A primary frequency modulation control method of a new energy station considering power grid fault characteristics

Abstract

The application discloses a kind of new energy station primary frequency modulation control method considering power grid failure characteristics, it is related to smart grid technical field, including, the operating data of power grid is collected, and it is pretreated;Based on historical power grid operating data and failure data, a failure prediction model is constructed, the data after pretreatment is input into the failure prediction model, to generate prediction results;According to the prediction result, the state of charge of energy storage device is adjusted;The state of charge of energy storage device after adjustment and real-time acquisition power grid operating data are combined, and the failure characteristic information is extracted by analyzing real-time monitoring data;Based on the state of charge of energy storage device after adjustment and failure characteristic information, the frequency modulation response priority of energy storage device is dynamically allocated, to generate the instantaneous frequency modulation power instruction of energy storage device;According to the instantaneous frequency modulation power instruction, the power output of energy storage device is dynamically adjusted.The application generates instantaneous frequency modulation power instruction according to the frequency modulation response priority of energy storage device calculated, and accurately carries out frequency modulation.

Description

Technical Field

[0001] This invention relates to the field of smart grid technology, and in particular to a primary frequency regulation control method for new energy power plants that takes into account the characteristics of grid faults. Background Technology

[0002] With the rapid development of renewable energy power generation technology, the proportion of new energy power plants in the power system is increasing day by day. Due to the intermittent and random nature of wind and solar renewable energy, their large-scale integration has brought new challenges to the stability of grid frequency. Traditional primary frequency regulation control methods mainly rely on the mechanical inertia of generators to respond to frequency deviations. This control method faces huge challenges when facing the high penetration of new energy.

[0003] In the field of smart grids, existing frequency regulation control strategies often lack consideration for grid fault characteristics. The frequency regulation response of energy storage devices is not timely and cannot fully play their regulatory role. Current frequency regulation control methods usually use simple weight allocation or linear functions to calculate the frequency regulation power command of energy storage devices. This method is difficult to adapt to complex grid operation conditions and variable fault characteristics. The management of the state of charge of energy storage devices is also relatively crude and fails to make full use of information to optimize the frequency regulation behavior of energy storage devices. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults, which solves the problem of untimely and inaccurate frequency regulation response of energy storage equipment due to the lack of consideration of the characteristics of power grid faults.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults, comprising: collecting power grid operation data and preprocessing it;

[0008] A fault prediction model is constructed based on historical power grid operation data and fault data. The preprocessed data is then input into the fault prediction model to generate prediction results.

[0009] Adjust the state of charge of energy storage devices based on the forecast results;

[0010] By combining the adjusted state of charge of the energy storage device with real-time power grid operation data, fault characteristic information is extracted from the real-time monitoring data.

[0011] Based on fault characteristic information and the adjusted state of charge of the energy storage device, the frequency regulation response priority of the energy storage device is dynamically allocated, and the instantaneous frequency regulation power command of the energy storage device is generated.

[0012] The power output of the energy storage device is dynamically adjusted according to the real-time frequency modulation power command.

[0013] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults as described in this invention, the method includes the following steps: collecting and preprocessing the power grid's operating data.

[0014] The collected power grid operation data includes frequency deviation, frequency change rate, and voltage fluctuation. The collected power grid operation data is then filtered and outlier removed.

[0015] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults as described in this invention, the method includes the following steps: constructing a fault prediction model based on historical power grid operation data and fault data.

[0016] Time-series alignment of historical power grid operation data and fault data;

[0017] The preprocessed power grid operation data is segmented into fixed time windows aligned with the time series, and feature vectors are extracted from the data within each time window.

[0018] A fault prediction model was constructed using a time series model based on a long short-term memory network.

[0019] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in this invention, the method involves the following steps: inputting preprocessed data into a fault prediction model to generate prediction results.

