Grid-connected control method based on optimal distribution of grid-connected indexes of gravity energy storage synchronous motor

Abstract

The grid-connected control method based on the optimal distribution of the grid-connected indicators of the gravity energy storage synchronous motor of the application establishes a gravity energy storage system grid-connected model, analyzes the influence characteristics of different motor terminal voltage phases, amplitudes and frequency parameters on the impact current, summarizes the change law of the influence weight of different motor terminal voltage indicators on the impact current, introduces the weighted conditions of the gravity energy storage synchronous generator / motor grid connection, and selects three data of the motor terminal voltage indicator data, optimizes the indicator data of the motor terminal voltage of the synchronous generator / motor. Through simulation verification, the method can improve the grid connection success rate while significantly reducing the impact current, and fully verifies the correctness and practicability of the proposed method.

Description

Technical Field

[0001] This invention relates to the field of battery management technology, and in particular to a grid-connected control method based on the optimal allocation of grid-connected indicators for gravity energy storage synchronous motors. Background Technology

[0002] Gravity energy storage is a mechanical energy storage method. Its storage medium is primarily water or solid materials. The charging and discharging process of the energy storage system is achieved by raising and lowering the storage medium based on a height difference. The basic principle of gravity energy storage is to use surplus electricity to lift a heavy object, storing its gravitational potential energy; when needed, the heavy object is released, and its potential energy drives a generator to produce electricity. The storage medium for gravity energy storage is mainly divided into water and solid materials. Water-based gravity energy storage is easily limited by terrain and water source constraints, making large-scale energy storage systems easier to build near natural water sources. Solid-material gravity energy storage mainly uses high-density materials such as metals, cement, and sand, utilizing structures such as mountains, underground shafts, and artificial structures to lift and lower the heavy object. Although the demand for energy storage continues to grow, among the energy storage technologies currently available for large-scale application, pumped hydro storage and chemical batteries face significant challenges in terms of scalability, levelized cost-effectiveness, safety, and environmental risks. Therefore, in the context of global decarbonization, gravity energy storage, with its high safety, reliability, and environmental friendliness, has become one of the most promising energy storage technologies. Furthermore, for the mountainous and water-scarce "Three-North" region (Northwest, North, and Northwest China), solid-based gravity energy storage systems, which are not limited by water resources, are the optimal choice. Compared to pumped storage, it eliminates the need to excavate reservoirs and water conveyance tunnels, significantly reducing the amount of engineering work and thus lowering costs. Therefore, solid-based gravity energy storage technology holds promise for developing into a large-scale, low-cost, and safe energy storage technology for power systems.

[0003] On the other hand, taking a vertical gravity energy storage system as an example, the basic requirement for grid connection of its generator / motor is that the phase sequence, frequency, amplitude, and phase of the voltage at the generator end and the grid end must be consistent. However, in actual grid connection of gravity energy storage systems, due to different startup and grid connection methods, as well as control system deviations, it is difficult for the voltage indicators at the generator end and the grid end to be completely consistent. Moreover, compared with conventional generator sets, gravity energy storage systems need to frequently switch between charging and discharging operating conditions according to grid demand, and each switch requires the startup and grid connection of the generator / motor. At this time, the grid connection success rate and the impact problems caused by frequent grid connection will threaten the safe operation of the grid connection. If there is always a large error between the voltage indicators at the generator end and the grid end, a stable transient inrush current will be generated. If no human intervention and control are carried out, long-term operation will bring hidden dangers to the safety of the grid connection system, causing a series of problems such as equipment aging or even damage. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a grid-connected control method based on the optimal allocation of grid-connected index data of a gravity-storage synchronous motor. This method employs weight allocation and data processing techniques to improve the grid connection success rate and achieve precise processing of grid connection index data. It effectively suppresses the continuous and stable inrush current generated by frequent grid connections, thereby improving the power quality of the power grid. Specifically, the grid-connected control method based on the optimal allocation of grid-connected index data of a gravity-storage synchronous motor includes the following steps:

[0005] Step 1) Based on the influence characteristics of the phase, amplitude, and frequency of the terminal voltage on the inrush current, confirm the weighted conditions for grid connection of the gravity energy storage synchronous motor.

[0006] Step 2) Based on the maximum inrush current generated when the phase, amplitude, and frequency fluctuate, confirm the priority of the influence weight of different grid connection indicators on the system as frequency, phase, and amplitude, respectively.

