A compatible control optimization method for submersible permanent magnet motor
By real-time monitoring and historical data analysis of the submersible permanent magnet motor, the control of the direct shaft current and torque current is optimized, solving the problem of inaccurate motor control in the existing technology and realizing efficient torque regulation in high-temperature environments.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- 天津市百成油田采油设备制造有限公司
- Filing Date
- 2025-06-29
- Publication Date
- 2026-08-04
AI Technical Summary
Existing control optimization technologies for submersible permanent magnet motors cannot optimize and regulate them in a timely manner, resulting in insufficient current control and an inability to ensure accurate torque delivery, especially under high-temperature conditions.
By monitoring the working status of the submersible permanent magnet motor in real time and obtaining working parameters, the relationship between direct shaft current and real-time temperature, torque current and real-time temperature, direct shaft current and required torque, and torque current and required torque is analyzed based on historical operating data, and the control of direct shaft current and torque current is optimized.
It improves the efficiency and accuracy of submersible permanent magnet motor control optimization, and can fine-tune the current in a timely manner according to temperature changes to ensure that the motor reaches the accurate torque.
Smart Images
Figure CN120710408B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of submersible permanent magnet motor control optimization technology, specifically a compatible control optimization method for submersible permanent magnet motors. Background Technology
[0002] Submersible permanent magnet motor control optimization technology refers to the regulation technology that optimizes the operating parameters of submersible permanent magnet motors through a series of monitoring and calculations. Its core purpose is to ensure that the submersible permanent magnet motor can maintain its optimal working state when working in the oil well, and to ensure that its torque and copper loss parameters are within the normal range.
[0003] Because submersible permanent magnet motors (SPMs) operate thousands of meters deep in oil wells, temperature variations affect their operating parameters. If these parameters are controlled using existing relational models, excessive control precision errors will occur, causing the motor to lose synchronization and fail to reach its target state. Current SPM control optimization technologies typically monitor the actual magnetic force and wire impedance within the motor to control its operating parameters. However, monitoring magnetic force and impedance is extremely difficult in the operating environment of SPMs, and the calculation process for the required operating parameters from magnetic force and impedance is highly complex, making timely adjustment of the SPM impossible. Furthermore, existing SPM control optimization technologies also rely on torque and current... The fixed relationship used in traditional oil-filled permanent magnet motors (PLMs) for fixed-axis current and torque current control does not take into account the effects of high temperature on magnetic force and impedance. This results in the original direct-axis current and torque current failing to reach the desired torque. For example, the patent application CN118393348A discloses a "parameter testing method for oil-filled permanent magnet motors." This method tests the motor using rated frequency and rated torque, but it does not consider the impact of high temperature on the motor, leading to a significant difference between the test results and the actual values. Existing control optimization technologies for oil-filled permanent magnet motors also suffer from the inability to optimize and control the motor in a timely manner and the insufficient precision in current control, resulting in the inability to ensure that the motor reaches the accurate torque in a timely manner. Summary of the Invention
[0004] This invention aims to at least partially solve one of the technical problems in the prior art. It involves real-time monitoring of the operating status of a submersible permanent magnet motor to obtain operating parameters. Then, based on historical operating data, it analyzes the relationship between the direct-axis current and real-time temperature, as well as the relationship between torque current and real-time temperature. Furthermore, it analyzes the relationship between the direct-axis current and the required torque, and finally, based on the current-torque relationship and the current-temperature relationship, it optimizes the control of the direct-axis current and torque current. This addresses the problem that existing submersible permanent magnet motor control optimization technologies cannot optimize and regulate the motor in a timely manner, and the current control of the motor is not precise enough, resulting in the submersible permanent magnet motor failing to ensure timely and accurate torque delivery.
[0005] To achieve the above objectives, this application provides a method for compatible control optimization of a submersible permanent magnet motor, comprising the following steps:
[0006] Real-time monitoring of the operating status of the submersible permanent magnet motor to obtain operating parameters;
[0007] Based on historical operating data analysis, the relationship between direct-axis current and torque current and real-time temperature is named the current-temperature relationship.
[0008] Based on historical operating data analysis, the relationship between direct-axis current and torque current and demand torque is named the current-torque relationship.
[0009] The direct-axis current and torque current are optimized and controlled based on the current-torque relationship and the current-temperature relationship.
[0010] Furthermore, the operating parameters include direct-axis real-time current, torque real-time current, required torque, and real-time temperature.
[0011] Furthermore, analyzing the relationship between direct-axis current and torque current and real-time temperature based on historical operating data includes the following sub-steps:
[0012] Analyze the relationship between direct-axis current and real-time temperature based on historical operating data;
[0013] The relationship between torque current and real-time temperature was analyzed based on historical operating data.
[0014] Furthermore, analyzing the relationship between direct-axis current and real-time temperature based on historical operating data includes the following sub-steps:
[0015] The historical operating data records historical data of direct-axis real-time current, torque real-time current, required torque, and real-time temperature, which are named direct-axis historical current, torque historical current, historical torque, and historical temperature, respectively. The direct-axis historical current, torque historical current, historical torque, and historical temperature constitute a historical operating data.
