Power stabilizing method and system for port wind storage combined system

Abstract

The invention relates to the technical field of new energy grid-connected control, and discloses a port wind storage combined system power stabilizing method and system. Comprising the steps of dynamically adjusting a filtering weight of wind power original power based on a first fluctuation degree of the wind power original power and a second fluctuation degree of wind power prediction power; the filtering proportion is adjusted based on the filtering weight, the wind power original power is filtered, and a power fluctuation component needing to be stabilized by the wind storage combined system is determined; decomposing the power fluctuation component by adopting a self-adaptive frequency division empirical mode decomposition method comprising a local mean value and an optimization operator, and determining a plurality of mode power components of different frequencies; and controlling a capacitor to stabilize the modal power component of which the frequency is greater than the frequency of the crossover frequency based on the energy router of the wind storage combined system, and controlling a storage battery to stabilize the modal power component of which the frequency is less than the frequency of the crossover frequency based on the energy router of the wind storage combined system. According to the invention, the overall performance of the energy storage system can be improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of new energy grid connection control, and particularly to a method and system for power smoothing of a port wind and energy storage combined system. Background Art

[0002] In a port, the turbulence intensity of offshore wind power is high and the wind speed changes frequently. The fan system must ensure that the active power output meets certain requirements to achieve grid connection. However, during the grid connection process, the wind power fluctuates greatly, and the volatility of the wind power poses a severe challenge to the stability of the power grid. Therefore, it is necessary to smooth the original wind power so that the wind power can be output stably.

[0003] In the related art, it is difficult to balance high-frequency and low-frequency fluctuations, so the system operation cost is high and the service life is short. Summary of the Invention

[0004] To solve the above technical problems, the present disclosure provides a method for power smoothing of a port wind and energy storage combined system, including the following steps: Obtain the first fluctuation degree of the original wind power and the second fluctuation degree of the predicted wind power, and dynamically adjust the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree; adjust the filtering ratio based on the filtering weight, filter the original wind power, and determine the power fluctuation component that needs to be smoothed by the wind and energy storage combined system; use the adaptive frequency division empirical mode decomposition method including local mean and optimization operator to decompose the power fluctuation component and determine multiple modal power components with different frequencies; determine the frequency division frequency, and based on the energy router of the wind and energy storage combined system, control the capacitor to smooth the modal power component with a frequency greater than the frequency division frequency, and control the battery to smooth the modal power component with a frequency less than the frequency division frequency.

[0005] Further, the dynamically adjusting the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree includes: Obtain the first grid-connected power obtained by filtering the original wind power based on moving average filtering, and the second grid-connected power obtained by filtering the original wind power based on anti-pulse interference average filtering; calculate the standard deviation of the original wind power based on the first fluctuation degree, and calculate the standard deviation of the predicted wind power based on the second fluctuation degree; calculate the first weight of the first grid-connected power and the second weight of the second grid-connected power based on the standard deviation of the original wind power and the standard deviation of the predicted wind power.

[0006] Further, after dynamically adjusting the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree, the method further includes: Determine the first grid-connected component incorporated into the wind-storage combined system based on the first grid-connected power and the first weight; determine the second grid-connected component incorporated into the wind-storage combined system based on the second grid-connected power and the second weight; and determine the sum of the first grid-connected component and the second grid-connected component as the grid-connected power of the wind-storage combined system.

[0007] Further, the optimization operator includes a first optimization operator and a second optimization operator; the method for adaptively decomposing the power fluctuation component by frequency using the empirical mode decomposition method incorporating local mean and optimization operator to determine multiple modal power components of different frequencies includes: Determine the power fluctuation signal, signal-to-noise ratio, and the number of times of adding white noise of the power fluctuation component; optimize the white noise added for the first time based on the first optimization operator and the signal-to-noise ratio to obtain the first white noise; estimate the sum of the first white noise and the power fluctuation signal using the local mean to determine the local power mean; optimize the local power mean based on the second optimization operator to determine the first power residual, and decompose the power fluctuation component based on the local power mean and the first power residual to determine multiple modal power components of different frequencies.

[0008] Further, the method for decomposing the power fluctuation component based on the local power mean and the first power residual to determine multiple modal power components of different frequencies includes: Determine the difference between the power fluctuation signal and the first power residual as the first modal power component; optimize the sum of the first power residual and the first white noise according to the second optimization operator to determine the second power residual, and determine the difference between the first power residual and the second power residual as the second modal power component; optimize the white noise added for the second time based on the first optimization operator and the signal-to-noise ratio to obtain the second white noise; optimize the sum of the second power residual and the second white noise according to the second optimization operator to determine the third power residual, and determine the difference between the second power residual and the third power residual as the third modal power component; until the number of modal power components is the same as the number of times of adding white noise, stop decomposing the power fluctuation component to determine multiple modal power components of different frequencies.

[0009] Further, the determining of the frequency division frequency includes: Extract the instantaneous frequency of each of the modal power components, and determine the correlation between the instantaneous frequency and time; determine the frequency interval according to the maximum and minimum values of the instantaneous frequency; determine a temporary frequency division frequency within the frequency interval, and calculate the energy of the aliased modal power between adjacent modal power components based on the temporary frequency division frequency and the correlation; determine the frequency division frequency corresponding to the minimum value of the energy as the allocated frequency.

[0010] Further, the step of determining a temporary frequency division frequency within the frequency interval and calculating the energy of the aliased modal power between adjacent modal power components based on the temporary frequency division frequency and the correlation includes: Determine a plurality of temporary frequency division frequencies within the frequency interval based on a preset frequency interval, and determine the time corresponding to each temporary frequency division frequency according to the correlation; determine the modal power components corresponding to adjacent instantaneous frequencies according to the temporary frequency division frequency, and determine the aliased modal power of adjacent modal power components; calculate the energy of the aliased modal power based on the time and adjacent modal power components.

[0011] Further, the energy router of the wind-storage integrated system includes a plurality of DC-DC converters, a plurality of DC-AC converters, and an intelligent energy storage controller; one end of each DC-DC converter is respectively connected to the port wind power and the hybrid energy storage, and is used to obtain the original wind power and the power output by the hybrid energy storage system, and the other ends are all connected in parallel to the DC bus; One end of each DC-AC converter is connected in parallel to the DC bus, and the other end is connected in parallel to the power grid, and is used to output the AC voltage after the port wind-storage integrated system suppresses the power; the intelligent energy storage controller is used to control the capacitor to suppress the modal power components with frequencies greater than the frequency division frequency, and control the battery to suppress the modal power components with frequencies less than the frequency division frequency.

