Photovoltaic power generation energy storage type motor power supply driving intelligent control system

Abstract

The application discloses a photovoltaic power generation energy storage type motor power supply driving intelligent control system, mainly relates to the photovoltaic power generation energy storage motor driving control field, is through collecting multiple source original data to construct effective initial data pool, then relies on the remaining moving distance and real-time running linear velocity to divide three types of working conditions, so as to fit the real motor motion process segmented calculation power demand, avoid the power consumption estimation deviation caused by unified model, and establish motor power consumption mathematical model, rely on current and angle speed double deviation to complete fault research, once current and angle deviation are over limit at the same time, immediately cut into safety strategy, realize motor working condition abnormal rapid identification, improve equipment operation safety, wind speed and illumination are divided into 9 types of environmental working conditions, fine matching photovoltaic and energy storage power distribution coefficient, preferentially utilize photovoltaic to suppress power fluctuation, and add battery SOC and strong wind working condition linkage protection, prevent the battery overdischarge.

Description

Technical Field

[0001] This invention relates to the field of photovoltaic power generation and energy storage motor drive control, and in particular to an intelligent control system for photovoltaic power generation and energy storage motor drive. Background Technology

[0002] In the field of outdoor mobile load drive control, especially in motor drive systems for large inertia and long stroke loads such as outdoor canopies, awnings, and retractable canopies, power supply stability and dynamic response performance have always been the core technical challenges. These loads are often installed in open-air environments without direct grid supply and rely on off-grid hybrid power supply systems composed of photovoltaic power generation and energy storage batteries. Their operating conditions are strongly coupled by natural environmental factors (such as sudden changes in wind speed and fluctuations in sunlight) and the load's own motion state (such as starting acceleration, constant speed movement, and braking deceleration), resulting in the power demand of the motor drive end exhibiting strong time-varying and nonlinear characteristics.

[0003] In recent years, to improve photovoltaic utilization and power supply reliability, the industry has proposed several motor control schemes based on power prediction or energy management. For example, some schemes use historical meteorological data and photovoltaic output curves to construct short-term photovoltaic power generation prediction models and formulate charging and discharging plans for energy storage batteries accordingly. Other schemes monitor the real-time deviation of DC bus voltage or current and dynamically adjust the duty cycle of DC / DC converters on the photovoltaic or battery side to maintain stable bus voltage. However, the above-mentioned existing technologies still have the following significant shortcomings:

[0004] Firstly, many solutions adopt a unified power consumption model or constant power assumption, and fail to divide the operation process into typical stages such as start-up acceleration, constant speed operation, and braking deceleration based on the remaining moving distance of the load and the real-time operating linear velocity, resulting in a rough estimation of power demand.

[0005] Secondly, most current solutions simply allocate power based on the battery's state of charge (SOC) or the instantaneous output of photovoltaics, without combining wind speed and light intensity into fine-grained operating conditions (such as combinations of no wind-low light, medium wind-medium light, strong wind-strong light, etc.). This makes it impossible for the output weight of photovoltaics and energy storage to adapt to real-time environmental changes, making it difficult to maximize photovoltaic utilization and effectively protect energy storage batteries under severe weather conditions.

[0006] Therefore, there is an urgent need to develop a new type of intelligent control system that can integrate multi-source data acquisition, automatic division of operation phases, and dynamic energy allocation under operating conditions, so as to achieve precise coordination between the power supply side and the load side and improve the overall system efficiency and operational safety. Summary of the Invention

[0007] This invention aims to overcome the shortcomings of traditional photovoltaic energy storage power supply motor drive control systems in terms of operating condition identification, dynamic power prediction and correction, environmental adaptability matching, and multi-level fault protection. It provides a photovoltaic power generation and energy storage motor power supply drive intelligent control system to achieve precise coordination between the power supply side and the load side, thereby improving energy utilization efficiency, power response stability, and equipment operation safety.

[0008] The objective of this invention can be achieved through the following technical solution: a photovoltaic power generation and energy storage type motor power supply and drive intelligent control system, comprising:

[0009] The initial data acquisition module is used to collect multi-source raw data of the target motor when the target motor is started, including environmental parameters, load data of the target motor load end and energy storage and electrical energy parameters. Based on the collected multi-source raw data, an initial data sheet is constructed, and the initial data sheet is preprocessed to obtain an effective initial data pool.