[0016] The number of different historical torques is counted and named as the number of torques. The maximum value among the number of torques is found and marked as the maximum torque. The historical torque corresponding to the maximum torque is named the optimal torque. The historical operating data where the historical torque is equal to the optimal torque is marked as the data applicable to analysis.
[0017] A two-dimensional coordinate system is established with historical temperature as the X-axis and direct-axis historical current as the Y-axis, named the direct-axis fluctuation graph. The direct-axis historical current in the applicable data is entered into the direct-axis fluctuation graph according to the historical temperature.
[0018] Count the number of different historical temperatures and name them as temperature counts. Find the maximum value among the temperature counts and mark it as the most frequent temperature. Name the historical temperature corresponding to the most frequent temperature as the normal temperature.
[0019] Discrete regression analysis was performed on the direct-axis fluctuation diagram to obtain the direct-axis fluctuation function. The direct-axis conventional current was obtained by substituting the historical temperature equal to the conventional temperature into the direct-axis fluctuation function.
[0020] The minimum and maximum historical temperatures in the applicable data for analysis are labeled as MinT and MaxT, respectively. MinT and MaxT are substituted into the direct-axis fluctuation function, and the calculation results are labeled as EF1 and EF2, respectively.
[0021] Compare the magnitudes of EF1 and EF2. If EF1 < EF2, then name EF1 the direct-axis low-level current and EF2 the direct-axis high-level current. Otherwise, name EF1 the direct-axis high-level current and EF2 the direct-axis low-level current.
[0022] The direct-axis fluctuation function, direct-axis conventional current, direct-axis low-level current, and direct-axis high-level current together constitute the direct-axis temperature relationship.
[0023] Furthermore, analyzing the relationship between torque current and real-time temperature based on historical operating data includes the following sub-steps:
[0024] A two-dimensional coordinate system is established with historical temperature as the X-axis and historical torque current as the Y-axis, named Torque Fluctuation Chart. The historical torque current in the applicable data for analysis is entered into the Torque Fluctuation Chart according to the historical temperature.
[0025] Discrete regression analysis was performed on the torque fluctuation diagram to obtain the torque fluctuation function. By substituting the historical temperature equal to the normal temperature into the torque fluctuation function, the torque normal current was obtained by solving.
[0026] Substitute MinT and MaxT into the torque ripple function, and label the calculation results as TF1 and TF2, respectively.
[0027] Compare the magnitudes of TF1 and TF2. If TF1 < TF2, then TF1 is named the low torque current and TF2 is named the high torque current. Otherwise, TF1 is named the high torque current and TF2 is named the low torque current.
[0028] The torque fluctuation function, the normal torque current, the low torque current, and the high torque current together constitute the torque-temperature relationship, and the direct-axis temperature relationship and the torque-temperature relationship together constitute the current-temperature relationship.
[0029] Furthermore, based on historical operating data analysis, the relationship between direct-axis current and torque current and the required torque is named the current-torque relationship and includes the following sub-steps:
[0030] Analyze the relationship between direct-axis current and required torque based on historical operating data;
[0031] The relationship between torque current and required torque is analyzed based on historical operating data.
[0032] Furthermore, analyzing the relationship between direct-axis current and required torque based on historical operating data includes the following sub-steps:
[0033] Obtain all historical operating data and establish a two-dimensional coordinate system with demanded torque as the horizontal axis and direct current as the vertical axis, named the Direct Axis Trend Chart.
[0034] Record the historical current of the direct axis from the historical operating data into the direct axis trend chart according to the historical torque;
[0035] Discrete regression analysis is performed on the direct axis trend graph to obtain the direct axis trend function, which is the direct axis torque relationship.
[0036] Furthermore, analyzing the relationship between torque current and required torque based on historical operating data includes the following sub-steps:
[0037] Obtain all historical operating data and establish a two-dimensional coordinate system with the required torque as the horizontal axis and the torque current as the vertical axis, named Torque Trend Chart;
[0038] Enter the historical torque and historical current from the historical operating data into the torque trend graph according to the historical torque.
[0039] Discrete regression analysis is performed on the torque trend graph to obtain the torque trend function, which is the torque-torque relationship. The direct-axis torque relationship and the torque-torque relationship together constitute the current-torque relationship.
[0040] Furthermore, the optimized control of the direct-axis current and torque current based on the current-torque relationship and the current-temperature relationship includes the following sub-steps:
[0041] Obtain the required torque and real-time temperature of the submersible permanent magnet motor;
[0042] Substitute the real-time temperature into the direct-axis temperature function and the torque temperature function respectively, and name the calculation results as the target direct-axis ripple current and the target torque ripple current respectively, represented by the symbols TFE and TFT respectively;
[0043] The symbol ECC represents the direct-axis conventional current, the symbol CTC represents the torque conventional current, the symbols HE and LE represent the direct-axis high-level current and direct-axis low-level current respectively, and the symbols HT and LT represent the torque high-level current and torque low-level current respectively.