[0012] Further, the DC-AC converter is a three-level DC-AC converter; the three-level DC-AC converter includes a DC part and an AC part; the DC part and the AC part are respectively composed of a plurality of triode common-emitter diode units connected in series and parallel.

[0013] A port wind-storage integrated system power suppression system, the system includes: An adjustment module is configured to obtain a first fluctuation degree of the original wind power and a second fluctuation degree of the predicted wind power, and dynamically adjust the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree; A determination module is configured to adjust the filtering ratio based on the filtering weight, filter the original wind power, and determine a power fluctuation component that needs to be suppressed by the wind storage combined system; An adaptive frequency division empirical mode decomposition method including local mean and optimization operator is used to decompose the power fluctuation component to determine multiple modal power components of different frequencies; A suppression module is configured to determine a frequency division frequency, and based on the energy router of the wind storage combined system, control the capacitor to suppress the modal power component with a frequency greater than the frequency division frequency, and based on the energy router of the wind storage combined system, control the battery to suppress the modal power component with a frequency less than the frequency division frequency.

[0014] The embodiments of the present disclosure have the following technical effects: The power smoothing method of the port wind storage combined system provided by the present disclosure, first, obtains the first fluctuation degree of the original wind power and the second fluctuation degree of the predicted wind power, and dynamically adjusts the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree, adjusts the filtering ratio based on the filtering weight, filters the original wind power, and determines the power fluctuation component that needs to be suppressed by the wind storage combined system. Through the dynamic adjustment of the filtering weight, the advantages of different filters are combined, effectively solving the possible time delay problem and the limitations in the smoothing effect, so as to achieve a more accurate and smooth filtering effect and reduce the system risk caused by power fluctuations. Then, an adaptive frequency division empirical mode decomposition method including local mean and optimization operator is used to decompose the power fluctuation component to determine multiple modal power components of different frequencies. The present disclosure decomposes the power fluctuation component into multiple modal power components of different frequencies, better reasonably allocates the power fluctuation component, thereby improving the operation efficiency and stability of the wind farm. Finally, determine the frequency division frequency, and based on the energy router of the wind storage combined system, control the capacitor to suppress the modal power component with a frequency greater than the frequency division frequency, and based on the energy router of the wind storage combined system, control the battery to suppress the modal power component with a frequency less than the frequency division frequency. Furthermore, it can be understood that by allocating signals of different frequency bands to the most suitable energy storage devices, the overall performance of the energy storage system can be improved, the service life of the equipment can be extended, and at the same time, the output stability of the wind storage combined system can be improved, and the operation cost of the system can be reduced. Description of the Drawings

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of the power smoothing method for the port wind-storage integrated system provided by an embodiment of the present disclosure; Figure 2 It is a flowchart of the wind power prediction method provided by an embodiment of the present disclosure; Figure 3 It is a flowchart of the power fluctuation component decomposition method provided by an embodiment of the present disclosure; Figure 4 It is a flowchart of the power fluctuation component smoothing method provided by an embodiment of the present disclosure; Figure 5 It is a topological structure diagram of the energy router provided by an embodiment of the present disclosure; Figure 6 It is a topological structure diagram of the three-level converter of the energy router of the port wind-storage integrated system provided by an embodiment of the present disclosure; Figure 7 It is a structure diagram of the wind-storage integrated system provided by an embodiment of the present disclosure; Figure 8 It is a schematic diagram of the predicted wind power provided by an embodiment of the present disclosure; Figure 9 It is a smoothing effect diagram of dynamically weighted filtering to smooth the original wind power fluctuation provided by an embodiment of the present disclosure; Figure 10 It is a schematic diagram of the power that the hybrid energy storage system needs to smooth obtained by dynamically weighted filtering provided by an embodiment of the present disclosure; Figures 11(a)-(d) are the modal power components and their spectrograms provided by an embodiment of the present disclosure; Figure 12 It is an instantaneous frequency-time curve of the modal power component provided by an embodiment of the present disclosure; Figure 13 It is a smoothing effect diagram of the wind power fluctuation provided by an embodiment of the present disclosure; Figure 14 It is a schematic diagram of the operating conditions of the energy storage system provided by an embodiment of the present disclosure; Figure 15 It is a schematic diagram of the power smoothing system of the port wind-storage integrated system provided by an embodiment of the present disclosure. Specific Embodiments

[0017] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without any creative efforts belong to the present invention.

[0018] In ports, the turbulence intensity of offshore wind power is high and the wind speed changes frequently. The fan system must ensure that the active power output meets certain requirements before grid connection can be achieved. During the grid connection process, the wind power fluctuates greatly, and the volatility of wind power poses a severe challenge to the stability of the power grid. Therefore, it is necessary to suppress the original wind power so that the wind power can be output smoothly.

[0019] This application proposes a power smoothing method and system for a port wind-storage combined system aiming at quantifying the smoothing effect of wind power fluctuations. First, the filtering ratio is adjusted using a determined filtering weight to filter and adjust the original wind power. Subsequently, the adaptive frequency division empirical mode decomposition method is used to decompose the power fluctuation components that need to be smoothed by the wind-storage combined system. The power fluctuation components are decomposed into modal functions of different frequencies, and the instantaneous frequencies of the modal functions are further extracted through Hilbert transform to obtain the relationship between the instantaneous frequency and time. Finally, a power distribution smoothing control strategy and an energy control device for the hybrid energy storage system are proposed, enabling the supercapacitor to better bear the high-frequency fluctuations, while the battery is responsible for the low-frequency fluctuations, thereby ensuring that the energy storage system can effectively respond to various power fluctuations, obtaining the power distribution of different energy storage devices, and achieving effective smoothing of wind power fluctuations.

[0020] Figure 1 is a flowchart of the power smoothing method for the port wind-storage combined system provided by the embodiments of the present disclosure. Refer to Figure 1 , which specifically includes the following steps: In step S11, the first degree of fluctuation of the original wind power and the second degree of fluctuation of the predicted wind power are obtained, and the filtering weight of the original wind power is dynamically adjusted based on the first degree of fluctuation and the second degree of fluctuation.

[0021] In the embodiments of the present disclosure, it is possible to determine the established wind-storage combined system. Generally, the wind-storage combined system consists of a wind turbine, a supercapacitor, a battery pack, a DC-DC converter, a DC-AC inverter, and a transformer.