[0020] The preprocessed feature vectors are output to the fault prediction model to obtain the power grid fault prediction result, expressed as follows:

[0021]

[0022] Among them, Q f (t) represents the predicted power grid fault value at time t, σ represents the output conversion value, X represents the eigenvector, and H represents the eigenvector. j (t) represents the state of the j-th unit in the output at time t, w j Let be the weight coefficient of the state of the j-th unit, b be the bias term for adjusting the baseline level, α be the bias term for adjusting the baseline level, and T be... k (t) represents the value of the state of the k-th unit after a nonlinear transformation, z k v represents the weight coefficient for the state of the k-th unit. lLet be the weight coefficient of the l-th feature in the feature vector X, j be the index of the output unit, l be the index of the nonlinear transformation parameter in the feature vector X, N be the dimension of the feature vector X, and F be the weight coefficient of the l-th feature. l (X) represents the nonlinear transformation of the l-th feature of the feature vector X, and K represents the number of output units.

[0023] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in this invention, the method for adjusting the state of charge (SOC) of energy storage devices based on prediction results includes the following steps: obtaining the current SOC of the energy storage devices, and calculating the SOC based on the current grid operating status and fault prediction values.

[0024] Head

[0025] The standard charge state expression is as follows:

[0026]

[0027] Among them, S t+1 S represents the target state of charge of the energy storage device at time t+1. t Let η be the state of charge of the energy storage device at the current time t, and t be the adjustment coefficient.

[0028] Adjust the state of charge of the energy storage device according to the calculated target state of charge.

[0029] When S t+1 >S t When this happens, the charging process is initiated, changing the state of charge of the energy storage device from S... t Adjust to S t+1 Increase the charging power of the energy storage device until the state of charge reaches S. t+1 ;

[0030] When S t+1 >S t When this happens, the discharge process is initiated, changing the state of charge of the energy storage device from S... t Adjust to S t+1 Increase the discharge power of the energy storage device until the state of charge reaches S. t+1 ;

[0031] When S t+1 =S t When the charge state remains unchanged, the current charge state is maintained.

[0032] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in this invention, the method includes the following steps: combining the adjusted state of charge of the energy storage equipment with real-time collected grid operation data to analyze and extract fault characteristic information from the real-time monitoring data.

[0033] The adjusted state of charge of the energy storage device is combined with real-time data on grid operating frequency deviation, frequency change rate, and voltage fluctuation. A nonlinear function is used to fuse these data to generate a comprehensive fault characteristic value, expressed as follows:

[0034]

[0035] Among them, O fault GΔ(t) represents the fault characteristic value at time point t, and GΔ(t) represents the frequency deviation at time point t.

[0036] U df (t) represents the rate of change of frequency at time t, R V (t) represents the voltage fluctuation at time point t, and β1 is the adjustment coefficient.

[0037] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in this invention, the following steps are included: dynamically allocating the frequency regulation response priority of energy storage devices based on fault characteristic information and the adjusted state of charge of the energy storage devices, and generating real-time frequency regulation power commands for the energy storage devices.

[0038] The frequency regulation response priority of the energy storage device is calculated based on the fault characteristic values ​​and the adjusted state of charge of the energy storage device. The expression is as follows:

[0039] Z priority (t)=(m1·O fault (t)+m2)·(γ1·S t+1 +γ2·S t+1 );

[0040] Among them, Z priority (t) represents the frequency regulation response priority of the energy storage device at time t, m1 represents the influence of the control fault characteristic value on the frequency regulation response priority, m2 is a constant term, γ1 represents the influence of the control state of charge on the frequency regulation response priority, and γ2 represents the secondary influence of the control state of charge on the frequency regulation response priority.

[0041] Based on the calculated frequency regulation response priority, an instantaneous frequency regulation power command for the energy storage device is generated, expressed as follows:

[0042] A freq (t)=C max ·Z priority (t);

[0043] Among them, A freq (t) represents the instantaneous frequency modulation power command at time point t, C max This represents the maximum frequency regulation power of the energy storage device.