[0044] Calculate TFE / ECC and label the result as EQ; calculate TFT / CTC and label the result as TQ; calculate TFE / HE and label the result as EHQ; calculate TFE / LE and label the result as ELQ; calculate TFT / HT and label the result as THQ; calculate TFT / LT and label the result as TLQ.
[0045] Based on the calculated EQ, TQ, EHQ, ELQ, THQ and TLQ, the direct-axis current and torque current are optimized and controlled in combination with the current-torque relationship.
[0046] Furthermore, based on the calculated EQ, TQ, EHQ, ELQ, THQ, and TLQ, and combined with the current-torque relationship, the optimized control of the direct-axis current and torque current includes the following sub-steps:
[0047] Substitute the required torque into the direct-axis torque function and the torque-torque function respectively, and name the results obtained by solving them as the direct-axis ideal current and the torque ideal current, respectively, and represent them by the symbols IE and IT.
[0048] Name the coordinate points in the direct-axis torque diagram and the torque-torque diagram as direct-axis torque points and torque-torque points, respectively. Find the direct-axis torque points and torque-torque points whose horizontal axis equals the required torque, and name them as direct-axis effective points and torque effective points, respectively.
[0049] Find the minimum value of the vertical axis among the effective points of the vertical axis, name it the lowest point of the vertical axis, and mark it as MIE. Find the maximum value of the vertical axis among the effective points of the vertical axis, name it the highest point of the vertical axis, and mark it as MAE. Find the minimum value of the vertical axis among the effective points of the torque, name it the lowest point of the torque, and mark it as MIT. Find the maximum value of the vertical axis among the effective points of the torque, name it the highest point of the torque, and mark it as MAT.
[0050] Determine the relationship between MIE and MAE and IE. If MIE < IE and MAE > IE, calculate (MIE × ELQ + IE × EQ + MAE × EHQ) / 3 to obtain the optimal current along the direct axis. If MIE ≥ IE and MAE > IE, calculate (IE × EQ + MAE × EHQ) / 2 to obtain the optimal current along the direct axis. If MIE < IE and MAE ≤ IE, calculate (MIE × ELQ + IE × EQ) / 2 to obtain the optimal current along the direct axis.
[0051] Determine the relationship between MIE, MAE, and IE. If MIT < IT and MAT > IT, calculate (MIT × TLQ + IT × TQ + MAT × THQ) / 3 to obtain the optimal torque current. If MIT ≥ IT and MAT > IT, calculate (IT × TQ + MAT × THQ) / 2 to obtain the optimal torque current. If MIT < IT and MAT ≤ IT, calculate (MIT × TLQ + IT × TQ) / 2 to obtain the optimal torque current.
[0052] Adjust the direct-axis current to the optimal direct-axis current, and at the same time adjust the torque current to the optimal torque current.
[0053] The beneficial effects of this invention are as follows: This invention monitors the working status of a submersible permanent magnet motor in real time to obtain working parameters, and then analyzes the relationship between the direct-axis current and real-time temperature based on historical operating data. Simultaneously, it analyzes the relationship between the torque current and real-time temperature based on historical operating data. The advantage lies in the fact that high temperatures affect the magnetic force and impedance of the submersible permanent magnet motor, which can further prevent it from achieving the required torque. Therefore, the direct-axis current and torque current of the submersible permanent magnet motor can be finely adjusted in a timely manner based on temperature. This method allows for calibration of the direct-axis current and torque current based on the variation range under different temperature conditions, improving the efficiency and accuracy of submersible permanent magnet motor control optimization.
[0054] This invention analyzes the relationship between direct-axis current and required torque based on historical operating data, and simultaneously analyzes the relationship between torque current and required torque based on historical operating data. Finally, it optimizes the control of direct-axis current and torque current based on the current-torque relationship and the current-temperature relationship. The advantage lies in the fact that it calculates the ideal values of direct-axis current and torque current through the direct-axis torque relationship and the torque-torque relationship, and then fine-tunes them by combining the direct-axis temperature relationship and the torque-temperature relationship. This allows for timely and accurate adjustment of direct-axis current and torque current, further improving the accuracy and efficiency of submersible permanent magnet motor control optimization. Attached Figure Description
[0055] Figure 1 This is a flowchart illustrating the steps of the method of the present invention;
[0056] Figure 2 This is the direct-axis fluctuation diagram of the present invention;
[0057] Figure 3 This is a linear trend chart of the present invention;
[0058] Figure 4 This is a schematic diagram of the electronic device of the present invention. Detailed Implementation
[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0060] Example 1, please refer to Figure 1 As shown, this application provides a method for compatible control optimization of a submersible permanent magnet motor, including the following steps:
[0061] Step S1: Monitor the working status of the submersible permanent magnet motor in real time and obtain the working parameters; the working parameters include the real-time current of the direct shaft, the real-time torque current, the required torque, and the real-time temperature.