[0022] The present disclosure obtains the original wind power based on a wind turbine unit and determines the fluctuation degree of the original wind power; obtains the predicted wind power based on a pre-constructed wind power prediction model and determines the fluctuation degree of the predicted wind power. For the convenience of distinction, the present disclosure determines the fluctuation degree of the original wind power as the first fluctuation degree and refers to the fluctuation degree of the predicted wind power as the second fluctuation degree.

[0023] Among them, the fluctuation degree includes the fluctuation frequency and amplitude, which are used to determine the standard deviation of the grid-connected wind power and its predicted wind power, and the standard deviation of the grid-connected wind power and its predicted wind power is used to determine the filtering weight of the original wind power.

[0024] The filtering weight is used to adjust the filtering ratio for filtering the original wind power, and the filtering includes moving average filtering and anti-pulse interference filtering.

[0025] In step S12, based on the filtering weight, the filtering ratio is adjusted to filter the original wind power, and the power fluctuation component that needs to be smoothed by the wind-storage combined system is determined.

[0026] In the present disclosure, according to the filtering ratio, the original wind power is filtered by using moving average filtering and anti-pulse interference filtering, and the original wind power can be decomposed into two components, namely, the grid-connected wind volume that can be directly incorporated into the wind power storage system and the power fluctuation component that needs to be smoothed by the wind-storage combined system.

[0027] In step S13, an adaptive frequency division empirical mode decomposition method including local mean and optimization operator is used to decompose the power fluctuation component to determine multiple modal power components with different frequencies.

[0028] In the embodiment of the present disclosure, it is proposed to improve the adaptive frequency division empirical mode decomposition method by calculating the local mean and the optimization operator, so that based on the improved adaptive frequency division empirical mode decomposition method, the power fluctuation component is decomposed to determine multiple modal power components with different frequencies.

[0029] Among them, the optimization operator can be a mathematical tool or function, which is used to improve the defects of the traditional noise-assisted decomposition method, reduce mode mixing and noise residue, so as to improve the accuracy and stability of signal decomposition.

[0030] In step S14, the frequency division frequency is determined, and the capacitor of the wind-storage combined system is used to suppress the modal power component with a frequency greater than the frequency division frequency, and the battery of the wind-storage combined system is used to suppress the modal power component with a frequency less than the frequency division frequency.

[0031] In the embodiments of the present disclosure, an appropriate frequency division frequency can be determined according to the instantaneous frequency of the modal power component, so that the capacitor of the wind-storage combined system is used to suppress the modal power component with a frequency greater than the frequency division frequency, and the battery of the wind-storage combined system is used to suppress the modal power component with a frequency less than the frequency division frequency.

[0032] Through the present disclosure, the filtering ratio can be adjusted based on the determined filtering weights to filter the original wind power, and the power fluctuation component that needs to be suppressed by the wind-storage combined system can be determined, so as to reduce unnecessary power output, which helps to reduce the configuration capacity of the energy storage system, save investment costs, and improve the energy utilization efficiency of the energy storage system. Further, the adaptive frequency division empirical mode decomposition method including local mean and optimization operator is used to decompose the power fluctuation component to determine multiple modal power components with different frequencies, which can better reasonably allocate the power fluctuation component, thereby improving the operation efficiency and stability of the wind farm. Determine the frequency division frequency, use the capacitor of the wind-storage combined system to suppress the modal power component with a frequency greater than the frequency division frequency, and use the battery of the wind-storage combined system to suppress the modal power component with a frequency less than the frequency division frequency. Furthermore, it can be understood that by allocating signals in different frequency bands to the most suitable energy storage device, the overall performance of the energy storage system can be improved, the service life of the device can be extended, the output stability of the wind-storage combined system can be improved, and the operation cost of the system can be reduced; better reasonably allocate the power fluctuation component, thereby improving the operation efficiency and stability of the wind farm.

[0033] The embodiments of the present disclosure can construct a wind power prediction model based on the extreme learning machine model, and use the wind power prediction model to predict the wind power, so as to determine the weight value of dynamic weight filtering according to the predicted wind power and the original wind power.

[0034] Figure 2 is the flowchart of the wind power prediction method provided by the embodiments of the present disclosure. See Figure 2 Among them, the implementation manner of constructing a wind power prediction model based on the extreme learning machine model (Extreme Learning Machine, ELM) and predicting the wind power is as follows: First, ELM needs to be trained. Therefore, a wind power historical data set can be obtained. The wind power historical data set includes environmental factor characteristics such as wind speed, wind direction, humidity, and temperature input to the extreme learning machine model, as well as the corresponding output wind power. And the wind power historical data set is divided into a training data set and a test data set according to a ratio of 9:1.

[0035] The wind power historical data set is normalized, and the normalization formula is as shown in the following formula.

[0036]

[0037] In the formula, g is the normalization processing of the historical wind power dataset; is the i th historical wind power data is the minimum value in the historical wind power dataset; is the maximum value in the historical wind power dataset.

[0038] The purpose of data normalization processing is to eliminate the unit differences between different types of data, thereby improving the efficiency of model learning.

[0039] After that, the parameters of ELM can be configured, and then the training of ELM can be started. In this disclosure, 90% of the historical wind power dataset is selected to form the training dataset. Through continuous iterative training, it is judged whether the extreme learning machine model meets the pre-set performance criteria. When the model reaches the preset criterion, ELM is tested. If not, the model continues to be trained.

[0040] Furthermore, based on the divided test dataset, ELM can be tested and the predicted value of wind power can be obtained. The predicted value is used to test the prediction performance of ELM, and the accuracy of the model is evaluated by calculating the error between the predicted value and the actual value.

[0041] After the training of ELM is completed, a wind power prediction model that can be used to predict wind power is finally obtained, and then the wind power can be predicted based on the wind power prediction model.

[0042] In the embodiments of this disclosure, the first grid-connected power obtained by filtering the original wind power based on moving average filtering can be obtained. Further, the moving average filtering uses a specific sliding window to smooth the data within a certain time period, and represents any data point by calculating the arithmetic mean of adjacent data within the window. As the output data changes, the sliding window will be updated accordingly. The characteristic of the fixed window size makes a set of data averages recalculated each time it slides. Therefore, the moving average filtering technology can effectively represent the value at the center point of the time window. The moving average filtering smooths the time series data by calculating the average value of the data points within the sliding window. The window moves with time, moving one data point each time, and updating the corresponding average value at the same time. Among them, the calculation formula for determining the grid-connected power by moving average filtering is as follows:

[0043] In the formula, N is the time step; is the first grid-connected power after moving average filtering; is the original wind power, tis time.