[0044] As a preferred embodiment of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in this invention, the method for dynamically adjusting the power output of energy storage devices according to real-time frequency regulation power commands includes the following steps:

[0045] Based on the real-time frequency modulation power command and the current power output, the power adjustment amount of the energy storage device is calculated, expressed as follows:

[0046] Δd(t)=A freq (t)-Y current (t);

[0047] Where ΔD(t) is the power adjustment at time point t, Y current (t) represents the current power output at time point t;

[0048] When ΔD(t)>0, it indicates that the power output of the energy storage device needs to be increased;

[0049] When ΔD(t) < 0, it indicates that the power output of the energy storage device needs to be reduced;

[0050] When ΔD(t) = 0, it means that the current power output already meets the frequency modulation requirements and no adjustment is needed.

[0051] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in the first aspect of the present invention.

[0052] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in the first aspect of the present invention.

[0053] The beneficial effects of this invention are as follows: By collecting power grid operation data, filtering and removing outliers from the collected power grid operation data improves the reliability and consistency of the data. The preprocessed feature vector is output to the fault prediction model to obtain the power grid fault prediction result, providing a reliable basis for frequency regulation control. Using the calculated power grid fault prediction value, the target state of charge is calculated based on the current power grid operation state and the fault prediction value, ensuring timely frequency regulation support in the event of a fault. The adjusted energy storage device status and voltage fluctuation data are combined to generate a comprehensive fault feature value. Multi-parameter fusion improves the comprehensiveness and accuracy of fault features. Based on the calculated frequency regulation response priority of the energy storage device, an instantaneous frequency regulation power command is generated to ensure fast and accurate frequency regulation in the event of a fault. Based on the instantaneous frequency regulation power command, the power output of the energy storage device is dynamically adjusted to ensure that frequency regulation requirements are met. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a flowchart of the primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults in Example 1.

[0056] Figure 2 This is a schematic diagram of the real-time frequency regulation power command flowchart of the primary frequency regulation control method for new energy power plants with grid fault characteristics in Example 1. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0060] Example 1, referring to Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides a primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults, including the following steps:

[0061] S1. Collecting and preprocessing the power grid's operational data includes the following steps:

[0062] The collected power grid operation data includes frequency deviation, frequency change rate, and voltage fluctuation. The collected power grid operation data is then filtered and outlier removed.

[0063] Furthermore, high-frequency noise was removed through filtering, making the data smoother and more stable, reducing the impact of noise on subsequent analysis. Outlier removal eliminated abnormal data caused by equipment failure, improving the reliability and consistency of the data.

[0064] S2. Constructing a fault prediction model based on historical power grid operation data and fault data includes the following steps.

[0065] Time-series alignment of historical power grid operation data and fault data;

[0066] The preprocessed power grid operation data is segmented into fixed time windows aligned with the time series, and feature vectors are extracted from the data within each time window.

[0067] A fault prediction model was constructed using a time series model based on a long short-term memory network.

[0068] Furthermore, by aligning time series data, the consistency of timestamps across all data is ensured, avoiding errors caused by time misalignment. By segmenting data into fixed time windows and extracting features, key features are extracted from each time window, improving the representativeness and information density of the data.

[0069] S3. Input the preprocessed data into the fault prediction model to generate prediction results, including the following steps.

[0070] The preprocessed feature vectors are output to the fault prediction model to obtain the power grid fault prediction result, expressed as follows:

[0071]

[0072] Among them, Q f (t) represents the predicted power grid fault value at time t, σ represents the output conversion value, X represents the eigenvector, and H represents the eigenvector. j (t) represents the state of the j-th unit in the output at time t, w jLet be the weight coefficient of the state of the j-th unit, b be the bias term for adjusting the baseline level, α be the bias term for adjusting the baseline level, and T be... k (t) represents the value of the state of the k-th unit after a nonlinear transformation, z k v represents the weight coefficient for the state of the k-th unit. l Let be the weight coefficient of the l-th feature in the feature vector X, j be the index of the output unit, l be the index of the nonlinear transformation parameter in the feature vector X, N be the dimension of the feature vector X, and F be the weight coefficient of the l-th feature. l (X) represents the nonlinear transformation of the l-th feature of the feature vector X, and K is the number of output units;

[0073] Furthermore, the feature vectors X within each time window are arranged in chronological order and then input into the LSTM model.