[0062] In practice, monitoring the magnetic force and impedance of the submersible permanent magnet motor is quite difficult, but monitoring the real-time current of the direct shaft, the real-time torque current, the required torque, and the real-time temperature is relatively simple and can be calculated quickly, enabling the controller to quickly regulate the submersible permanent magnet motor.
[0063] Step S2 involves analyzing the relationship between direct-axis current and torque current and real-time temperature based on historical operating data, naming this relationship "current-temperature relationship." Step S2 includes the following sub-steps:
[0064] Step S201: Analyze the relationship between direct-axis current and real-time temperature based on historical operating data;
[0065] Step S201 includes the following sub-steps:
[0066] Step S201.1: The historical operating data records the historical data of direct shaft real-time current, torque real-time current, required torque and real-time temperature, which are named direct shaft historical current, torque historical current, historical torque and historical temperature respectively. The direct shaft historical current, torque historical current, historical torque and historical temperature constitute a historical operating data.
[0067] Step S201.2: Count the number of different historical torques and name them as torque quantity. Find the maximum value among the torque quantities and mark it as the maximum torque. Name the historical torque corresponding to the maximum torque as the optimal torque. Mark the historical running data where the historical torque is equal to the optimal torque as the data applicable to analysis.
[0068] In practice, finding the optimal torque is to find the data with the same required torque and the largest amount of data from a large amount of historical operating data as the applicable data for analysis. This is used to analyze the impact of real-time temperature on direct-axis current and torque current under the same required torque. Due to the large amount of data, it is not convenient to show it in detail in this embodiment. In this embodiment, only the direct-axis fluctuation diagram and torque fluctuation diagram are used to simply show the applicable data for analysis.
[0069] Please see Figure 2 As shown, in step S201.3, a two-dimensional coordinate system is established with historical temperature as the X-axis and direct-axis historical current as the Y-axis, named the direct-axis fluctuation graph. The direct-axis historical current in the applicable data is entered into the direct-axis fluctuation graph according to the historical temperature.
[0070] Step S201.4: Count the number of different historical temperatures and name them as temperature quantity. Find the maximum value in the temperature quantity and mark it as the maximum temperature. Name the historical temperature corresponding to the maximum temperature as the normal temperature.
[0071] Step S201.5: Perform discrete regression analysis on the direct-axis fluctuation diagram to obtain the direct-axis fluctuation function. Substitute the historical temperature equal to the normal temperature into the direct-axis fluctuation function to solve for the direct-axis normal current.
[0072] In practice, the direct-axis fluctuation diagram is constructed as follows: Figure 2 As shown, discrete regression analysis of the direct-axis fluctuation plot yields the direct-axis fluctuation function as YE = 0.006 × XE. 2 -0.9405×XE+237.14, where YE is the direct-axis historical current and XE is the historical temperature, from Figure 2 It can be observed that the coordinate points with historical temperatures between 100℃ and 120℃ are the most numerous, indicating that the submersible permanent magnet motor often operates within the range of 100℃ to 120℃. By statistically analyzing the number of temperatures, the normal operating temperature is found to be 115℃. Substituting XE = 115 into the direct-axis fluctuation function, the normal direct-axis current is calculated to be 208A. The normal direct-axis current is calculated because the submersible motor has been operating at 115℃ for the longest time, resulting in the most historical operating data, which has the greatest impact on the subsequent direct-axis torque relationship. Therefore, the direct-axis torque relationship is closer to the relationship between the direct-axis current and the required torque at 115℃. The direct-axis current calculated from the direct-axis torque relationship can be calibrated by combining the normal direct-axis current, the low-level direct-axis current, and the high-level direct-axis current.
[0073] Step S201.6: Mark the minimum and maximum historical temperatures in the applicable data as MinT and MaxT, respectively. Substitute MinT and MaxT into the direct-axis fluctuation function, and mark the calculation results as EF1 and EF2, respectively.
[0074] Step S201.7: Compare the magnitudes of EF1 and EF2. If EF1 < EF2, then name EF1 as the direct-axis low-level current and EF2 as the direct-axis high-level current; otherwise, name EF1 as the direct-axis high-level current and EF2 as the direct-axis low-level current.
[0075] Step S201.8: The direct-axis fluctuation function, the direct-axis conventional current, the direct-axis low-level current, and the direct-axis high-level current together constitute the direct-axis temperature relationship;
[0076] In specific implementation, by Figure 2 It is known that the direct-axis current increases with the increase of real-time temperature. Therefore, the values calculated at MinT and MaxT are the minimum and maximum values of the direct-axis current. MinT and MaxT are obtained as 80℃ and 150℃, respectively. Solving for EF1 and EF2, we get 200A and 231A, respectively. Thus, the low-level direct-axis current is 200A and the high-level direct-axis current is 231A.
[0077] Step S202: Analyze the relationship between torque current and real-time temperature based on historical operating data;
[0078] Step S202 includes the following sub-steps:
[0079] Step S202.1: Establish a two-dimensional coordinate system with historical temperature as the X-axis and historical torque current as the Y-axis, named Torque Fluctuation Chart. Enter the historical torque current from the applicable data into the Torque Fluctuation Chart according to the historical temperature.