[0044] It is also possible to obtain a second grid-connected power obtained by filtering the original wind power based on anti-impulse interference average filtering, and anti-impulse interference average filtering can also be referred to as the median average filtering method. Further, anti-impulse interference average filtering first obtains the original wind power. Each time M points are selected from the original wind power, and the maximum value and the minimum value are removed to reduce the influence of accidental impulse interference. Subsequently, arithmetic mean processing is performed on the filtered data to obtain a set of relatively smooth output data. This method effectively reduces the noise components in the wind power signal and improves the accuracy and reliability of the wind power output power data.

[0045] The calculation formula for anti-impulse interference average filtering to determine the grid-connected power is as follows:

[0046] In the formula, M is the number of data points intercepted from the original wind power sequence, is from the current time t backtrack M wind power original power value at the sampling points, is from the current time t backtrack M +1 wind power original power value at the sampling points is the second grid-connected power after anti-impulse interference average filtering; is the maximum value in the original wind power, is the minimum value in the original wind power.

[0047] Thus, the present disclosure can obtain the original wind power and the wind power prediction power, as well as the first grid-connected power obtained by filtering the original wind power based on moving average filtering and the second grid-connected power obtained by filtering the original wind power based on anti-impulse interference average filtering, and calculate the standard deviation of the original wind power and the standard deviation of the wind power prediction power.

[0048] Among them, the calculation formula for calculating the standard deviation of the original wind power is as follows:

[0049] In the formula, is the standard deviation of the original wind power, n is the number of wind power data; is the average value of the wind power grid-connected power. When calculating the standard deviation of the original wind power, is the wind power grid-connected power. When calculating the standard deviation of the wind power prediction power is the wind power prediction power.

[0050] Further, the first weight of the first grid-connected power and the second weight of the second grid-connected power can be calculated according to the calculated standard deviation of the original wind power and the standard deviation of the wind power prediction power.

[0051] In the present disclosure, taking the calculation of the first weight for grid-connected power by calculating the moving average filter as an example, the calculation method of the first weight is shown in the following formula.

[0052]

[0053] In the formula, is the standard deviation of the wind power prediction power, θ is the first weight, h 1 is the upper limit of the standard deviation of the wind power, h 2 is the lower limit of the standard deviation of the wind power, .

[0054] Among them, the sum of the first weight and the second weight is 1.

[0055] In the present disclosure, based on the first grid-connected power and the first weight, the first grid-connected component incorporated into the wind-storage combined system is determined; based on the second grid-connected power and the second weight, the second grid-connected component incorporated into the wind-storage combined system is determined; the sum of the first grid-connected component and the second grid-connected component is determined as the grid-connected power of the wind-storage combined system. Among them, the original wind power is directly incorporated into the grid-connected power of the storage combined system as follows:

[0056] In the formula, is the grid-connected power. is the first grid-connected power, is the second grid-connected power.

[0057] Through the filtering method proposed in this application for dynamically adjusting the hybrid filtering ratio according to the filtering weight, the original wind power is adjusted to determine the grid-connected power of the wind-storage combined system, which can reduce the unnecessary output of the energy storage system. The reduced unnecessary output not only helps to reduce the configuration capacity of the energy storage system, thereby saving investment costs, but also can improve the energy utilization efficiency of the energy storage system. In addition, the application of dynamically adjusting the hybrid filtering ratio can also enhance the stability and reliability of the wind power system, reduce the system risks caused by power fluctuations, and is of great significance for optimizing the operation of the wind-storage combined system, improving the economic and technical benefits of the energy storage system.

[0058] In the present disclosure, after determining the power fluctuation component, the adaptive frequency division empirical mode decomposition method is further improved based on the local mean and the optimization operator, so as to decompose the power fluctuation component according to the improved adaptive frequency division empirical mode decomposition method, and its implementation manner is as follows in the following embodiments.

[0059] Figure 3 is the flow chart of the power fluctuation component decomposition method provided by the embodiment of the present disclosure. See Figure 3, it is first necessary to determine the power fluctuation signal, signal-to-noise ratio, and the number of times white noise is added for the power fluctuation component. Further optimize the added white noise according to the optimization operator and the signal-to-noise ratio, and then use the empirical mode decomposition method to calculate and estimate the sum of the first white noise and the power fluctuation signal to determine the local power mean.

[0060] The calculation formula for the local power mean is as follows:

[0061] In the formula, is the local power mean of the i th power fluctuation signal, is the power fluctuation signal, is the signal-to-noise ratio, is the white noise added for the i th time, is the first optimization operator, t is the time.

[0062] Among them, the calculation formula for the signal-to-noise ratio is as follows:

[0063] In the formula, is the reciprocal of the signal-to-noise ratio.

[0064] Finally, optimize the local power mean based on the second optimization operator to determine the first power residual, and decompose the power fluctuation component based on the local power mean and the first power residual to determine multiple modal power components of different frequencies.

[0065] Among them, the calculation formula for the first power residual is as follows:

[0066] In the formula, r 1 is the first power residual, N is the second optimization operator.

[0067] For the convenience of description in this disclosure, the difference between the power fluctuation signal and the first power residual is determined as the first modal power component. Among them, the calculation formula for the first modal power component is as follows:

[0068] In the formula, IMF1 is the first modal power component.

[0069] Furthermore, optimize the sum of the first power residual and the first white noise according to the second optimization operator to determine the second power residual, and determine the difference between the first power residual and the second power residual as the second modal power component.

[0070] Among them, the calculation formula of the second power residual is as follows:

[0071] In the formula, r $\\xi_2$ is the second power residual, $SNR_2$ is the signal-to-noise ratio corresponding to the second addition of white noise, $O_1^2$ is the first optimization operator corresponding to the second addition of white noise.

[0072] Therefore, the calculation formula of the second modal power component is as follows:

[0073] In the formula, $IMF_2$ is the second modal power component.

[0074] Furthermore, the white noise added for the second time is optimized based on the first optimization operator and the signal-to-noise ratio to obtain the second white noise; The sum of the second power residual and the second white noise is optimized according to the second optimization operator to determine the third power residual, and the difference between the second power residual and the third power residual is determined as the third modal power component. The decomposition of the power fluctuation component is stopped until the number of modal power components is the same as the number of times of adding white noise, and multiple modal power components with different frequencies are determined.