[0007] Step 3) The phase, amplitude, and frequency of the generator terminal voltage are used to comprehensively determine whether grid connection can be completed through ternary linear regression. The three sets of data are plotted in the same coordinate system, generating three curves: synchronous motor outlet voltage phase... Corresponding data Synchronous motor output voltage amplitude V: corresponding data V1, V2, V3…V i The output voltage frequency f of the synchronous motor corresponds to the data f1, f2, f3…f i ;

[0008] Step 4) The terminal voltage index data was fitted to reduce data error. After optimizing the data model, the index data was selected three times based on the priority of the grid connection index's impact on the system weight, which reduced the terminal voltage.

[0009] Step 5) Obtain the set of three sets of data connection conditions that can just complete the grid connection according to the multiple linear regression algorithm, so as to achieve the optimal and accurate processing of data.

[0010] A further design of the grid-connected control method based on the optimal allocation of grid-connected indexes for gravity energy storage synchronous motors is that, in step 1), the influence characteristics of changes in the phase, amplitude, and frequency of the generator terminal voltage on the grid-connected inrush current are first analyzed through simulation:

[0011] To ensure a reasonable weighting of the final grid connection parameters, so that changes in phase, amplitude, and frequency result in the same inrush current, an inrush current threshold of 120A is set. The degree to which each grid connection indicator deviates from the standard value is then analyzed, and weighting conditions for grid connection are proposed, namely:

[0012]

[0013] In the formula, Ω is the weighted index for grid connection, f, V represents the frequency, phase, and amplitude of the port voltage, respectively. The optimal grid connection index, i.e., the optimal grid connection time, is when the phase, amplitude, and frequency of the terminal voltage are completely consistent with those of the grid terminal voltage. At this time, the weighted index for grid connection is Ω0, and the phase, amplitude, and frequency of the terminal voltage are respectively... Given V0 and f0, we have:

[0014]

[0015] The most basic requirement for meeting the weighted index for grid connection is that the weighted value does not exceed ±10% of the optimal grid connection index Ω0, that is: |Ω-Ω0|≤10%Ω0.

[0016] A further design of the grid connection control method based on the optimal allocation of grid connection indicators for gravity energy storage synchronous motors is that, in step 3), the range of three grid connection conditions is initially determined according to the grid connection condition range adjusted according to the power quality fluctuation range standard of the grid connection specification: setting the phase deviation value of the synchronous motor output voltage. As a phase correction value And determine the phase range, that is: The output voltage phase of the synchronous motor is defined; the amplitude deviation value ΔV of the output voltage of the synchronous motor is set as the amplitude correction value V. m =V0-ΔV,V n =V0 + ΔV, and determine the amplitude range, i.e.: V m ≤V i ≤V n V i The output voltage amplitude of the synchronous motor is set; the frequency deviation value Δf of the output voltage of the synchronous motor is set as the frequency correction value f. o =f0-Δf,f p = f0 + Δf, and determine the frequency range, i.e.: f o ≤f i ≤f p f i This refers to the output voltage frequency of the synchronous motor.

[0017] A further design of the grid-connected control method based on the optimal allocation of grid-connected indicators for gravity energy storage synchronous motors is that the process of reducing data errors and optimizing the data model by fitting the terminal voltage index data in step 4) includes the following steps:

[0018] Step 4-1) By installing and collecting data, the phase, amplitude, and frequency data of the terminal voltage of the gravity energy storage synchronous motor during operation are obtained. Since the acquisition time is fixed, the three sets of data of terminal voltage phase, amplitude, and frequency correspond to the same time axis t. That is, during the process of the synchronous motor being started by the mass block and the synchronous motor reaching the rated excitation, the frequency, phase, and amplitude of the synchronous motor outlet voltage must change together with time t to the standard value. Therefore, the horizontal axis of the three curves uses the same coordinate axis, ensuring the synchronization of the three sets of data.

[0019] Three sets of data are used to generate three sets of curves in the same coordinate system:

[0020] Synchronous motor output voltage phase Corresponding data

[0021] Synchronous motor output voltage amplitude V: corresponding data (V1, V2, V3…V) i );

[0022] Synchronous motor output voltage frequency f: corresponding data (f1, f2, f3…f i );

[0023] Step 4-2) If data acquisition cards or experimental measurements of the phase, amplitude, and frequency of the terminal voltage of gravity energy storage generators / motors are used, the data are all observations, and their complete accuracy cannot generally be guaranteed. Therefore, it is necessary to introduce data fitting methods such as the least squares method to eliminate bad data, reduce the error of the data sets, and achieve the goal of optimizing the data model.