[0080] Step S202.2: Perform discrete regression analysis on the torque fluctuation diagram to obtain the torque fluctuation function. Substitute the historical temperature equal to the normal temperature into the torque fluctuation function to solve for the torque normal current.
[0081] Step S202.3: Substitute MinT and MaxT into the torque ripple function respectively, and label the calculation results as TF1 and TF2 respectively;
[0082] Step S202.4: Compare the magnitudes of TF1 and TF2. If TF1 < TF2, then name TF1 as the low torque current and TF2 as the high torque current; otherwise, name TF1 as the high torque current and TF2 as the low torque current.
[0083] Step S202.5: The torque fluctuation function, the normal torque current, the low torque current, and the high torque current together constitute the torque-temperature relationship; the direct-axis temperature relationship and the torque-temperature relationship together constitute the current-temperature relationship.
[0084] In practice, since the analysis process and principle of torque-temperature relationship are exactly the same as those of direct-axis temperature relationship, this embodiment will not provide a specific explanation, but will only take the analysis process of direct-axis temperature relationship as an example.
[0085] Step S3 involves analyzing the relationship between the direct-axis current and torque current and the required torque based on historical operating data, naming this relationship the current-torque relationship. Step S3 includes the following sub-steps:
[0086] Step S301: Analyze the relationship between direct-axis current and required torque based on historical operating data;
[0087] Step S301 includes the following sub-steps:
[0088] Please see Figure 3 As shown, in step S301.1, all historical operating data are obtained, and a two-dimensional coordinate system is established with the required torque as the horizontal axis and the vertical axis current as the vertical axis, named the vertical axis trend chart.
[0089] Step S301.2: Enter the historical current of the direct axis from the historical operating data into the direct axis trend graph according to the historical torque;
[0090] Step S301.3: Perform discrete regression analysis on the direct axis trend graph to obtain the direct axis trend function, which is the direct axis torque relationship;
[0091] In practice, the resulting linear trend chart is as follows: Figure 3 As shown, the direct-axis trend graph reflects the relationship between direct-axis current and required torque. Under the same required torque, the direct-axis current exhibits certain fluctuations due to the influence of the high-temperature operating environment. Therefore, subsequent analysis needs to incorporate the direct-axis temperature relationship and calibrate the direct-axis current. The resulting direct-axis trend function is YER = 0.003 × XER. 2 -0.23×XER+175, where YER is the direct-axis historical current and XER is the historical torque;
[0092] Step S302: Analyze the relationship between torque current and required torque based on historical operating data;
[0093] Step S302 includes the following sub-steps:
[0094] Step S302.1: Obtain all historical operating data, establish a two-dimensional coordinate system with the required torque as the horizontal axis and the torque current as the vertical axis, and name it Torque Trend Chart;
[0095] Step S302.2: Enter the historical torque current from the historical operating data into the torque trend graph according to the historical torque.
[0096] Step S302.3: Perform discrete regression analysis on the torque trend graph to obtain the torque trend function. The torque trend function is the torque-torque relationship. The direct-axis torque relationship and the torque-torque relationship together form the current-torque relationship.
[0097] In practice, since the analysis process and principle of torque-torque relationship are exactly the same as those of direct-axis torque relationship, this embodiment will not provide a specific explanation, but will only take the analysis process of direct-axis torque relationship as an example.
[0098] Step S4 involves optimizing the control of the direct-axis current and torque current based on the current-torque relationship and the current-temperature relationship. Step S4 includes the following sub-steps:
[0099] Step S401: Obtain the required torque and real-time temperature of the submersible permanent magnet motor;
[0100] Step S402: Substitute the real-time temperature into the direct-axis temperature function and the torque temperature function respectively, and name the calculation results as the target direct-axis ripple current and the target torque ripple current respectively, represented by the symbols TFE and TFT respectively;
[0101] Step S403: The symbol ECC represents the direct-axis conventional current, the symbol CTC represents the torque conventional current, the symbols HE and LE represent the direct-axis high-level current and the direct-axis low-level current, respectively, and the symbols HT and LT represent the torque high-level current and the torque low-level current, respectively.
[0102] Step S404: Calculate TFE / ECC and label the result as EQ; calculate TFT / CTC and label the result as TQ; calculate TFE / HE and label the result as EHQ; calculate TFE / LE and label the result as ELQ; calculate TFT / HT and label the result as THQ; calculate TFT / LT and label the result as TLQ.
[0103] In specific implementation, since the processing of torque-temperature relationship and torque-torque relationship in this embodiment is exactly the same as the processing of direct-axis temperature relationship and direct-axis torque relationship, this embodiment only uses the processing of direct-axis temperature relationship and direct-axis torque relationship as an example for illustration; The required torque is obtained as 150 Nm, and the real-time temperature is 130℃. Substituting XE = 130 into YE = 0.006 × XE 2-0.9405×XE+237.14, solving for TFE yields 216A, rounded to the nearest integer; ECC yields 208A; HE yields 231A; LE yields 200A; EQ yields 1.0385; EHQ yields 0.9351; ELQ yields 1.08, rounded to four decimal places.