[0075] In summary, the calculation formula of the $k$-th residual is as follows:

[0076] In the formula, $\\xi_k$ is the k $k$-th power residual, $\\xi_{k - 1}$ is the k $(k - 1)$-th power residual, $SNR_{k + 1}$ is the signal-to-noise ratio corresponding to the k $(k + 1)$-th addition of white noise, $SNR_k$ is the signal-to-noise ratio corresponding to the $k$-th addition of white noise, $O_1^k$ is the first optimization operator corresponding to the k $k$-th addition of white noise, k $k = 3, \\cdots$, K $n$.

[0077] Then, the calculation formula of the k $k$-th modal modulus component is as follows:

[0078] By calculating the local power mean in the present disclosure, the residual noise level in the decomposed mode can be effectively reduced, thereby significantly improving the accuracy and clarity of signal processing. Adding the optimization operator can reduce the white noise overlap phenomenon in the power residual.

[0079] In the present disclosure, after decomposing the power fluctuation component into multiple modal power components, the instantaneous frequency of the modal frequency component is further extracted through the Hilbert Transform (HT), and the correlation relationship between the instantaneous frequency and time is obtained.

[0080] In the present disclosure, the power fluctuation signal is as follows:

[0081] where is the power fluctuation signal, is the Hilbert transform of

[0082] For the modal component IMF i ( t ), the present invention adopts the Hilbert transform to obtain the corresponding H [IMFk( t )]:

[0083] Construct the analytic signal of each modal component, that is:

[0084] where is the k-th analytic signal, is the instantaneous value of the amplitude of each IMF; is the instantaneous value of the phase of each IMF; is the instantaneous value of the frequency of each IMF, τ is the actual time, and finally the Hilbert time-frequency spectrum represented in polar coordinate form H[ω, t] is obtained, j where i is the imaginary unit.

[0085] Through the curve on the time-frequency spectrum, the variation law of the power fluctuation signal with time and frequency in the entire frequency band can be observed, and the specific law is as follows:

[0086] According to the determined variation law of the power fluctuation signal with time and frequency in the entire frequency band, the correlation relationship between the instantaneous frequency of the modal power component and time is determined.

[0087] Furthermore, according to the maximum and minimum values of the instantaneous frequency, a frequency interval is determined, a temporary frequency division frequency is determined within the frequency interval, and based on the temporary frequency division frequency and the correlation relationship, the energy of the aliased modal power between adjacent modal power components is calculated, and the temporary frequency division frequency corresponding to the minimum value of the energy is determined as the allocated frequency.

[0088] Further, multiple temporary frequency division frequencies can be determined within the frequency range based on a preset frequency interval, and the time corresponding to each of the temporary frequency division frequencies can be determined according to the association relationship. The modal power components corresponding to adjacent instantaneous frequencies are determined according to the temporary frequency division frequencies, and the aliased modal power of the adjacent modal power components is determined. Based on the time and the adjacent modal power components, the energy of the aliased modal power is calculated.

[0089] In the present disclosure, the frequency interval can be 0.000001 Hz.

[0090] Wherein, the calculation formula for the energy of the aliased modal power between adjacent modal power components is as follows:

[0091] In the formula, is the energy of the aliased modal power between adjacent modal power components, is the h th power greater than ; is the l th power less than , is the temporary frequency division frequency, is the time difference.

[0092] Combined with the context, after determining the frequency division frequency, the modal power components of different frequencies can be distributed and suppressed. Figure 4 is the flowchart of the power fluctuation component suppression method provided by the embodiment of the present disclosure. Refer to Figure 4 , read the original wind power, and perform predictive analysis on the wind power through the ELM model to determine the predicted wind power. Then, the original wind power is decomposed by using the dynamic weight filtering method to determine the grid-connected power directly incorporated into the system and the power fluctuation components that need to be suppressed by the energy storage system. Further, the power fluctuation components are adaptively frequency-divided and empirically mode decomposed, so that based on the determined frequency division frequency, the modal power components with frequencies greater than the frequency division frequency are suppressed by controlling the capacitor based on the energy router of the wind-storage combined system, and the modal power components with frequencies less than the frequency division frequency are suppressed by controlling the battery based on the energy router of the wind-storage combined system.

[0093] The following embodiments are the specific implementation manners of the present application.

[0094] Exemplarily, Figure 5 is the topological structure diagram of the energy router provided by the embodiment of the present disclosure, as shown in Figure 5As shown in the figure, the energy router of the port wind and energy storage combined system is composed of multiple DC-DC converters (DC / DC), multiple DC-AC converters (DC / AC), and an intelligent energy storage (VSG) controller. By controlling devices such as wind turbines and energy storage, the power smoothing strategy of the present disclosure is realized. Further, the DC-DC converter and the DC-AC converter are used to output the AC voltage after the power smoothing of the port wind and energy storage combined system; the intelligent energy storage controller is used. This energy router includes 4 interfaces, namely 2 wind power generation interfaces, 1 energy storage device interface, and 1 grid connection interface. One end of multiple DC / DCs is respectively connected to the port wind power source interface and the hybrid energy storage power source interface. In the present disclosure, it is used to obtain the original wind power and the power smoothing output by the hybrid energy storage system. Its second ends are all connected in parallel to the DC bus to realize the grid connection of the voltage of the wind and energy storage combined system. Among them, the Energy Storage System (ESS) port controls the DC / DC to realize the adaptive regulation of the voltage and the bidirectional flow of energy. The VSG controller is respectively connected to the second ends of each DC / DC. By controlling the DC / DC connected to the port wind power to output a low voltage direct current (LVDC), it is possible to control the capacitor to suppress the modal power component with a frequency greater than the frequency division frequency, and control the battery to suppress the modal power component with a frequency less than the frequency division frequency. The DC / AC converter is an interface for AC devices such as AC loads and grid connection. The DC bus voltage outputs a high voltage alternating current (HVAC) through the DC / AC converter and is connected to the grid. Figure 6 is the topology structure diagram of the three-level converter of the energy router of the port wind and energy storage combined system provided by the embodiment of the present disclosure, as Figure 6 shown, the converter is a three-level DC / AC converter. The three-level DC / AC converter includes a DC part and an AC part; the DC part and the AC part are respectively composed of multiple triode common-emitter diode units connected in series and parallel. Among them, Sa1…Sa6, Sb1…Sb6, Sc1…Sc6 are all triode common-emitter diode units, which are applicable to the port scenario and can realize the stable operation of the energy router and the stable control of the wind and energy storage combined system.