[0024] Based on the power quality fluctuation range standards in the grid connection specifications and practical engineering application experience, a range of grid connection index data conforming to the weighted index for grid connection is obtained, resulting in a set of generator terminal voltage phase data. Terminal voltage amplitude data set V = {V1, V2, V3…V i The set of terminal voltage frequency data is f = {f1, f2, f3…f}. i The least squares method is introduced to reduce the error of the data sets, optimize the data model, and ensure that the data model has high accuracy and strong robustness. Assume that the fitting function used is...

[0025]

[0026] In the formula: x i (i = 1, 2, ..., n) represents the i-th data point in the dataset, and n is the total number of data points in the dataset; φ(x i The score data is obtained by fitting the data after assigning weights. The data is obtained after least squares operation. Assume the actual data is Y(x). i If the problem is transformed into solving for the unknown parameters a and b, then the solution is obtained by finding the unknown parameters a and b. and Y(x i The mean square error of ) is minimized, that is:

[0027]

[0028] Taking the partial derivatives with respect to a and b respectively, we get:

[0029]

[0030]

[0031] Setting the values ​​of the two equations above to zero respectively, we obtain the optimal solutions for a and b as follows:

[0032]

[0033]

[0034] Therefore, we get:

[0035]

[0036] Set the terminal voltage phase data Amplitude data set V = {V1, V2, V3…V} i}, the frequency data set f = {f1, f2, f3…f i Substituting these values ​​into the above formula yields a more accurate result. V, f data sets;

[0037]

[0038]

[0039]

[0040] The fitted dataset is denoted as:

[0041]

[0042] A further design of the grid-connected control method based on the optimal allocation of grid-connected indicators for gravity energy storage synchronous motors is that, in step 4), the indicator data is selected three times to reduce the generator terminal voltage. Specifically, this includes the following steps:

[0043] Step 4-1) First extraction of three sets of data: based on the amplitude range V o ≤V i ≤V p Vi This represents the amplitude of the synchronous motor's output voltage, extracted from the frequency data of the synchronous motor's output voltage after the second extraction (f). jm …f kn Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V) jm …V kn ), and extract frequency data (f) within the same time range again. jmo …f knp Phase data Amplitude data (V) jmo …V knp );

[0044] Step 4-2) Select three sets of data for the second time: based on the phase range This represents the phase of the synchronous motor's output voltage, derived from the frequency data of the synchronous motor's output voltage after the first extraction (f). j …f k Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V) j …V k Frequency data (f) within the same time range are extracted again from the data. jm …f kn Phase data Amplitude data (V) jm …V kn Step 4-3) Select three sets of data for the third time: based on the frequency range f o ≤f i ≤f p f i This represents the output voltage frequency of the synchronous motor, derived from the output voltage frequency data (f1, f2, f3…f…). i Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V1, V2, V3…V) i Extracting frequency data (f) from the same time range j …f k Phase data Amplitude data (V) j …V k ).

[0045] A further design of the grid-connection control method based on the optimal allocation of grid-connection indicators of gravity energy storage synchronous motor is that, in step 5), after the accuracy of the terminal voltage index data of gravity energy storage synchronous motor is maximized, the three sets of data are analyzed through multiple linear regression to reduce the difficulty of judging the accuracy of the data. By setting the data set Y that can just complete the grid connection of the three sets of data, the data is further filtered.

[0046] Based on the multiple linear regression algorithm, the dependent variable is set as the data set Y of three sets of data when grid connection can be completed under the conditions; the three independent variables are then analyzed again: synchronous motor outlet voltage and frequency data (f jmo …f knp Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V) jmo …V knp The effect of ), on the dependent variable Y;

[0047]

[0048] In the formula, Y is the data set when the three sets of data conditions are just enough to complete grid connection, and β0 is the intercept; β i For x i The regression coefficients are given by i = 1, 2, 3, and ε is the residual term. The regression coefficients are calculated by the least squares method.

[0049] β=(X T X) -1 X T Y

[0050] Where X is:

[0051]

[0052] In the formula, β=[β1,β2,β3] T ;Y = [y jmo ,……y knp ] T , which is the response variable of the data, finally yields the set Y of the three selected sets of grid connection conditions, thus completing the optimal and precise processing of the data.

[0053] The advantages of this invention are as follows:

[0054] This invention improves the success rate of motor grid connection under frequent grid connection conditions by introducing weighted conditions for gravity energy storage synchronous power generation / motor grid connection.