[0104] Step S405: Based on the calculated EQ, TQ, EHQ, ELQ, THQ and TLQ, optimize the control of direct-axis current and torque current in combination with the current-torque relationship;
[0105] Step S405 includes the following sub-steps:
[0106] Step S405.1: Substitute the required torque into the direct-axis torque function and the torque-torque function respectively, and name the results obtained by solving them as the direct-axis ideal current and the torque ideal current respectively, and represent them by the symbols IE and IT respectively;
[0107] Step S405.2: Name the coordinate points in the direct axis torque diagram and the torque torque diagram as direct axis torque points and torque torque points, respectively. Find the direct axis torque points and torque torque points whose horizontal axis is equal to the required torque, and name them as direct axis effective points and torque effective points, respectively.
[0108] In practice, XER = 150 is substituted into YER = 0.003 × XER. 2 Solving for -0.23×XER+175 yields an ideal direct-axis current IE of 208A. The direct-axis torque point at XER=150 is the effective direct-axis point. Under the same required torque, the direct-axis current of the submersible permanent magnet motor fluctuates due to high temperatures. With sufficient historical operating data, records show the submersible permanent magnet motor operating at temperatures above and below normal for a given required torque. This data can be used to calibrate the ideal direct-axis current in conjunction with EHQ and ELQ. However, when historical operating data is insufficient, the submersible permanent magnet motor operates above or below normal temperature for certain required torques. In this case, only EHQ and E... One of the LQ methods calibrates the direct-axis ideal current. For example, when the required torque is 170 Nm, the effective points on the direct axis are all below the curve corresponding to the direct-axis torque function, which means that the vertical axis of the effective points on the direct axis is lower than the direct-axis ideal current. In this case, the direct-axis ideal current can only be calibrated by ELQ, and cannot be calibrated by EHQ. This is because the direct-axis torque function is most affected by normal temperature, and the calculated direct-axis ideal current is closer to the value at normal temperature. If the effective points on the direct axis are below the direct-axis ideal current, it means that the historical temperature of the effective points on the direct axis is lower than the normal temperature. EHQ is calculated from data above the normal temperature, so it cannot be used. The analysis process for torque ideal current is similar.
[0109] Step S405.3: Find the minimum value of the vertical axis among the effective points of the direct axis, name it the lowest point of the direct axis, and mark it as MIE; find the maximum value of the vertical axis among the effective points of the direct axis, name it the highest point of the direct axis, and mark it as MAE; find the minimum value of the vertical axis among the effective points of the torque, name it the lowest point of the torque, and mark it as MIT; find the maximum value of the vertical axis among the effective points of the torque, name it the highest point of the torque, and mark it as MAT.
[0110] Step S405.4: Determine the relationship between MIE and MAE and IE. If MIE < IE and MAE > IE, calculate (MIE × ELQ + IE × EQ + MAE × EHQ) / 3 to obtain the optimal current on the direct axis. If MIE ≥ IE and MAE > IE, calculate (IE × EQ + MAE × EHQ) / 2 to obtain the optimal current on the direct axis. If MIE < IE and MAE ≤ IE, calculate (MIE × ELQ + IE × EQ) / 2 to obtain the optimal current on the direct axis.
[0111] Step S405.5: Determine the relationship between MIE and MAE and IE. If MIT < IT and MAT > IT, calculate (MIT × TLQ + IT × TQ + MAT × THQ) / 3 to obtain the optimal torque current. If MIT ≥ IT and MAT > IT, calculate (IT × TQ + MAT × THQ) / 2 to obtain the optimal torque current. If MIT < IT and MAT ≤ IT, calculate (MIT × TLQ + IT × TQ) / 2 to obtain the optimal torque current.
[0112] Step S405.6: Adjust the direct-axis current to the optimal direct-axis current, and at the same time adjust the torque current to the optimal torque current;
[0113] In the specific implementation, the lowest point MIE of the direct axis is obtained as 206A, the highest point MAE of the direct axis is 217A, and IE = 208A. It is determined that MIE < IE and MAE > IE. The optimal current of the direct axis is obtained by calculating (MIE × ELQ + IE × EQ + MAE × EHQ) / 3, which is 214A. The calculation result is rounded to the nearest integer, and the direct axis current of the submersible permanent magnet motor is adjusted to 214A. The analysis process for the optimal torque current is exactly the same as the analysis process for the optimal direct axis current, so it will not be described in detail in this embodiment.
[0114] Example 2, please refer to Figure 4 As shown, Figure 4A schematic diagram of an electronic device is provided, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps as described in a submersible permanent magnet motor compatible control optimization method to achieve the following functions: real-time monitoring of the submersible permanent magnet motor's operating status to obtain operating parameters; analysis of the relationship between the direct-axis current and torque current and real-time temperature based on historical operating data, termed the current-temperature relationship; analysis of the relationship between the direct-axis current and torque current and the required torque based on historical operating data, termed the current-torque relationship; and optimized control of the direct-axis current and torque current based on the current-torque relationship and the current-temperature relationship.