[0095] Figure 7 is the structure diagram of the wind and energy storage combined system provided by the embodiment of the present disclosure, as Figure 7 shown, it mainly consists of a wind turbine, a super capacitor, a battery pack, a DC-DC converter, a DC-AC inverter, and a transformer. After the wind power grid-connected power and the energy storage output power are grid-connected, they enter the grid through a substation.

[0096] The present disclosure has pre-established an ELM model for predicting wind power, and predicts and analyzes wind power through the ELM model. In terms of data, the present disclosure selects the historical wind power data from September to November in 23 years at the Pacific Terminal of Tianjin Port as the input data for the ELM. The wind power data for 24 hours is predicted, with an interval of 15 minutes, and the prediction results are as Figure 8 shown, Figure 8 which is a schematic diagram of the predicted wind power provided by an embodiment of the present disclosure. The figure shows the true value of the wind power and the predicted value of the wind power prediction model. The predicted value curve obtained by the wind power prediction model during wind power prediction is highly fitted with the actual value curve, fully demonstrating that the model has excellent performance and accuracy in the field of wind power prediction.

[0097] Furthermore, the original wind power is adjusted according to the dynamic weight filtering method to reduce the noise component in the wind power signal and improve the accuracy and reliability of the wind power grid-connected power data.

[0098] Figure 9 which is the effect diagram of suppressing the fluctuation of the original wind power by dynamic weight filtering provided by an embodiment of the present disclosure. As Figure 9 shown, the fitting degree between the processed wind power and the original wind power directly connected to the grid is relatively high, and there is basically no time delay. When t = 11:20, the original wind power is 75.29 MW, and the wind power grid-connected power after moving average processing increases to 89.22 MW, which requires the hybrid energy storage system to release 13.93 MW of electricity to smooth the power fluctuation. While in Figure 8 it is shown that the grid-connected power after filtering processing drops to 81.08 MW. Correspondingly, the hybrid energy storage system only needs to release 5.79 MW of electricity to smooth the power fluctuation.

[0099] Table 1 shows the effects of different filtering methods on suppressing wind power fluctuations. Compared with the traditional moving average filtering method, the maximum volatility using dynamic weight filtering drops from 11.247% to 8.5914%. At the same time, the smoothness of the power fluctuation component drops from 3.6609% of the moving average filtering method to 1.3998% of the dynamic weight filtering.

[0100] Table 1

[0101] In summary, the dynamic weight filtering proposed by the present disclosure performs excellently in suppressing the fluctuation of the original wind power, especially for the suppression effect of long-time scale fluctuations is more significant, provides a more stable and reliable power output for wind power grid-connected operation, and helps to improve the safety and economy of the power grid.

[0102] Figure 10This is a schematic diagram of the power that needs to be smoothed by the hybrid energy storage system obtained by dynamic weight filtering according to an embodiment of the present disclosure. Figure 10 It can be seen that the reference output of the hybrid energy storage system shows frequent fluctuations near zero. If we only rely on traditional energy storage systems such as batteries to smooth out the fluctuations in wind power, then this frequent charging and discharging process will have a great impact on the service life of the energy storage system and significantly shorten its operating cycle. The introduction of power storage elements such as supercapacitors can effectively cope with high-frequency fluctuations in wind power. This can reduce the burden on the battery and reduce the number of times the battery is charged and discharged, thereby greatly extending the cycle life of the battery. The design of this hybrid energy storage system not only improves the working performance of the overall system, but also effectively avoids the loss of the battery caused by excessive charging and discharging processes, ensuring the reliable operation of the wind-storage combined system.

[0103] Based on the above implementation process, the adaptive frequency-division empirical mode decomposition method can be used to decompose the hybrid energy storage power, and the power fluctuation component of the energy storage is decomposed into modal functions of different frequencies, namely modal power components. The signal-to-noise ratio is set to 0.5, and the operators Q1(.) and N(.) are introduced to add 500 times of white noise as an example, and the iterative process is performed 5000 times. The decomposition results are shown in Figures 11 (a) to 11 (d), and Figures 11 (a) to (d) are modal power components and their spectrum diagrams provided by the embodiment of the present disclosure. The figure shows a total of 8 intrinsic mode functions from high frequency to low frequency, and the spectrum diagrams of these 8 intrinsic mode functions are obtained. The power fluctuation component is decomposed into 8 parts within the working frequency band of 0~1Hz, so that the power fluctuation can be effectively decomposed into different frequency bands, and each IMF corresponds to a specific frequency range. This method can better allocate the power fluctuation components reasonably, thereby improving the operating efficiency and stability of the wind farm.

[0104] After the power fluctuation component of wind power is processed by Hilbert transform, eight IMFs are extracted from the original data, and the Hilbert spectrum of each IMF is obtained, so as to gain a deeper understanding of the dynamic correlation between instantaneous frequency and time. Figure 12 is the instantaneous frequency-time curve of the modal power component provided by the embodiment of the present disclosure. Figure 12 As shown in Figure 2, the Hilbert spectrum shows the main operating frequency band of the hybrid energy storage system. The energy storage system is most active in this frequency band and undertakes most of the energy storage and release tasks. Figure 12, it can be found that the two curves of IMF3 and IMF4 remain clearly separated throughout almost the entire time period without aliasing. This indicates that the signals they represent are independent of each other in terms of frequency and time without mutual interference. Therefore, the present invention selects k = 4 as the power frequency division point of the hybrid energy storage to reconstruct the power of the hybrid energy storage system. The selection of this frequency division point not only helps improve the efficiency of energy management but also helps ensure the stable operation of the energy storage system in different working frequency bands. Based on the above embodiments, the present disclosure uses dynamic weight filtering and adaptive frequency division empirical mode decomposition to not only improve the response speed of the energy storage system but also optimize the distribution and utilization of energy storage. By allocating the modal power component signals in different frequency bands to the most suitable energy storage devices, the overall performance of the energy storage system can be improved, the service life of the devices can be extended, and the operating cost of the system can be reduced at the same time.

