fan output of the hth fan at the tth moment. The location in the operational data of the wind farm group during the identification period; The data to be eliminated is determined by the following formula: Among them, 1 <k<min(h,M2-h)。 7. A system for determining the impact of wind farm group output on power system services, characterized in that: The system comprises: A processing unit, configured to perform abnormal data processing on the operating data of the wind farm group within the identification period; An acquisition unit, used for acquiring the output sequence of the wind farm group within the identification period; a transformation unit, configured to perform a Z transformation on the output sequence of the wind farm group within the identification period; A judgment unit is used to sample the output sequence of the wind farm group within the identification period after the Z transformation, and judge the impact of the wind farm group output within the identification period on the peak regulation and frequency regulation business of the power system according to the sampling points; The acquisition unit is specifically configured to: The output sequence P of the wind farm group within the identification period is determined as follows: T : P T =(P(1),P(2),…P(t)…,P(T)) Where T is the total number of moments in the identification period, P(t) is the wind farm group output at moment t, t = 1, 2, ..., T; The wind farm group output P(t) at the time t is determined by the following formula: Where, is the wind turbine output of the i-th wind turbine at time t after the abnormal data is processed, and M is the number of wind turbines in the wind farm group; The transformation unit is specifically configured to: The output sequence P(z) of the wind farm group within the identification period after Z transformation is determined as follows: The judgment unit is specifically used for: For P(z) along the unit circle on the Z plane, the pth sampling point Z of P(z) is p for: Where θ0 is the starting frequency of P(z), is the frequency calculation step, N is the number of sampling points; If the sampling point of P(z) is within [0Hz-0.0002Hz], the output of the wind farm group has an impact on the medium- and long-term peak-shaving business of the power system; If the sampling point of P(z) is within [0.0002Hz-0.001Hz], the output of the wind farm group has an impact on the short-term peak-shaving business of the power system; If the sampling point of P(z) is within [0.001Hz-0.15Hz], the output of the wind farm group has an impact on the secondary frequency regulation business of the power system; If the sampling point of P(z) is within [0.15 Hz-0.5 Hz], the output of the wind farm group has an impact on the primary frequency regulation business of the power system.

8. The system according to claim 7, wherein: The operating data of the wind farm group includes: wind speed, wind turbine speed and wind turbine output.

9. The system according to claim 7, wherein: The processing unit includes: A preliminary elimination module is used to perform preliminary elimination of abnormal data from the operating data of the wind farm group within the identification period; The deep elimination module is used to perform quartile deep elimination on the operating data of the wind farm group within the identification period. The completion module is used to complete the missing data of the wind farm group's operating data within the identification period.

10. The system according to claim 9, wherein The preliminary elimination module is specifically used for: If the i-th wind turbine is idling and not connected to the grid at time t, and the corresponding operating data of the i-th wind turbine at time t is and satisfy: and When , the operating data corresponding to the i-th wind turbine at the t-th moment is retained; If the i-th wind turbine is connected to the grid for power generation at time t, and the corresponding operating data of the i-th wind turbine at time t is and satisfy: and When , the operating data corresponding to the i-th wind turbine at time t is retained; If the operating data corresponding to the i-th fan at time t and satisfy: and When , the operating data corresponding to the i-th wind turbine at time t is retained; If the operating data corresponding to the i-th fan at time t and satisfy: and When , the operating data corresponding to the i-th wind turbine at time t is retained; Otherwise, the operating data corresponding to the i-th wind turbine at time t is eliminated; Where, is the wind speed of the i-th wind turbine at the t-th time in the identification period, is the fan speed of the i-th fan at the t-th time in the identification cycle, To identify the fan output of the i-th fan at time t within the identification cycle, is the rated cut-in wind speed of the i-th wind turbine, v i,r,rated is the wind speed corresponding to the rated rotor speed of the i-th wind turbine, v i,rated is the rated wind speed of the i-th wind turbine, is the cut-out wind speed of the i-th wind turbine, is the minimum rotor speed of the i-th wind turbine, is the rated rotor speed of the i-th wind turbine, P i,r,rated is the wind turbine output corresponding to the rated wind turbine speed of the i-th wind turbine, P i,rated is the rated fan output of the i-th fan.

11. The system according to claim 9, wherein The depth culling module is specifically used for: Arrange the wind turbine output at time t in the operating data of the wind farm group within the identification period after the abnormal data are initially eliminated in ascending order to obtain a set P1; The quartiles of the set P1 are determined as follows: M1 is an odd number, is an odd number M1 is an odd number, Even number M1 is an even number, is an odd number M1 is an even number, Even number Where M1 is the number of wind turbine outputs in the set P1, P1 I is the first quartile of the set P1, P1 II is the second quartile of the set P1, P1 III is the third quartile of the set P1, P1 X is the output of the X-th wind turbine in the set P1, where X∈[1,M1]; a and b are the first and second quartile parameters, respectively, a∈[0,1], b∈[0,1], a+b=1; The interquartile range D1 of the set P1 is determined as follows: D1=P1 III -P1 I After the abnormal data are initially eliminated, the wind turbine output at the t-th moment in the operation data of the wind farm group within the identification period is [P1 I -1.5D1,P1 III +1.5D1] outside the range.

12. The system according to claim 9, wherein The completion module is specifically used for: If the wind turbine output of the hth wind turbine at the tth time in the operation data of the wind farm group within the identification period is If it is eliminated, the average fan output of the k fans before and after the tth moment is added to the fan output of the hth fan at the tth moment. The location in the operational data of the wind farm group during the identification period; The data to be eliminated is determined by the following formula: Among them, 1 <k<min(h,M2-h)。

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