[0010] The phase division module is used to extract parameters from the effective initial data pool, including the remaining moving distance and real-time running linear velocity. It performs discrimination processing on the extracted parameters and outputs the phase discrimination results for the start-up acceleration phase, constant speed running phase, and braking deceleration phase.

[0011] The power-state management module is used to identify the stage discrimination results and calculate the input power evaluation value of the current control cycle. Based on the input power evaluation value, it obtains the current deviation rate and speed deviation rate and performs correction judgment, and outputs a safe power distribution strategy or matching command.

[0012] When generating the matching command, the prediction-correction module is used to make a prediction by combining the Preq sequence of N historical cycles, and output the input power evaluation value Pr1 for the next control cycle. At the same time, it performs correction analysis by combining the calculated correction power amount, and outputs the final execution power command PF.

[0013] When a matching instruction is generated, the dynamic matching module is used to retrieve the final execution power instruction PF mentioned above, and at the same time collect the average wind speed and light intensity in the valid initial data pool to construct the working condition-power allocation strategy. Combining the matching analysis of the wind speed level and light intensity of the current target motor's environment, the module outputs the power allocation strategy under the current wind speed level and light intensity.

[0014] Preferably, the multi-source raw data includes environmental parameters, load data of the target motor load end, and energy storage and electrical energy parameters;

[0015] Environmental parameters include average wind speed and light intensity; load data includes real-time location and real-time operating linear velocity; and energy storage and electrical parameters include the real-time state of charge of the energy storage battery and the current maximum allowable instantaneous discharge power.

[0016] Preferably, the analysis process of the stage discrimination result is as follows:

[0017] Based on an effective initial data pool, the real-time position of the load, the real-time running linear velocity, and the target position of the load are extracted. The remaining travel distance is calculated by subtracting the real-time position of the load from the target position. At the same time, the pre-set starting distance threshold, deceleration distance threshold, initial speed threshold, and constant speed range are retrieved to distinguish the remaining travel distance and real-time running linear velocity, and the starting acceleration stage, constant speed running stage, and braking deceleration stage are output.

[0018] Preferably, the analysis process for the input power evaluation value is as follows:

[0019] Based on the judgment output results of the remaining moving distance and real-time running linear speed, the real-time stage of the current target motor is obtained. Based on the real-time stage of the current target motor, a mathematical model of motor power consumption is constructed. Based on the constructed mathematical model of motor power consumption, the corresponding valid initial data pool is substituted into the mathematical model of motor power consumption, and the input power evaluation value Preq of the target motor is output. The output input power evaluation value Preq is used as the input power evaluation value Pdt of the next control cycle.

[0020] Preferably, within this control cycle, the actual DC bus current and the actual mechanical angular velocity are simultaneously collected. Based on the input power evaluation value Pdt output by the above-mentioned motor power consumption mathematical model, and combined with the collected current bus voltage, the theoretical current is calculated as follows: Theoretical current = input power evaluation value Pdt / (current bus voltage × total operating efficiency coefficient).

[0021] Based on the current real-time stage of the target motor, the current theoretical angular velocity of the motor is obtained. The current deviation rate is calculated based on |actual DC bus current - theoretical current| / theoretical current. The speed deviation rate is calculated based on |actual mechanical angular velocity - theoretical angular velocity| / theoretical angular velocity. The current deviation rate and speed deviation rate are processed for discrimination, and a safe power distribution strategy or matching command is output.

[0022] Preferably, the analysis process for the final executed power command PF is as follows:

[0023] Based on the current control cycle and the Preq sequence of the most recent N historical cycles stored previously, the input is fed into the pre-built prediction model, and the output is the input power evaluation value Pr1 for the next control cycle.

[0024] Based on the input power evaluation value Pdt of the target motor, the actual DC bus current, theoretical current and predicted current are obtained. The comprehensive current deviation is calculated based on a1×(actual DC bus current-theoretical current)+a2×(actual DC bus current-predicted current), where a1 and a2 are preset weighting coefficients and are both natural numbers greater than zero.