[0055] This invention achieves precise processing of grid connection index data by accurately selecting motor operation data, reducing the transient inrush current during grid connection, and ensuring that the terminal voltage index of gravity energy storage synchronous generator / motor has high accuracy under frequent grid connection conditions, generating very small transient inrush current, which better meets the control requirements for inrush current during grid connection.

[0056] Instead of directly using measured or simulated data for data selection and optimization, this invention first uses the least squares method to optimize the data model, ensuring that the data model of this invention has high accuracy and strong robustness. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of a grid-connected model of a gravity-powered energy storage synchronous motor.

[0058] Figure 2 This is a schematic diagram of a gravity energy storage grid-connected module. Detailed Implementation

[0059] The technical solution of the present invention will be described in detail below.

[0060] like Figure 1 This embodiment describes a grid-connected control method based on the optimal allocation of grid-connected performance indicators for gravity-storage synchronous motors. This method is based on the system provided in this embodiment, such as... Figure 1The system mainly consists of a vertical gravity energy storage module, a grid-connected module, a synchronous generator / motor, a circuit breaker, and an infinite power grid module. The vertical gravity energy storage module serves as a backup power source for the grid. When the grid's power supply is insufficient, the mass block in the vertical gravity energy storage system descends, converting gravitational potential energy into electrical energy to supply power to the grid. When there is surplus power in the grid, the grid supplies power to the gravity energy storage system, driving the generator / motor to lift the mass block, converting electrical energy into gravitational potential energy for storage. The higher the mass block, the greater the stored gravitational potential energy. The grid-connected module accurately achieves grid connection of the gravity energy storage synchronous generator / motor. Since changing the frequency of the terminal voltage is equivalent to indirectly changing the period of the terminal voltage, and thus changing the phase of the terminal voltage, when analyzing the influence characteristics of frequency changes on the inrush current, it is necessary to ensure that the grid connection time is the same for different terminal voltage frequency settings, achieving the effect of controlling a single variable. The synchronous generator / motor is the main component for connecting the vertical gravity energy storage system to the infinite grid. During grid connection, the terminal voltage and grid voltage should maintain consistency in voltage phase sequence, phase, amplitude, and frequency. The infinite grid module simulates the power grid and completes the connection between the vertical gravity energy storage system and the grid. The circuit breaker controls the opening and closing of the vertical gravity energy storage synchronous generator / motor and the infinite grid. When the grid connection inrush current meets the requirements for grid connection closing (generally, the inrush current is between 0.1 and 0.9 times the rated current), the circuit breaker closes.

[0061] The method specifically includes the following steps:

[0062] Step 1) Based on the influence characteristics of the phase, amplitude, and frequency of the terminal voltage on the inrush current, confirm the weighted conditions for grid connection of the gravity energy storage synchronous motor.

[0063] The phase, amplitude, and frequency of the terminal voltage were adjusted to be close to the existing grid connection specifications, and the inrush current was analyzed through simulation as follows:

[0064] The phase adjustment range is (0°±15°), and the amplitude adjustment range is (1±5%)U. N U N The voltage is the rated voltage, and the frequency adjustment range is (50±0.5)Hz. The inrush current of the three is simulated.

[0065] Based on the simulation results of the model after changes in phase, amplitude, and frequency, the maximum / minimum transient inrush current, the multiple of the rated current, the conditions for the generation of the maximum / minimum transient inrush current, and the time of generation of the maximum transient inrush current were analyzed after changes in each grid connection index, as shown in Table 1. The synchronous motor parameters used in the simulation and the various simulation modules are shown in Table 2. Figure 1 and Figure 2 As shown.

[0066] Table 1 Simulation results of inrush current under different grid connection indicators

[0067]

[0068] Table 2. Parameter settings for the synchronous motor of the vertical gravity energy storage system.

[0069]

[0070] Step 2) Based on the maximum inrush current generated by phase, amplitude, and frequency fluctuations, the priority of different grid connection indicators on the system's influence weight is determined to be frequency, phase, and amplitude, respectively. The weighting condition for the grid connection of the gravity energy storage synchronous motor is proposed based on the influence characteristics of the generator terminal voltage phase, amplitude, and frequency on the inrush current. First, the influence characteristics of changes in generator terminal voltage phase, amplitude, and frequency on the grid connection inrush current are analyzed through simulation:

[0071] Table 1 shows that the timing of the maximum / minimum inrush current and the minimum inrush current value are basically the same when different grid connection indicators fluctuate, and the condition for the minimum inrush current to occur is the optimal grid connection indicator. The main difference is the maximum inrush current value. The maximum inrush current generated by phase, amplitude, and frequency fluctuations are 45.23A, 9.12A, and 363.31A, respectively, which are 2.23 times, 0.45 times, and 18.16 times the rated current of the synchronous generator / motor, respectively. Therefore, it can be preliminarily concluded that the weight of different grid connection indicators on the system is frequency > phase > amplitude.