[0105] The present disclosure processes the decomposed IMF components according to the wind power grid connection fluctuation limit. The high-frequency components are suppressed by the energy storage system, and the suppression control strategy is realized by the control of the four-port port wind-storage combined system energy router, so as to achieve the stable output of wind power and the healthy and economic operation of the energy storage device. Its operating conditions can be seen in Figure 13 and Figure 14 . Figure 13 is the wind power suppression fluctuation effect diagram provided by the embodiment of the present disclosure. Figure 14 is the schematic diagram of the operating conditions of the energy storage system provided by the embodiment of the present disclosure. It can be obtained from Figure 12 that most of the fluctuation components are effectively absorbed and compensated by the energy storage system. Compared with before suppression, the situation of wind power fluctuation exceeding the limit after suppression has decreased. It can be seen from Figure 13 that the use of adaptive frequency division empirical mode decomposition makes the change in the depth of discharge (DOD) of the battery relatively gentle, and the change range of the state of charge of the battery decreases. This is mainly because the supercapacitor undertakes more power fluctuations, and the charging and discharging processes of the battery are more stable, effectively reducing the overcharging and over-discharging phenomena of the battery. The service life of the battery is improved, and the absorption capabilities of the supercapacitor and the battery are better matched and utilized. Since the adaptive frequency division empirical mode decomposition can decompose the energy signal more finely, the energy storage system can more accurately determine the energy distribution between the supercapacitor and the battery, making the synergy between the two closer.

[0106] Based on the same principle as the method shown in Figure 1 , the present disclosure also provides a port wind-storage combined system power suppression system. Figure 15 is the schematic diagram of the port wind-storage combined system power suppression system provided by the embodiment of the present disclosure. Refer to Figure 15 , the port wind-storage combined system power suppression system 1500 may include: An adjustment module 1501 is configured to obtain a first fluctuation degree of the original wind power and a second fluctuation degree of the predicted wind power, and dynamically adjust a filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree; a determination module 1502 is configured to adjust a filtering ratio based on the filtering weight, filter the original wind power, and determine a power fluctuation component that needs to be suppressed by a wind-storage combined system; an adaptive frequency division empirical mode decomposition method including a local mean and an optimization operator is used to decompose the power fluctuation component to determine multiple modal power components with different frequencies; a suppression module 1503 is configured to suppress the modal power components with frequencies greater than the frequency division frequency by using a capacitor of the wind-storage combined system, and suppress the modal power components with frequencies less than the frequency division frequency by using a battery of the wind-storage combined system.

[0107] In the present disclosure, the adjustment module 1501 is configured to obtain a first grid-connected power obtained by filtering the original wind power based on a moving average filter, and a second grid-connected power obtained by filtering the original wind power based on an anti-pulse interference average filter; calculate a standard deviation of the original wind power based on the first fluctuation degree, and calculate a standard deviation of the predicted wind power based on the second fluctuation degree; calculate a first weight of the first grid-connected power and a second weight of the second grid-connected power based on the standard deviation of the original wind power and the standard deviation of the predicted wind power.

[0108] In the present disclosure, the determination module 1502 is configured to determine a first grid-connected component incorporated into the wind-storage combined system based on the first grid-connected power and the first weight; determine a second grid-connected component incorporated into the wind-storage combined system based on the second grid-connected power and the second weight; and determine the sum of the first grid-connected component and the second grid-connected component as the grid-connected power of the wind-storage combined system.

[0109] In the present disclosure, the determination module 1502 is configured to determine a power fluctuation signal, a signal-to-noise ratio, and the number of times of adding white noise of the power fluctuation component; optimize the white noise added for the first time based on the first optimization operator and the signal-to-noise ratio to obtain a first white noise; estimate the sum of the first white noise and the power fluctuation signal by using a local mean to determine a local power mean; optimize the local power mean based on a second optimization operator to determine a first power residual, and decompose the power fluctuation component based on the local power mean and the first power residual to determine multiple modal power components with different frequencies.

[0110] In the present disclosure, the determining module 1502 is configured to determine the difference between the power fluctuation signal and the first power residual as the first modal power component; optimize the sum of the first power residual and the first white noise according to the second optimization operator to determine a second power residual, and determine the difference between the first power residual and the second power residual as the second modal power component; optimize the white noise added for the second time based on the first optimization operator and the signal-to-noise ratio to obtain a second white noise; optimize the sum of the second power residual and the second white noise according to the second optimization operator to determine a third power residual, and determine the difference between the second power residual and the third power residual as the third modal power component; until the number of modal power components is the same as the number of times of adding white noise, stop decomposing the power fluctuation air volume, and determine multiple modal power components with different frequencies.

[0111] In the present disclosure, the determining module 1502 is configured to extract the instantaneous frequency of each modal power component and determine the correlation between the instantaneous frequency and time; determine a frequency interval according to the maximum and minimum values of the instantaneous frequency; determine a temporary frequency division frequency within the frequency interval, and calculate the energy of the aliased modal power between adjacent modal power components based on the temporary frequency division frequency and the correlation; determine the temporary frequency division frequency corresponding to the minimum value of the energy as the frequency division frequency.

[0112] In the present disclosure, the determining module 1502 is configured to determine multiple temporary frequency division frequencies within the frequency interval based on a preset frequency interval, and determine the time corresponding to each temporary frequency division frequency according to the correlation; determine the modal power components corresponding to adjacent instantaneous frequencies according to the temporary frequency division frequency, and determine the aliased modal power of the adjacent modal power components; calculate the energy of the aliased modal power based on the time and the adjacent modal power components.

[0113] In the present disclosure, the energy router of the wind-storage combined system is composed of multiple DC-DC converters, multiple DC-AC converters, and an intelligent energy storage controller; one end of each DC-DC converter is respectively connected to the port wind power and the hybrid energy storage, and is used to obtain the original wind power and the power output by the hybrid energy storage system, and the other ends are all connected in parallel to the DC bus; One end of each DC-AC converter is connected in parallel to the DC bus, and the other end is connected in parallel to the power grid, and is used to output the AC voltage after the port wind-storage combined system suppresses the power; the intelligent energy storage controller is used to control the capacitor to suppress the modal power components with frequencies greater than the frequency division frequency, and control the battery to suppress the modal power components with frequencies less than the frequency division frequency.