[0025] The absolute value of the comprehensive current deviation is matched with the preset comprehensive current deviation range, and the preset correction gain coefficient set within the preset comprehensive current deviation range to which the absolute value of the comprehensive current deviation belongs is output.

[0026] The corrected power is calculated based on the preset correction gain coefficient × |comprehensive current deviation| × actual DC bus current. The corrected power is then limited. After the limiting process, the final execution power command PF is calculated based on the input power evaluation value Pdt + corrected power + γ × (Pr1 - input power evaluation value Pdt), where γ is the preset proportional coefficient.

[0027] Preferably, the construction and analysis process of the operating condition-power distribution strategy is as follows:

[0028] Retrieve the average wind speed from the valid initial data pool, set the preset wind speed range [FSmin, FSmax], and determine the wind speed level of the current target motor's environment. The wind speed level includes no wind, moderate wind, and strong wind.

[0029] Retrieve the light intensity from the valid initial data pool, set the preset light intensity range [Gmin, Gmax], determine the light level of the current target motor's environment, which includes low light level, medium light level and strong light level, and further subdivide it into 9 environmental conditions based on wind speed level and light level. Based on the 9 environmental conditions, pre-build the condition-power allocation strategy.

[0030] Preferably, the analysis process for the power distribution strategy under the current wind speed and light intensity levels is as follows:

[0031] Based on the judgment results of the average wind speed and light intensity of the current target motor, the wind speed level and light intensity level of the current target motor environment are output. Combined with the working condition-power distribution strategy, the power distribution strategy under the current wind speed level and light intensity level is matched and output.

[0032] During execution, the battery SOC value is monitored in real time. At the same time, when the real-time wind speed level is high wind level and the battery SOC value is lower than the preset battery SOC value threshold, the safe power allocation strategy is used instead of the power allocation strategy matched to the high wind level.

[0033] The safe power distribution strategy includes: triggering audible and visual alarms and pausing the current operating command.

[0034] The beneficial effects of this invention are as follows:

[0035] (1) This invention constructs an effective initial data pool by collecting raw data from multiple sources, cleaning the data and aligning the time sequence, and then divides the working conditions into three categories: acceleration, constant speed and deceleration based on the remaining moving distance and real-time running linear speed. This can fit the actual motor motion process and calculate the power demand in segments, avoiding the power consumption estimation deviation caused by the unified model.

[0036] (2) The present invention also establishes a mathematical model of motor power consumption, and completes fault judgment by relying on the dual deviation of current and angular velocity. Once the current and angular deviation exceed the limit at the same time, the safety strategy is immediately switched to realize the rapid identification of abnormal motor operating conditions and improve the safety of equipment operation. At the same time, multi-cycle historical input power evaluation values ​​are introduced, and the base power is proportionally corrected by combining the predicted values, which greatly reduces the power prediction error caused by gust disturbances and improves the dynamic accuracy of power command. Furthermore, wind speed and light are divided into 9 types of environmental conditions, and the photovoltaic and energy storage power allocation coefficients are finely matched. Photovoltaics are given priority to smooth out power fluctuations. In addition, battery SOC and strong wind conditions are linked for protection, which effectively prevents battery over-discharge. Attached Figure Description

[0037] The invention will now be further described with reference to the accompanying drawings;

[0038] Figure 1 This is a flowchart of the system of the present invention;

[0039] Figure 2 This is a schematic diagram of the final power command (PF) analysis of the present invention;

[0040] Figure 3 This is a schematic diagram for the power distribution decision analysis of the present invention. Detailed Implementation

[0041] 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.

[0042] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments;

[0043] Example 1: Please refer to Figures 1 to 3As shown, the present invention is an intelligent control system for photovoltaic power generation and energy storage motor power supply drive, including an initial data acquisition module, a stage division module, a power-state management module, a prediction-correction module, and a dynamic matching module;

[0044] The initial data acquisition module is used to collect multi-source raw data of the target motor when it is started, including environmental parameters, load data at the target motor's load end, and energy storage and electrical energy parameters. Based on the collected multi-source raw data, an initial data sheet is constructed. The initial data sheet is preprocessed to obtain an effective initial data pool, specifically including:

[0045] S1: When starting the target motor, collect multi-source raw data in real time, including environmental parameters of the target motor's environment, load data of the target motor's load end, energy storage and electrical energy parameters;

[0046] Among them, environmental parameters include average wind speed and light intensity, load data (such as outdoor canopy) includes real-time location and real-time operating linear velocity, and energy storage and electrical energy parameters include real-time state of charge of energy storage batteries and current maximum allowable instantaneous discharge power.