[0072] To ensure a reasonable weighting, the phase, amplitude, and frequency must all be changed to achieve the same inrush current. Here, the inrush current threshold is set at 120A, and the degree to which each grid-connected indicator deviates from the standard value is analyzed. A weighting condition for grid connection is proposed, namely:

[0073]

[0074] In the formula, Ω is the weighted index for grid connection, f, V represents the frequency, phase, and amplitude of the port voltage, respectively, with units of Hz, degree, and kV.

[0075] Clearly, the optimal grid connection index, i.e., the optimal grid connection time, is when the phase, amplitude, and frequency of the generator terminal voltage are completely consistent with those of the grid terminal voltage. At this time, the weighted index for grid connection is Ω0, and the phase, amplitude, and frequency of the generator terminal voltage are respectively... Given V0 and f0, we have:

[0076]

[0077] The most basic requirement for meeting the weighted index for grid connection is that the weighted value does not exceed ±10% of the optimal grid connection index Ω0. That is:

[0078] |Ω-Ω0|≤10%Ω0

[0079] Step 3) To reduce data error, the terminal voltage index data was fitted. After optimizing the data model, the index data was selected three times to reduce the terminal voltage. This included the following steps:

[0080] Step 3-1) By installing and collecting data, a set of terminal voltage phase, amplitude, and frequency data is obtained during the operation of the gravity energy storage synchronous generator / motor. Since the acquisition time is fixed, the three sets of terminal voltage phase, amplitude, and frequency data correspond to the same time axis t. That is, during the process of the synchronous motor being started by the mass block and the synchronous motor reaching the rated excitation, the frequency, phase, and amplitude of the synchronous motor outlet voltage must change together with time t to the standard value. Therefore, the horizontal axis of the three curves uses the same coordinate axis, ensuring the synchronization of the three sets of data.

[0081] Three sets of data are used to generate three sets of curves in the same coordinate system:

[0082] Synchronous motor output voltage phase Corresponding data

[0083] Synchronous motor output voltage amplitude V: corresponding data (V1, V2, V3…V) i );

[0084] Synchronous motor output voltage frequency f: corresponding data (f1, f2, f3…f i );

[0085] Step 3-2) If data acquisition cards or experimental measurements of the phase, amplitude, and frequency of the terminal voltage of gravity energy storage generators / motors are used, the data are all observations, and their complete accuracy cannot generally be guaranteed. Therefore, it is necessary to introduce data fitting methods such as the least squares method to eliminate bad data, reduce the error of the data set, and achieve the goal of optimizing the data model.

[0086] Based on the power quality fluctuation range standards in the grid connection specifications and practical engineering application experience, a range of grid connection index data conforming to the weighted index for grid connection is obtained, resulting in a set of generator terminal voltage phase data. Terminal voltage amplitude data set V = {V1, V2, V3…V i The set of terminal voltage frequency data is f = {f1, f2, f3…f}. i The least squares method is introduced to reduce the error of the data sets, optimize the data model, and ensure that the data model has high accuracy and strong robustness. Assume that the fitting function used is...

[0087]

[0088] In the formula: x i (i = 1, 2, ..., n) represents the i-th data point in the dataset, and n is the total number of data points in the dataset; φ(x i (x) represents the score data obtained after data fitting by assigning weights; i Y(x) represents the data obtained through least squares calculation. Assume the actual data is Y(x). i If the problem is transformed into solving for the unknown parameters a and b, then the solution is obtained by finding the unknown parameters a and b. and Y(x i The mean square error of ) is minimized, that is:

[0089]

[0090] Taking the partial derivatives with respect to a and b respectively, we get:

[0091]

[0092]

[0093] Setting the values ​​of the two equations above to zero respectively, we obtain the optimal solutions for a and b as follows:

[0094]

[0095]

[0096] The calculated a * and b * Substituting into equation (8), we get:

[0097]

[0098] Set the terminal voltage phase data Amplitude data set V = {V1, V2, V3…V} i}, the frequency data set f = {f1, f2, f3…f i Substituting these values ​​into the above formula yields a more accurate result. V and f data sets.