[0074] The outputs of the LSTM layer are weighted and summed, with a bias term b added. The formula is as follows:

[0075] The weighted summation of the nonlinear transformation results of the eigenvectors is given by the following formula:

[0076] The nonlinear transformation results output by the LSTM layer are weighted and summed, with a bias term 'a' added. The formula is as follows:

[0077] For denominators that are exponential functions, used to adjust the strength of the nonlinear transformation, the expression is:

[0078] Through complex nonlinear transformations and weighted summation, the prediction results are made more accurate, improving the model's predictive ability. The introduction of various nonlinear transformations and weight coefficients enhances the model's expressive and generalization capabilities. High-quality fault prediction models can quickly generate prediction results, enabling energy storage devices to respond in a timely manner. Accurate fault prediction can provide early warnings of potential faults, allowing energy storage devices to prepare in advance and enhancing their reliability and stability.

[0079] S4. Adjusting the state of charge of the energy storage device based on the forecast results includes the following steps.

[0080] Obtain the current state of charge of the energy storage device, and calculate based on the current grid operating status and fault prediction values.

[0081] Head

[0082] The standard charge state expression is as follows:

[0083]

[0084] Among them, St+1 S represents the target state of charge of the energy storage device at time t+1. t Let η be the state of charge of the energy storage device at the current time t, and t be the adjustment coefficient.

[0085] Adjust the state of charge of the energy storage device according to the calculated target state of charge.

[0086] When S t+1 >S t When this happens, the charging process is initiated, changing the state of charge of the energy storage device from S... t Adjust to S t+1 Increase the charging power of the energy storage device until the state of charge reaches S. t+1 ;

[0087] When S t+1 t When this happens, the discharge process is initiated, changing the state of charge of the energy storage device from S... t Adjust to S t+1 Increase the discharge power of the energy storage device until the state of charge reaches S. t+1 ;

[0088] When S t+1 =S t When the charge state remains unchanged, the current charge state is maintained.

[0089] Furthermore, after using the calculated power grid fault prediction value, the range of its prediction value is [0, 1];

[0090] When Q f When (t) is close to 0, the probability of a grid failure is low, and the target state of charge S t+1 Approaching the current state of charge S t The state of charge of the energy storage device does not change significantly;

[0091] When Q f When (t) approaches 0, the probability of a grid fault is high, and the target state of charge S t+1 This will increase significantly, and the state of charge of energy storage devices will be adjusted to a higher level in order to provide more backup energy in the event of a failure;

[0092] When S t+1 >S t When calculating the required increase in electricity, the expression is ΔE. charge =(S t+1 -S t ) × Total capacity;

[0093] Where, ΔE charge To increase the amount of electricity;

[0094] ​Based on the charging efficiency and maximum charging power, determine the charging time, set the charging controller, start the charging process, and monitor the charging status until the state of charge reaches S. t+1 ;

[0095] When S t+1 t At that time, the amount of electricity that needs to be reduced is calculated, expressed as ΔE. discharge =(S t -S t+1 ) × Total capacity;

[0096] Where, ΔE discharge To reduce power consumption;

[0097] Set the discharge controller, start the discharge process, and monitor the discharge status until the state of charge reaches S. t+1 ;

[0098] When S t+1 =S t At this time, the current state of charge remains unchanged, and no charging or discharging operation is performed.

[0099] S5. Combining the adjusted state of charge of the energy storage device with real-time power grid operation data, and analyzing the real-time monitoring data to extract fault characteristic information includes the following steps.