[0047] S2: Construct an initial data sheet based on the collected environmental parameters, load data, and energy storage and power parameters;

[0048] The collected initial data sheets are preprocessed, including cleaning and time alignment, to obtain an effective initial data pool;

[0049] The phase division module is used to extract parameters from the valid initial data pool, including the remaining travel distance and real-time running linear velocity. It then performs discrimination processing on the extracted parameters and outputs the phase discrimination results for the start-up acceleration phase, constant speed running phase, and braking deceleration phase. Specifically, this includes:

[0050] Based on the effective initial data pool, the real-time position of the load, the real-time running linear velocity, and the load's set target position are extracted. The remaining travel distance is calculated by subtracting the real-time position of the load from the set target position. Simultaneously, pre-set thresholds for starting distance, deceleration distance, initial speed, and constant speed range are retrieved to process the remaining travel distance and real-time running linear velocity.

[0051] If the remaining moving distance is greater than the starting distance threshold and the real-time running linear speed is less than the initial speed threshold, it is determined to be the starting acceleration phase. If the remaining moving distance is greater than the starting distance threshold, but the remaining moving distance has not reached the deceleration distance threshold and the real-time running linear speed is within the constant speed range, it is determined to be the constant speed running phase. If the remaining moving distance reaches the deceleration distance threshold, it is determined to be the braking deceleration phase.

[0052] The power-state management module is used to identify the stage discrimination results and calculate the input power evaluation value for the current control cycle. Based on the input power evaluation value, it obtains the current deviation rate and speed deviation rate, performs correction judgments, and outputs a safe power allocation strategy or matching command, specifically including:

[0053] Based on the judgment output results of the remaining travel distance and real-time running linear speed, the real-time stage of the current target motor is obtained, and based on the real-time stage of the current target motor, the input power evaluation value Preq of the target motor in the current control cycle is calculated.

[0054] That is, to construct a mathematical model of motor power consumption:

[0055] Where η represents the overall operating efficiency coefficient of the motor and mechanical transmission mechanism, which is a dimensionless constant, and F f F represents the frictional force of load movement at the load end. w (V w ) represents the average moving wind resistance under the current average wind speed, V(t) represents the current load operating linear velocity, J represents the total rotational inertia of all moving parts converted to the motor shaft, ω represents the current mechanical angular velocity of the motor, and Δω / Δt represents the motor angular acceleration calculated by dividing the difference between the angular velocities of the current cycle and the previous cycle by the control cycle Δt.

[0056] Based on the constructed mathematical model of motor power consumption, the effective initial data pool is substituted into the mathematical model of motor power consumption, and the input power evaluation value Preq of the target motor is output. The output input power evaluation value Preq is used as the input power evaluation value Pdt of the next control cycle.

[0057] During this control cycle, the actual DC bus current and actual mechanical angular velocity of the motor driver are read simultaneously.

[0058] Based on the input power evaluation value Pdt output by the above motor power consumption mathematical model, and combined with the current bus voltage collected, the theoretical current is calculated as follows: Theoretical current = input power evaluation value Pdt / (current bus voltage × η).

[0059] Meanwhile, based on the current real-time stage of the target motor, the current theoretical angular velocity of the motor is obtained, the current deviation rate is calculated based on |actual DC bus current - theoretical current| / theoretical current, and the speed deviation rate is calculated based on |actual mechanical angular velocity - theoretical angular velocity| / theoretical angular velocity.

[0060] The current deviation rate and speed deviation rate are judged and processed. If the current deviation rate is greater than the preset current deviation rate threshold and the speed deviation rate is greater than the preset speed deviation rate threshold, the target motor is determined to be abnormal. When the target motor is determined to be abnormal, the safe power distribution strategy is directly switched.

[0061] If the current deviation rate is less than or equal to the preset current deviation rate threshold, or the speed deviation rate is less than or equal to the preset speed deviation rate threshold, a matching command is triggered.