[0099] Right now:

[0100]

[0101]

[0102]

[0103] The fitted dataset is denoted as:

[0104]

[0105]

[0106]

[0107] Step 4) After optimizing the data model based on the terminal voltage operation index data of the gravity energy storage synchronous generator / motor, a three-step data selection method is adopted, taking into account the priority of the grid connection index's impact on the system. Each step of data selection reduces errors, improves grid connection accuracy, and achieves the goal of reducing transient inrush current during grid connection. Specifically, the steps include the following:

[0108] Step 4-1) First extraction of three sets of data: based on the amplitude range V o ≤V i (Synchronous motor output voltage amplitude) ≤ V p The output voltage and frequency data of the synchronous motor after the second extraction (f jm …f kn Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V) jm …V kn ), and extract frequency data (f) within the same time range again. jmo …f knp Phase data Amplitude data (V) jmo …V knp ).

[0109] Step 4-2) Select three sets of data for the second time: based on the phase range The synchronous motor output voltage and frequency data (f) extracted after the first time j …f k Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V) j …V k Frequency data (f) within the same time range are extracted again from the data. jm …f kn Phase data Amplitude data (V) jm …V kn );

[0110] Step 4-3) Select three sets of data for the third time: based on the frequency range f o ≤f i (Synchronous motor output voltage frequency) ≤ f p From the synchronous motor output voltage frequency data (f1, f2, f3…fi Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V1, V2, V3…V) i Extracting frequency data (f) from the same time range j …f k Phase data Amplitude data (V) j …V k );

[0111] Step 5) Obtain the set of three data points that are just enough to complete grid connection using the multiple linear regression algorithm, thus achieving optimal and accurate data processing. Specifically: After maximizing the accuracy of the terminal voltage index data for the gravity storage synchronous generator / motor, analyze the three sets of data using multiple linear regression to reduce the difficulty of judging data accuracy. Further filter the data by setting the set Y of data points that are just enough to complete grid connection.

[0112] Based on the multiple linear regression algorithm, the dependent variable is set as the data set Y, representing the critical value at which grid connection can be achieved under the three data conditions. The three independent variables are then analyzed again: the synchronous motor outlet voltage and frequency data (f...). jmo …f knp Synchronous motor output voltage phase data Synchronous motor output voltage amplitude data (V) jmo …V knp The effect of ), on the dependent variable Y.

[0113]

[0114] In the formula, Y is the data set when the three sets of data conditions are just enough to complete grid connection, and β0 is the intercept; β i For x i The regression coefficients are given by ε, where i = 1, 2, 3, and ε is the residual term. The regression coefficients are calculated using the least squares method.

[0115] β=(X T X) -1 X T Y

[0116] Where X is used to solve for the regression coefficient β, β = (X T X) -1 X T Y is the calculation formula in linear regression. The first column of the elements in matrix X is 1, followed by the final determined phase, amplitude, and frequency data. X is:

[0117]

[0118] In the formula, β=[β1,β2,β3] T ;Y = [y jmo ,……y knp ] T , which serves as the response variable for the data. Ultimately, the set of grid connection conditions Y corresponding to the three selected sets of data is obtained. This achieves optimal and precise data processing.

[0119] The grid-connection control method based on the optimal allocation of grid-connection indicators for gravity energy storage synchronous motors in this embodiment improves the success rate of motor grid connection under frequent grid-connection conditions by introducing weighted conditions for grid connection of gravity energy storage synchronous generators / motors. By accurately selecting motor operating data, the grid-connection indicator data is processed more precisely, reducing the transient inrush current during grid connection. This ensures that the terminal voltage indicators of gravity energy storage synchronous generators / motors have high accuracy under frequent grid connection conditions, resulting in a very small transient inrush current, which better meets the control requirements for inrush current during grid connection.

[0120] On the other hand, the technical solution of this application does not directly use measured or simulated data for data selection and optimization. Instead, it first uses the least squares method to optimize the data model, ensuring that the data model of this invention has high accuracy and strong robustness.