[0115] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0116] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute a submersible permanent magnet motor compatible control optimization method provided by the above methods. The method includes: real-time monitoring of the operating state of the submersible permanent magnet motor to obtain operating parameters; analyzing the relationship between the direct-axis current and torque current and the real-time temperature based on historical operating data, naming it the current-temperature relationship; analyzing the relationship between the direct-axis current and torque current and the required torque based on historical operating data, naming it the current-torque relationship; and optimizing the control of the direct-axis current and torque current based on the current-torque relationship and the current-temperature relationship.
[0117] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps of the above-described submersible permanent magnet motor compatible control optimization method to achieve the following functions: real-time monitoring of the operating status of the submersible permanent magnet motor and obtaining operating parameters; analysis of the relationship between the direct-axis current and torque current and the real-time temperature based on historical operating data, named the current-temperature relationship; analysis of the relationship between the direct-axis current and torque current and the required torque based on historical operating data, named the current-torque relationship; and optimized control of the direct-axis current and torque current based on the current-torque relationship and the current-temperature relationship.
[0118] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the technical solutions described above, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products can be stored in computer-readable storage media, such as ROM / RAM, magnetic disks, optical disks, etc., and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.
[0119] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.
[0120] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A method for compatible control optimization of a submersible permanent magnet motor, characterized in that, Includes the following steps: Real-time monitoring of the operating status of the submersible permanent magnet motor to obtain operating parameters; Based on historical operating data analysis, the relationship between direct-axis current and torque current and real-time temperature is named the current-temperature relationship. Based on historical operating data analysis, the relationship between direct-axis current and torque current and demand torque is named the current-torque relationship. Optimized control of direct-axis current and torque current is performed based on the current-torque relationship and the current-temperature relationship. Analyzing the relationship between direct-axis current, torque current, and real-time temperature based on historical operating data includes the following sub-steps: Analyze the relationship between direct-axis current and real-time temperature based on historical operating data; Analyze the relationship between torque current and real-time temperature based on historical operating data; Analyzing the relationship between direct-axis current and real-time temperature based on historical operating data includes the following sub-steps: The historical operating data records historical data of direct-axis real-time current, torque real-time current, required torque, and real-time temperature, which are named direct-axis historical current, torque historical current, historical torque, and historical temperature, respectively. The direct-axis historical current, torque historical current, historical torque, and historical temperature constitute a historical operating data. The number of different historical torques is counted and named as the number of torques. The maximum value among the number of torques is found and marked as the maximum torque. The historical torque corresponding to the maximum torque is named the optimal torque. The historical operating data where the historical torque is equal to the optimal torque is marked as the data applicable to analysis. A two-dimensional coordinate system is established with historical temperature as the X-axis and direct-axis historical current as the Y-axis, named the direct-axis fluctuation graph. The direct-axis historical current in the applicable data is entered into the direct-axis fluctuation graph according to the historical temperature. Count the number of different historical temperatures and name them as temperature counts. Find the maximum value among the temperature counts and mark it as the most frequent temperature. Name the historical temperature corresponding to the most frequent temperature as the normal temperature. Discrete regression analysis was performed on the direct-axis fluctuation diagram to obtain the direct-axis fluctuation function. The direct-axis conventional current was obtained by substituting the historical temperature equal to the conventional temperature into the direct-axis fluctuation function. The minimum and maximum historical temperatures in the applicable data for analysis are labeled as MinT and MaxT, respectively. MinT and MaxT are substituted into the direct-axis fluctuation function, and the calculation results are labeled as EF1 and EF2, respectively. Compare the magnitudes of EF1 and EF2. If EF1 < EF2, then name EF1 the direct-axis low-level current and EF2 the direct-axis high-level current. Otherwise, name EF1 the direct-axis high-level current and EF2 the direct-axis low-level current. The direct-axis fluctuation function, the direct-axis conventional current, the direct-axis low-level current, and the direct-axis high-level current together constitute the direct-axis temperature relationship. Analyzing the relationship between torque current and real-time temperature based on historical operating data includes the following sub-steps: A two-dimensional coordinate system is established with historical temperature as the X-axis and historical torque current as the Y-axis, named Torque Fluctuation Chart. The historical torque current in the applicable data for analysis is entered into the Torque Fluctuation Chart according to the historical temperature. Discrete regression analysis was performed on the torque fluctuation diagram to obtain the torque fluctuation function. By substituting the historical temperature equal to the normal temperature into the torque fluctuation function, the torque normal current was obtained by solving. Substitute MinT and MaxT into the torque ripple function, and label the calculation results as TF1 and TF2, respectively. Compare the magnitudes of TF1 and TF2. If TF1 < TF2, then TF1 is named the low torque current and TF2 is named the high torque current. Otherwise, TF1 is named the high torque current and TF2 is named the low torque current. The torque fluctuation function, the normal torque current, the low torque current, and the high torque current together constitute the torque-temperature relationship, and the direct-axis temperature relationship and the torque-temperature relationship together constitute the current-temperature relationship. Based on historical operating data analysis, the relationship between direct-axis current, torque current, and required torque is named the current-torque relationship and includes the following sub-steps: Analyze the relationship between direct-axis current and required torque based on historical operating data; Analyze the relationship between torque current and demand torque based on historical operating data; Analyzing the relationship between direct-axis current and required torque based on historical operating data includes the following sub-steps: Obtain all historical operating data and establish a two-dimensional coordinate system with demanded torque as the horizontal axis and direct current as the vertical axis, named the Direct Axis Trend Chart. Record the historical current of the direct axis from the historical operating data into the direct axis trend chart according to the historical torque; Discrete regression analysis is performed on the direct axis trend graph to obtain the direct axis trend function, which is the direct axis torque relationship. Analyzing the relationship between torque current and demand torque based on historical operating data includes the following sub-steps: Obtain all historical operating data and establish a two-dimensional coordinate system with the required torque as the horizontal axis and the torque current as the vertical axis, named Torque Trend Chart; Enter the historical torque and historical current from the historical operating data into the torque trend graph according to the historical torque. Discrete regression analysis is performed on the torque trend graph to obtain the torque trend function, which is the torque-torque relationship. The direct-axis torque relationship and the torque-torque relationship together constitute the current-torque relationship.