[0114] In the present disclosure, the DC-AC converter is a three-level DC-AC converter; the three-level DC-AC converter includes a DC part and an AC part; the DC part and the AC part are respectively formed by connecting a plurality of transistor common-emitter diode units in series and parallel.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A power smoothing method for a port wind and energy storage combined system, characterized in that, Including the following steps: Obtain the first fluctuation degree of the original wind power and the second fluctuation degree of the predicted wind power, and dynamically adjust the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree; Adjust the filtering ratio based on the filtering weight, filter the original wind power, and determine the power fluctuation component that needs to be suppressed by the wind-storage combined system; Use the adaptive frequency division empirical mode decomposition method including local mean and optimization operator to decompose the power fluctuation component and determine multiple modal power components with different frequencies; Determine the frequency division frequency, and based on the energy router of the wind-storage combined system, control the capacitor to suppress the modal power component with a frequency greater than the frequency division frequency, and based on the energy router of the wind-storage combined system, control the battery to suppress the modal power component with a frequency less than the frequency division frequency.

2. A method for power smoothing of a port wind and energy storage combined system according to claim 1, characterized in that The dynamically adjusting the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree includes: Obtain the first grid-connected power obtained by filtering the original wind power based on moving average filtering, and the second grid-connected power obtained by filtering the original wind power based on anti-pulse interference average filtering; Calculate the standard deviation of the original wind power based on the first fluctuation degree, and calculate the standard deviation of the predicted wind power based on the second fluctuation degree; Calculate the first weight of the first grid-connected power and the second weight of the second grid-connected power based on the standard deviation of the original wind power and the standard deviation of the predicted wind power.

3. A power smoothing method for a port wind and energy storage combined system according to claim 2, characterized in that, After dynamically adjusting the filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree, the method further includes: Determine the first grid-connected component incorporated into the wind-storage combined system based on the first grid-connected power and the first weight; Determine the second grid-connected component incorporated into the wind-storage combined system based on the second grid-connected power and the second weight; Determine the sum of the first grid-connected component and the second grid-connected component as the grid-connected power of the wind-storage combined system.

4. A power smoothing method for a port wind and energy storage combined system according to claim 1, characterized in that, The optimization operator includes a first optimization operator and a second optimization operator; The using the adaptive frequency division empirical mode decomposition method including local mean and optimization operator to decompose the power fluctuation component and determine multiple modal power components with different frequencies includes: Determine the power fluctuation signal, signal-to-noise ratio, and the number of times of adding white noise of the power fluctuation component; Optimize the white noise added for the first time based on the first optimization operator and the signal-to-noise ratio to obtain the first white noise; Use the local mean to estimate the sum of the first white noise and the power fluctuation signal, and determine the local power mean; Optimize the local power mean based on the second optimization operator to determine the first power residual, and decompose the power fluctuation component based on the local power mean and the first power residual to determine multiple modal power components with different frequencies.

5. A method for power smoothing of a port wind-storage combined system according to claim 4, characterized in that, The decomposing the power fluctuation component based on the local power mean and the first power residual to determine multiple modal power components with different frequencies includes: Determine the difference between the power fluctuation signal and the first power residual as the first modal power component; Optimize the sum of the first power residual and the first white noise according to the second optimization operator to determine the second power residual, and determine the difference between the first power residual and the second power residual as the second modal power component; Optimize the white noise added for the second time based on the first optimization operator and the signal-to-noise ratio to obtain the second white noise; Optimize the sum of the second power residual and the second white noise according to the second optimization operator to determine the third power residual, and determine the difference between the second power residual and the third power residual as the third modal power component; Stop decomposing the power fluctuation component until the number of modal power components is the same as the number of times white noise is added, and determine multiple modal power components with different frequencies.

6. A method for power smoothing of a port wind and energy storage combined system according to claim 1, characterized in that, The determining of the frequency division frequency includes: Extract the instantaneous frequency of each modal power component and determine the correlation between the instantaneous frequency and time; Determine the frequency interval according to the maximum and minimum values of the instantaneous frequency; Determine the temporary frequency division frequency within the frequency interval, and calculate the energy of the aliased modal power between adjacent modal power components based on the temporary frequency division frequency and the correlation; Determine the temporary frequency division frequency corresponding to the minimum value of the energy as the frequency division frequency.

7. A power smoothing method for a port wind and storage combined system according to claim 6, characterized in that The determining of the temporary frequency division frequency within the frequency interval and calculating the energy of the aliased modal power between adjacent modal power components based on the temporary frequency division frequency and the correlation includes: Determine multiple temporary frequency division frequencies within the frequency interval based on a preset frequency interval, and determine the time corresponding to each temporary frequency division frequency according to the correlation; Determine the modal power components corresponding to adjacent instantaneous frequencies according to the temporary frequency division frequency, and determine the aliased modal power of adjacent modal power components; Calculate the energy of the aliased modal power based on the time and adjacent modal power components.

8. A power smoothing method for a port wind and energy storage combined system according to claim 1, characterized in that, The wind-storage integrated system energy router is composed of multiple DC-DC converters, multiple DC-AC converters and an intelligent energy storage controller; One end of the DC-DC converter is respectively connected to the port wind power and the hybrid energy storage, and is used to obtain the original wind power and the power output by the hybrid energy storage system, and the other ends are all connected in parallel to the DC bus; One end of the DC-AC converter is all connected in parallel to the DC bus, and the other end is all connected in parallel to the power grid, and is used to output the AC voltage after the port wind-storage integrated system suppresses the power; The intelligent energy storage controller is used to control the capacitor to suppress the modal power components with frequencies greater than the frequency division frequency, and control the battery to suppress the modal power components with frequencies less than the frequency division frequency.

9. A power smoothing method for a port wind and energy storage combined system according to claim 8, characterized in that, The DC-AC converter is a three-level DC-AC converter; The three-level DC-AC converter includes a DC part and an AC part; The DC part and the AC part are respectively composed of multiple transistor common-emitter diode units connected in series and parallel.

10. A power smoothing system for a port wind and energy storage combined system, characterized in that, The system includes: An adjustment module, configured to obtain a first fluctuation degree of the original wind power and a second fluctuation degree of the predicted wind power, and dynamically adjust a filtering weight of the original wind power based on the first fluctuation degree and the second fluctuation degree; A determination module, configured to adjust a filtering ratio based on the filtering weight, filter the original wind power, and determine a power fluctuation component that needs to be smoothed by a wind energy storage combined system; adopt an adaptive frequency division empirical mode decomposition method including a local mean and an optimization operator to decompose the power fluctuation component, and determine a plurality of modal power components with different frequencies; A smoothing module, configured to determine a frequency division frequency, and based on an energy router of the wind energy storage combined system, control a capacitor to smooth the modal power component with a frequency greater than the frequency division frequency, and based on the energy router of the wind energy storage combined system, control a storage battery to smooth the modal power component with a frequency less than the frequency division frequency.

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