[0062] Example 2: When generating the matching command, the prediction-correction module is used to predict by combining the Preq sequence of N historical cycles, outputting the input power evaluation value Pr1 for the next control cycle, and simultaneously performing correction analysis by combining the calculated correction power amount, outputting the final execution power command PF, specifically including:

[0063] Based on the current control cycle and the Preq sequence of the most recent N (N>0) historical cycles stored previously, the input is entered into the pre-built prediction model, and the output is the input power evaluation value Pr1 for the next control cycle.

[0064] Based on the input power evaluation value Pdt of the target motor, the actual DC bus current, theoretical current and predicted current are obtained. The comprehensive current deviation is calculated based on a1×(actual DC bus current-theoretical current)+a2×(actual DC bus current-predicted current), where a1 and a2 are preset weighting coefficients and are both natural numbers greater than zero.

[0065] The absolute value of the comprehensive current deviation is matched with the preset comprehensive current deviation range, and the preset correction gain coefficient set within the preset comprehensive current deviation range to which the absolute value of the comprehensive current deviation belongs is output.

[0066] The corrected power is calculated based on the preset correction gain coefficient × |comprehensive current deviation| × actual DC bus current;

[0067] The correction power is limited within the range of [-XZmax, +XZmax]. If the positive power is greater than +XZmax, then XZmax is used; if the positive power is less than -XZmax, then -XZmax is used.

[0068] The final execution power command PF is calculated based on the input power evaluation value Pdt + correction power + γ × (Pr1 - input power evaluation value Pdt), where γ is a preset proportional coefficient and the value of γ ranges from 0.2 to 0.4.

[0069] When a matching command is generated, the dynamic matching module retrieves the final execution power command PF and simultaneously collects the average wind speed and light intensity from the valid initial data pool. It then constructs a working condition-power allocation strategy, combining the matching analysis of the wind speed and light intensity levels of the current target motor's environment, and outputs the power allocation strategy under the current wind speed and light intensity levels. Specifically, this includes:

[0070] Retrieve the average wind speed from the valid initial data pool, set a preset wind speed range [FSmin, FSmax], and determine the wind speed level of the environment where the target motor is located. If the average wind speed is <FSmin, it is determined to be a calm level; if the average wind speed is ∈ [FSmin, FSmax], it is determined to be a moderate wind level; if the average wind speed is >FSmax, it is determined to be a strong wind level.

[0071] Retrieve the light intensity from the valid initial data pool, set the preset light intensity range [Gmin, Gmax], and determine the light level of the current target motor's environment. If the light intensity < Gmin, it is determined to be a low light level; if the light intensity ∈ [Gmin, Gmax], it is determined to be a medium light level; if the light intensity > Gmax, it is determined to be a strong light level.

[0072] Based on wind speed and light intensity levels, the environmental conditions are further divided into 9 categories, and a condition-power allocation strategy is pre-built based on these 9 environmental conditions.

[0073] Nine types of environmental conditions, such as: no wind - low light condition, no wind - medium light condition, no wind - high light condition, etc.

[0074] Based on the judgment results of the average wind speed and light intensity of the current target motor, the wind speed level and light intensity level of the current target motor environment are output. Combined with the working condition-power distribution strategy, the power distribution strategy under the current wind speed level and light intensity level is matched and output.

[0075] For example, if the real-time wind speed level is medium wind level and medium solar level, the power distribution strategy includes: setting the energy storage output power coefficient α (e.g., α = 0.4), that is, allocating 40% of the predicted power to the energy storage battery and 60% to the photovoltaic. Specifically: energy storage battery α × final executed power command PF, photovoltaic: (1-α) × final executed power command PF. The purpose of this mode is to reserve sufficient photovoltaic power margin to smooth out power fluctuations caused by gusts.

[0076] Based on the matched power distribution strategy and the final execution power command PF of the target motor, the output power command value of the energy storage battery and the output power command value of the photovoltaic are obtained and sent to the photovoltaic-side DC / DC converter and the battery-side bidirectional DC / DC converter, respectively.