2. The compatible control optimization method for the submersible permanent magnet motor according to claim 1, characterized in that, The operating parameters include real-time direct-axis current, real-time torque current, required torque, and real-time temperature.
3. The compatible control optimization method of the submersible permanent magnet motor according to claim 2, characterized in that, Optimization control of direct-axis current and torque current based on current-torque relationship and current-temperature relationship includes the following sub-steps: Obtain the required torque and real-time temperature of the submersible permanent magnet motor; Substitute the real-time temperature into the direct-axis temperature relationship and the torque-temperature relationship respectively, and name the calculation results as the target direct-axis ripple current and the target torque ripple current respectively, represented by the symbols TFE and TFT respectively; The symbol ECC represents the direct-axis conventional current, the symbol CTC represents the torque conventional current, the symbols HE and LE represent the direct-axis high-level current and direct-axis low-level current respectively, and the symbols HT and LT represent the torque high-level current and torque low-level current respectively. Calculate TFE / ECC and label the result as EQ; calculate TFT / CTC and label the result as TQ; calculate TFE / HE and label the result as EHQ; calculate TFE / LE and label the result as ELQ; calculate TFT / HT and label the result as THQ; calculate TFT / LT and label the result as TLQ. Based on the calculated EQ, TQ, EHQ, ELQ, THQ and TLQ, the direct-axis current and torque current are optimized and controlled in combination with the current-torque relationship.
4. The compatible control optimization method of the submersible permanent magnet motor according to claim 3, characterized in that, Based on the calculated EQ, TQ, EHQ, ELQ, THQ, and TLQ, the optimized control of the direct-axis current and torque current, combined with the current-torque relationship, includes the following sub-steps: Substitute the required torque into the direct-axis torque relationship and the torque-torque relationship respectively, and name the results obtained by solving them as the direct-axis ideal current and the torque ideal current, respectively, and represent them by the symbols IE and IT. Name the coordinate points in the direct-axis torque diagram and the torque-torque diagram as direct-axis torque points and torque-torque points, respectively. Find the direct-axis torque points and torque-torque points whose horizontal axis equals the required torque, and name them as direct-axis effective points and torque effective points, respectively. Find the minimum value of the vertical axis among the effective points of the vertical axis, name it the lowest point of the vertical axis, and mark it as MIE. Find the maximum value of the vertical axis among the effective points of the vertical axis, name it the highest point of the vertical axis, and mark it as MAE. Find the minimum value of the vertical axis among the effective points of the torque, name it the lowest point of the torque, and mark it as MIT. Find the maximum value of the vertical axis among the effective points of the torque, name it the highest point of the torque, and mark it as MAT. Determine the relationship between MIE and MAE and IE. If MIE < IE and MAE > IE, calculate (MIE × ELQ + IE × EQ + MAE × EHQ) / 3 to obtain the optimal current along the direct axis. If MIE ≥ IE and MAE > IE, calculate (IE × EQ + MAE × EHQ) / 2 to obtain the optimal current along the direct axis. If MIE < IE and MAE ≤ IE, calculate (MIE × ELQ + IE × EQ) / 2 to obtain the optimal current along the direct axis. Determine the relationship between MIT, MAT, and IT. If MIT < IT and MAT > IT, calculate (MIT × TLQ + IT × TQ + MAT × THQ) / 3 to obtain the optimal torque current. If MIT ≥ IT and MAT > IT, calculate (IT × TQ + MAT × THQ) / 2 to obtain the optimal torque current. If MIT < IT and MAT ≤ IT, calculate (MIT × TLQ + IT × TQ) / 2 to obtain the optimal torque current. Adjust the direct-axis current to the optimal direct-axis current, and at the same time adjust the torque current to the optimal torque current.