[0121] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A grid-connected control method based on the optimal allocation of grid-connected performance indicators for gravity-storage synchronous motors, characterized in that... Includes the following steps: Step 1) Based on the influence characteristics of the phase, amplitude, and frequency of the generator terminal voltage on the inrush current, confirm the weighted conditions for grid connection of the gravity energy storage synchronous motor; Step 2) Based on the maximum inrush current generated when the phase, amplitude, and frequency fluctuate, confirm the priority of the influence weight of different grid connection indicators on the system as frequency, phase, and amplitude, respectively. Step 3) The phase, amplitude, and frequency of the generator terminal voltage are used to comprehensively determine whether grid connection can be completed through ternary linear regression. The three sets of data are plotted in the same coordinate system, generating three curves: synchronous motor outlet voltage phase φ: corresponding data φ1, φ2, φ3…φ i Synchronous motor output voltage amplitude V: corresponding data V1, V2, V3…V i The output voltage frequency f of the synchronous motor corresponds to the data f1, f2, f3…f i ; Step 4) The terminal voltage index data was fitted to reduce data error. After optimizing the data model, the index data was selected three times based on the priority of the grid connection index's impact on the system weight, which reduced the terminal voltage. Step 5) Obtain the three sets of data grid connection conditions that can just complete the grid connection according to the multiple linear regression algorithm, and realize the optimal and accurate data processing; In step 1), the influence characteristics of changes in the phase, amplitude, and frequency of the terminal voltage on the grid-connected inrush current were first analyzed through simulation: To ensure a reasonable weighting of the final grid connection parameters, so that changes in phase, amplitude, and frequency result in the same inrush current, an inrush current threshold of 120A is set. The degree to which each grid connection indicator deviates from the standard value is then analyzed, and weighting conditions for grid connection are proposed, namely: In the formula, Ω is the weighting index for grid connection, and f, φ, and V are the frequency, phase, and amplitude of the port voltage, respectively. The optimal grid connection index, i.e., the optimal grid connection time, is when the phase, amplitude, and frequency of the generator terminal voltage are completely consistent with those of the grid terminal voltage. At this time, the weighted index for grid connection is Ω0, and the phase, amplitude, and frequency of the generator terminal voltage are φ0, V0, and f0, respectively. Then: The most basic requirement for meeting the weighted index for grid connection is that the weighted value does not exceed ±10% of the optimal grid connection index Ω0, that is: .

2. The grid-connected control method based on the optimal allocation of grid-connected indexes for gravity energy storage synchronous motors according to claim 1, characterized in that, In step 3), based on the grid connection condition range adjusted according to the power quality fluctuation range standard of the grid connection specification, the ranges of three grid connection conditions are initially determined: the synchronous motor output voltage phase deviation value Δφ is set as the phase correction value φ. j =φ0-Δφ,φ k =φ0+Δφ, and determine the phase range, i.e.: φ j ≤φ i ≤φ k φ i The output voltage phase of the synchronous motor is defined; the amplitude deviation value ΔV of the output voltage of the synchronous motor is set as the amplitude correction value V. m =V0-ΔV,V n = V0 + ΔV, and determine the amplitude range, i.e.: V m ≤V i ≤V n V i The output voltage amplitude of the synchronous motor is set; the frequency deviation value Δf of the output voltage of the synchronous motor is set as the frequency correction value f. o =f0-Δf,f p = f0 + Δf, and determine the frequency range, i.e.: f o ≤f i ≤f p f i This refers to the output voltage frequency of the synchronous motor.

3. The grid-connection control method based on the optimal allocation of grid-connection indicators for gravity energy storage synchronous motors according to claim 1, characterized in that, Step 4) involves data fitting to reduce data errors and optimizing the data model for the terminal voltage index data. This process includes the following steps: Step 4-1) By installing and collecting data, the phase, amplitude, and frequency data of the terminal voltage of the gravity energy storage synchronous motor during operation are obtained. Since the acquisition and time are fixed, the three sets of data of terminal voltage phase, amplitude, and frequency correspond to the same time axis t. That is, during the process of the synchronous motor being started by the mass block and the synchronous motor reaching the rated excitation, the frequency, phase, and amplitude of the synchronous motor outlet voltage must change together with time t to the standard value. Therefore, the horizontal axis of the three curves uses the same coordinate axis, ensuring the synchronization of the three sets of data. Three sets of data are used to generate three sets of curves in the same coordinate system: Synchronous motor output voltage phase φ: corresponding data (φ1, φ2, φ3…φ) i ); Synchronous motor output voltage amplitude V: Corresponding data (V1, V2, V3…V) i ); Synchronous motor output voltage frequency f: corresponding data (f1, f2, f3…f) i ); Step 4-2) If the phase, amplitude, and frequency data of the terminal voltage of the gravity energy storage generator / motor are obtained by using a data acquisition card or experimental measurement, the data are all observation values, and the complete accuracy of the data cannot be guaranteed. Therefore, it is necessary to introduce data fitting processing methods such as the least squares method to remove bad data, reduce the error of the data set, and achieve the purpose of optimizing the data model. Based on the power quality fluctuation range standards in the grid connection specifications and practical engineering application experience, the grid connection index data range conforming to the weighted index of grid connection is obtained, resulting in the generator terminal voltage phase data set φ = {φ1, φ2, φ3…φ}. i The set of terminal voltage amplitude data is V = {V1, V2, V3…V}. i } , The set of terminal voltage frequency data is f = {f1, f2, f3…f i The least squares method is introduced to reduce the error of the data set, optimize the data model, and ensure that the data model has high accuracy and strong robustness. It is assumed that the fitting function used is... In the formula: x i Let i = 1, 2, ..., n be the i-th data in the dataset, and n be the total number of data in the dataset; ϕ(x i The score data is obtained by fitting the data after assigning weights. The data is obtained after least squares calculation. Assume the actual data is Y(x). i If the problem is transformed into solving for the unknown parameters a and b, then the solution is obtained by finding the unknown parameters a and b. and Y( x i The mean square error of ) is minimized, that is: Taking the partial derivatives with respect to a and b respectively, we get: Setting the values ​​of the two equations above to zero respectively, we obtain the optimal solutions for a and b as follows: Therefore, we get: The terminal voltage phase data set φ = {φ1, φ2, φ3…φ i }, amplitude data set V = {V1, V2, V3…V i }, the frequency data set f = {f1, f2, f3…f i Substituting these values ​​into the above formula yields a more accurate set of φ, V, and f data. The fitted dataset is denoted as: .