[0100] The adjusted state of charge of the energy storage device is combined with real-time data on grid operating frequency deviation, frequency change rate, and voltage fluctuation. A nonlinear function is used to fuse these data to generate a comprehensive fault characteristic value, expressed as follows:

[0101]

[0102] Among them, O fault (t) represents the fault characteristic value at time point t, GΔ(t) represents the frequency deviation at time point t, and U df (t) represents the rate of change of frequency at time t, R V (t) represents the voltage fluctuation at time point t, and β1 is the adjustment coefficient;

[0103] Furthermore, Part One The effects of frequency deviation and frequency change rate are taken into account. The larger the frequency deviation, the larger the numerator; the larger the frequency change rate, the smaller the denominator, and the larger the overall value.

[0104] Part Two The effects of voltage fluctuations and the state of charge of energy storage devices are taken into account. The greater the voltage fluctuation, the larger the numerator; the higher the state of charge of energy storage devices, the larger the denominator, and the smaller the overall value.

[0105] ​S6. Based on fault characteristic information and the adjusted state of charge of the energy storage device, dynamically allocate the frequency regulation response priority of the energy storage device and generate the instantaneous frequency regulation power command of the energy storage device, including the following steps.

[0106] The frequency regulation response priority of the energy storage device is calculated based on the fault characteristic values ​​and the adjusted state of charge of the energy storage device. The expression is as follows:

[0107] Z priority (t)=(m1·O fault (t)+m2)·(γ1·S t+1 +γ2·S t+1 );

[0108] Among them, Z priority (t) represents the frequency regulation response priority of the energy storage device at time t, m1 represents the influence of the control fault characteristic value on the frequency regulation response priority, m2 is a constant term, γ1 represents the influence of the control state of charge on the frequency regulation response priority, and γ2 represents the secondary influence of the control state of charge on the frequency regulation response priority.

[0109] Based on the calculated frequency regulation response priority, an instantaneous frequency regulation power command for the energy storage device is generated, expressed as follows:

[0110] A freq (t)=C max ·Z priority (t);

[0111] Among them, A freq (t) represents the instantaneous frequency modulation power command at time point t, C max This refers to the maximum frequency regulation power of the energy storage device;

[0112] Furthermore, when the fault characteristic value P priority When (t) is large, it indicates a high risk of grid failure. fault The value of (t)+m2 will be larger, thereby increasing the priority of the frequency modulation response;

[0113] One influence γ1·S t+1 When the state of charge of an energy storage device is high, it indicates that the device has sufficient energy reserves and can provide more frequency regulation support, thus giving it a higher priority for frequency regulation response.

[0114] Secondary influence γ2·S t+1 By introducing a quadratic term, the influence of the state of charge can be further enhanced or weakened. When γ2 is positive, the growth rate of the frequency modulation response priority increases with the increase of the state of charge. When γ2 is negative, the growth rate of the frequency modulation response priority decreases with the increase of the state of charge.

[0115] S7. Dynamically adjusting the power output of the energy storage device according to the real-time frequency modulation power command includes the following steps.

[0116] Based on the real-time frequency modulation power command and the current power output, the power adjustment amount of the energy storage device is calculated, expressed as follows:

[0117] Δd(t)=A freq (t)-Y current (t);

[0118] Where ΔD(t) is the power adjustment at time point t, Y current (t) represents the current power output at time point t;

[0119] When ΔD(t)>0, it indicates that the power output of the energy storage device needs to be increased;

[0120] When ΔD(t) < 0, it indicates that the power output of the energy storage device needs to be reduced;

[0121] When ΔD(t) = 0, it means that the current power output already meets the frequency modulation requirements and no adjustment is needed;

[0122] Furthermore, when ΔP(t) > 0, the required increase in power ΔP(t) is calculated. Based on the maximum charge / discharge rate of the energy storage device and the current state of charge, the timing and method of power adjustment are determined. The power controller is then set to initiate the power adjustment process and monitor the power output until P is reached. freq (t);

[0123] When ΔP(t) < 0, calculate the power reduction ΔP(t) required. Based on the maximum charge / discharge rate of the energy storage device and the current state of charge, determine the timing and method of power adjustment. Set the power controller to start the power adjustment process and monitor the power output until P is reached. freq (t);

[0124] When ΔP(t) = 0, it means that the current power output already meets the frequency modulation requirements, and there is no need to adjust it to keep the current power output unchanged.