[0077] During execution, the battery SOC value is monitored in real time. At the same time, when the real-time wind speed level is high and the battery SOC value is lower than the preset battery SOC value threshold, the safe power distribution strategy is used instead of the power distribution strategy matched for the high wind level to prevent the battery from being over-discharged and damaged.

[0078] The safe power distribution strategy includes: triggering audible and visual alarms, pausing the current operation command, keeping the position unchanged, replacing the energy storage output power coefficient α with the preset safe energy storage output power coefficient (such as 0.2) when the real-time wind speed level is medium wind level, and running the load to the fully closed (collapsed) position at the preset minimum safe speed (such as 0.05m / s).

[0079] In summary, this invention integrates multi-source data acquisition, operational phase identification, and power closed-loop correction to achieve segmented modeling of operating conditions, power prediction and correction, dynamic energy allocation under environmental conditions, and multi-level fault protection. It takes into account energy utilization efficiency, power stability, and equipment operation safety, and is suitable for stable drive control of canopy motors in outdoor wind and light fluctuation scenarios. Specifically, it constructs an effective initial data pool by collecting multi-source raw data, cleaning and aligning the data with the time sequence, and then divides the operating conditions into three categories: acceleration, constant speed, and deceleration based on the remaining travel distance and real-time operating linear velocity. This allows for segmented calculation of power requirements to match the actual movement process of the motor, avoiding power consumption estimation deviations caused by a uniform model.

[0080] Furthermore, a mathematical model for motor power consumption is established, and fault judgment is completed by relying on the dual deviations of current and angular velocity. Once the current and angular deviations exceed the limits simultaneously, a safety strategy is immediately activated to achieve rapid identification of abnormal motor operating conditions and improve the safety of equipment operation. At the same time, multi-cycle historical input power evaluation values ​​are introduced and combined with predicted values ​​to make proportional corrections to the base power, which significantly reduces the power prediction error caused by gust disturbances and improves the dynamic accuracy of power commands. In addition, wind speed and sunlight are divided into 9 types of environmental conditions, and the power allocation coefficients of photovoltaic and energy storage are finely matched. Photovoltaic is given priority to smooth out power fluctuations, and battery SOC and high wind conditions are linked for protection to effectively prevent battery over-discharge.

[0081] The threshold is set for result comparison and analysis to determine whether it is good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influences. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A photovoltaic power generation and energy storage type motor power supply and drive intelligent control system, characterized in that, include: The initial data acquisition module is used to collect multi-source raw data of the target motor when the target motor is started, including environmental parameters, load data of the target motor load end and energy storage and electrical energy parameters. Based on the collected multi-source raw data, an initial data sheet is constructed, and the initial data sheet is preprocessed to obtain an effective initial data pool. The phase division module is used to extract parameters from the effective initial data pool, including the remaining moving distance and real-time running linear velocity. It performs discrimination processing on the extracted parameters and outputs the phase discrimination results for the start-up acceleration phase, constant speed running phase, and braking deceleration phase. The power-state management module is used to identify the stage discrimination results and calculate the input power evaluation value of the current control cycle. Based on the input power evaluation value, it obtains the current deviation rate and speed deviation rate and performs correction judgment, and outputs a safe power distribution strategy or matching command. When generating the matching command, the prediction-correction module is used to make a prediction by combining the Preq sequence of N historical cycles, and output the input power evaluation value Pr1 for the next control cycle. At the same time, it performs correction analysis by combining the calculated correction power amount, and outputs the final execution power command PF. When a matching instruction is generated, the dynamic matching module is used to retrieve the final execution power instruction PF mentioned above, and at the same time collect the average wind speed and light intensity in the valid initial data pool to construct the working condition-power allocation strategy. Combining the matching analysis of the wind speed level and light intensity of the current target motor's environment, the module outputs the power allocation strategy under the current wind speed level and light intensity.

2. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 1, characterized in that, The environmental parameters include average wind speed and light intensity; the load data includes real-time location and real-time operating linear velocity; and the energy storage and electrical parameters include the real-time state of charge of the energy storage battery and the current maximum allowable instantaneous discharge power.

3. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 2, characterized in that, The analysis process of the stage discrimination results is as follows: Based on an effective initial data pool, the real-time position of the load, the real-time running linear velocity, and the target position of the load are extracted. The remaining travel distance is calculated by subtracting the real-time position of the load from the target position. At the same time, the pre-set starting distance threshold, deceleration distance threshold, initial speed threshold, and constant speed range are retrieved to distinguish the remaining travel distance and real-time running linear velocity, and the starting acceleration stage, constant speed running stage, and braking deceleration stage are output.

4. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 3, characterized in that, The analysis process for the input power evaluation value is as follows: Based on the judgment output results of the remaining moving distance and real-time running linear speed, the real-time stage of the current target motor is obtained. Based on the real-time stage of the current target motor, a mathematical model of motor power consumption is constructed. Based on the constructed mathematical model of motor power consumption, the corresponding valid initial data pool is substituted into the mathematical model of motor power consumption, and the input power evaluation value Preq of the target motor is output. The output input power evaluation value Preq is used as the input power evaluation value Pdt of the next control cycle.

5. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 4, characterized in that, During this control cycle, the actual DC bus current and the actual mechanical angular velocity are collected simultaneously. Based on the input power evaluation value Pdt output by the above motor power consumption mathematical model, and combined with the collected current bus voltage, the theoretical current is calculated. Theoretical current = input power evaluation value Pdt / (current bus voltage × total operating efficiency coefficient). Based on the current real-time stage of the target motor, the current theoretical angular velocity of the motor is obtained. The current deviation rate is calculated based on |actual DC bus current - theoretical current| / theoretical current. The speed deviation rate is calculated based on |actual mechanical angular velocity - theoretical angular velocity| / theoretical angular velocity. The current deviation rate and speed deviation rate are processed for discrimination, and a safe power distribution strategy or matching command is output.

6. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 5, characterized in that, The analysis process for the final executed power command PF is as follows: Based on the current control cycle and the Preq sequence of the most recent N historical cycles stored previously, the input is fed into the pre-built prediction model, and the output is the input power evaluation value Pr1 for the next control cycle. Based on the input power evaluation value Pdt of the target motor, the actual DC bus current, theoretical current and predicted current are obtained. The comprehensive current deviation is calculated based on a1×(actual DC bus current-theoretical current)+a2×(actual DC bus current-predicted current), where a1 and a2 are preset weighting coefficients and are both natural numbers greater than zero. The absolute value of the comprehensive current deviation is matched with the preset comprehensive current deviation range, and the preset correction gain coefficient set within the preset comprehensive current deviation range to which the absolute value of the comprehensive current deviation belongs is output. The corrected power is calculated based on the preset correction gain coefficient × |comprehensive current deviation| × actual DC bus current. The corrected power is then limited. After the limiting process, the final execution power command PF is calculated based on the input power evaluation value Pdt + corrected power + γ × (Pr1 - input power evaluation value Pdt), where γ is the preset proportional coefficient.

7. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 6, characterized in that, The construction and analysis process of the operating condition-power distribution strategy is as follows: Retrieve the average wind speed from the valid initial data pool, set the preset wind speed range [FSmin, FSmax], and determine the wind speed level of the current target motor's environment. The wind speed level includes no wind, moderate wind, and strong wind. Retrieve the light intensity from the valid initial data pool, set the preset light intensity range [Gmin, Gmax], determine the light level of the current target motor's environment, which includes low light level, medium light level and strong light level, and further subdivide it into 9 environmental conditions based on wind speed level and light level. Based on the 9 environmental conditions, pre-build the condition-power allocation strategy.

8. The intelligent control system for photovoltaic power generation and energy storage motor power supply drive according to claim 7, characterized in that, The analysis process for the power distribution strategy under the current wind speed and solar irradiance levels is as follows: Based on the judgment results of the average wind speed and light intensity of the current target motor, the wind speed level and light intensity level of the current target motor environment are output. Combined with the working condition-power distribution strategy, the power distribution strategy under the current wind speed level and light intensity level is matched and output. During execution, the battery SOC value is monitored in real time. At the same time, when the real-time wind speed level is high wind level and the battery SOC value is lower than the preset battery SOC value threshold, the safe power allocation strategy is used instead of the power allocation strategy matched to the high wind level. The safe power distribution strategy includes: triggering audible and visual alarms and pausing the current operating command.

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