4. The grid-connection control method based on the optimal allocation of grid-connection indicators for gravity energy storage synchronous motors according to claim 3, characterized in that, Step 4) involves three data selections of the indicator data to reduce the terminal voltage, specifically including the following steps: Step 4-1) First extraction of three sets of data: based on the amplitude range V o ≤V i ≤V p V i This represents the amplitude of the synchronous motor's output voltage, extracted from the frequency data of the synchronous motor's output voltage after the second extraction (f). jm …f kn ), synchronous motor output voltage phase data (φ) jm …φ kn ), synchronous motor output voltage amplitude data (V jm …V kn ), and extract frequency data (f) within the same time range again. jmo …f knp Phase data (φ) jmo …φ knp ), amplitude data (V jmo …V knp ); Step 4-2) Select three sets of data for the second time: based on the phase range φ j ≤φ i ≤φ k φ i This represents the phase of the synchronous motor's output voltage, derived from the frequency data of the synchronous motor's output voltage after the first extraction (f). j …f k ), synchronous motor output voltage phase data (φ) j …φ k ), synchronous motor output voltage amplitude data (V j …V k Frequency data (f) within the same time range are extracted again from the data. jm …f kn Phase data (φ) jm …φ kn ), amplitude data (V jm …V kn ); Step 4-3) Select three sets of data for the third time: based on the frequency range f o ≤f i ≤f p f i This represents the output voltage frequency of the synchronous motor, derived from the output voltage frequency data of the synchronous motor (f1, f2, f3…f…). i ), synchronous motor output voltage phase data (φ1, φ2, φ3…φ i ), synchronous motor output voltage amplitude data (V1, V2, V3…V i Extracting frequency data (f) from the same time range j …f k Phase data (φ) j …φ k ), amplitude data (V j …V k ).

5. The grid-connection control method based on the optimal allocation of grid-connection indicators for gravity energy storage synchronous motors according to claim 4, characterized in that, In step 5), after the accuracy of the terminal voltage index data of the gravity energy storage synchronous motor is maximized, the three sets of data are analyzed through multiple linear regression to reduce the difficulty of judging the accuracy of the data. By setting the three sets of data conditions to just complete the data set Y when connected to the grid, the data is further filtered. Based on the multiple linear regression algorithm, the dependent variable is set as the data set Y of three sets of data when grid connection can be completed under the conditions; the three independent variables are then analyzed again: synchronous motor open-circuit voltage and frequency data (f jmo …f knp ), synchronous motor output voltage phase data (φ) jmo …φ knp ), synchronous motor output voltage amplitude data (V jmo …V knp The effect of ), on the dependent variable Y; In the formula, Y is the data set when the three sets of data conditions are just sufficient to complete grid connection, and β0 is the intercept; β i For x i The regression coefficients are given by i = 1, 2, 3, and ε is the residual term. The regression coefficients are calculated by the least squares method. Where X is: In the formula, β = [β1, β2, β3] T ;Y=[y jmo ,……y knp ] T , which is the response variable of the data, finally yields the set Y of the three selected sets of grid connection conditions, thus completing the optimal and precise processing of the data.

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