[0125] This embodiment also provides a computer device applicable to the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as proposed in the above embodiment.

[0126] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0127] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics, as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0128] In summary, this invention improves the reliability and consistency of power grid operation data by collecting, filtering, and removing outliers. The preprocessed feature vectors are then output to a fault prediction model to obtain power grid fault prediction results, providing a reliable basis for frequency regulation control. Using the calculated power grid fault prediction values, the target state of charge is calculated based on the current power grid operating state and the fault prediction values, ensuring timely frequency regulation support in fault conditions. The adjusted energy storage device status and voltage fluctuation data are combined to generate comprehensive fault feature values. Multi-parameter fusion improves the comprehensiveness and accuracy of fault features. Real-time frequency regulation power commands are generated based on the calculated frequency regulation response priority of the energy storage devices, ensuring rapid and accurate frequency regulation in fault conditions. Based on the real-time frequency regulation power commands, the power output of the energy storage devices is dynamically adjusted to ensure that frequency regulation requirements are met.

[0129] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics, characterized in that: include, Collect power grid operation data and preprocess it; A fault prediction model is constructed based on historical power grid operation data and fault data. The preprocessed data is then input into the fault prediction model to generate prediction results. The specific steps are as follows: The preprocessed feature vector is output to the fault prediction model to obtain the power grid fault prediction result, expressed as follows: Among them, Q f (t) represents the predicted power grid fault value at time t, σ represents the output conversion value, X represents the eigenvector, and H represents the eigenvector. j (t) represents the state of the j-th unit in the output at time t, w j Let be the weighting coefficient for the state of the j-th unit, b be the bias term for adjusting the baseline level, α be the adjustment coefficient for controlling the slope of the nonlinear function, and T be... k (t) represents the value of the state of the k-th unit after a nonlinear transformation, z k v represents the weight coefficient for the state of the k-th unit. l Let be the weight coefficient of the l-th feature in the feature vector X, j be the index of the output unit, l be the index of the nonlinear transformation parameter in the feature vector X, N be the dimension of the feature vector X, and F be the weight coefficient of the l-th feature. l (X) is the nonlinear transformation of the l-th feature of the feature vector X, K is the number of output units, and α is the bias term for adjusting the baseline level; Based on the forecast results, the state of charge of the energy storage equipment is adjusted. The specific steps are as follows: Obtain the current state of charge of the energy storage device, and calculate the target based on the current grid operating status and fault prediction values. The standard charge state expression is as follows: Among them, S t+1 S represents the target state of charge of the energy storage device at time t+1. t Let η be the state of charge of the energy storage device at the current time t, and t be the adjustment coefficient. Adjust the state of charge of the energy storage device according to the calculated target state of charge. When S t+1 >S t When this happens, the charging process is initiated, changing the state of charge of the energy storage device from S... t Adjust to S t+1 Increase the charging power of the energy storage device until the state of charge reaches S. t+1 ; When S t+1 >S t When this happens, the discharge process is initiated, changing the state of charge of the energy storage device from S... t Adjust to S t+1 Increase the discharge power of the energy storage device until the state of charge reaches S. t+1 ; When S t+1 =S t When that happens, the current state of charge remains unchanged; By combining the adjusted state of charge of the energy storage device with real-time power grid operation data, fault characteristic information is extracted from the real-time monitoring data. Based on fault characteristic information and the adjusted state of charge of the energy storage device, the frequency regulation response priority of the energy storage device is dynamically allocated, and the instantaneous frequency regulation power command of the energy storage device is generated. The power output of the energy storage device is dynamically adjusted according to the real-time frequency modulation power command.

2. The primary frequency regulation control method for new energy power plants considering grid fault characteristics as described in claim 1, characterized in that: Collecting and preprocessing power grid operation data includes the following steps: The collected power grid operation data includes frequency deviation, frequency change rate, and voltage fluctuation. The collected power grid operation data is then filtered and outlier removed.

3. The primary frequency regulation control method for new energy power plants considering grid fault characteristics as described in claim 1, characterized in that: Building a fault prediction model based on historical power grid operation data and fault data includes the following steps. Time-series alignment of historical power grid operation data and fault data; The preprocessed power grid operation data is segmented into fixed time windows aligned with the time series, and feature vectors are extracted from the data within each time window. A fault prediction model was constructed using a time series model based on a long short-term memory network.

4. The primary frequency regulation control method for new energy power plants considering grid fault characteristics as described in claim 1, characterized in that: By combining the adjusted state of charge of the energy storage device with real-time power grid operation data, fault characteristic information is extracted from the real-time monitoring data. Includes the following steps, The adjusted state of charge of the energy storage device is combined with real-time data on grid operating frequency deviation, frequency change rate, and voltage fluctuation. A nonlinear function is used to fuse these data to generate a comprehensive fault characteristic value, expressed as follows: Among them, O fault (t) represents the fault characteristic value at time point t, GΔ(t) represents the frequency deviation at time point t, and U df (t) represents the rate of change of frequency at time t, R V (t) represents the voltage fluctuation at time point t, and β1 is the adjustment coefficient.

5. The primary frequency regulation control method for new energy power plants considering grid fault characteristics as described in claim 4, characterized in that: Based on fault characteristic information and the adjusted state of charge of the energy storage devices, the following steps are taken to dynamically allocate the frequency regulation response priority of the energy storage devices and generate real-time frequency regulation power commands for the energy storage devices. The frequency regulation response priority of the energy storage device is calculated based on the fault characteristic values ​​and the adjusted state of charge of the energy storage device. The expression is as follows: Z priority (t)=(m1·O fault (t)+m2)·(γ1·S t+1 +γ2·S t+1 ); Among them, Z priority (t) represents the frequency regulation response priority of the energy storage device at time t, m1 represents the influence of the control fault characteristic value on the frequency regulation response priority, m2 is a constant term, γ1 represents the influence of the control state of charge on the frequency regulation response priority, and γ2 represents the secondary influence of the control state of charge on the frequency regulation response priority. Based on the calculated frequency regulation response priority, an instantaneous frequency regulation power command for the energy storage device is generated, expressed as follows: A freq (t)=C max ·Z priority (t); Among them, A freq (t) represents the instantaneous frequency modulation power command at time point t, C max This represents the maximum frequency regulation power of the energy storage device.

6. The primary frequency regulation control method for new energy power plants considering grid fault characteristics as described in claim 1, characterized in that: Dynamically adjusting the power output of energy storage devices according to real-time frequency modulation power commands includes the following steps. Based on the real-time frequency modulation power command and the current power output, the power adjustment amount of the energy storage device is calculated, expressed as follows: ΔD(t)=A freq (t)-Y current (t); Where ΔD(t) is the power adjustment at time point t, Y cuttent (t) represents the current power output at time point t; When ΔD(t)>0, it indicates that the power output of the energy storage device needs to be increased; When ΔD(t) < 0, it indicates that the power output of the energy storage device needs to be reduced; When ΔD(t) = 0, it means that the current power output already meets the frequency modulation requirements and no adjustment is needed.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the primary frequency regulation control method for new energy power plants that takes into account grid fault characteristics as described in any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the primary frequency regulation control method for new energy power plants that takes into account the characteristics of power grid faults as described in any one of claims 1 to 6.

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