A photovoltaic thermal heating control method and system based on wind power renewable energy stations

Abstract

This invention discloses a photovoltaic thermal heating control method and system based on wind power renewable energy stations, belonging to the field of heating control technology. The method includes deploying power supply equipment, distributed heating terminals, and electric heating load modules within the wind power renewable energy station; collecting equipment operation data and temperature data for preprocessing to generate a standardized dataset; based on the standardized dataset, dynamically adjusting the output power of the power supply equipment through sliding mode control and fuzzy control respectively; calculating the scheduling weight of the power supply equipment using a sliding window weighting method; setting temperature control targets and combining temperature data to calculate the total heating power; and allocating the total heating power to the power supply equipment according to the scheduling weights to obtain preliminary allocation results and grid power. This invention can improve the stability and efficiency of heating operation and is widely applicable to the clean heating needs of wind power stations in high-altitude, remote, and complex climatic environments.

Description

Technical Field

[0001] This invention relates to the field of heating control technology, and in particular to a photovoltaic thermal heating control method and system based on wind power new energy stations. Background Technology

[0002] With the rapid development of new energy technologies, wind power and photovoltaic power generation, as one of the most efficient forms of renewable energy, have gradually become the main energy supply methods for remote areas, areas with weak power grids, and even large-scale wind power bases. In new energy power plants, providing stable heating for maintenance living areas, control rooms, and equipment rooms has become a crucial foundation for ensuring the reliability of wind power equipment. Traditional wind farm heating methods mainly rely on independent electric heating equipment or external power grid connection, but these heating modes often lack adaptive adjustment capabilities and cannot match the output characteristics of wind power and photovoltaic equipment in real time. Meanwhile, with the widespread application of doubly-fed induction generators in wind turbine units, their advantages such as strong controllability and wide power adjustment range have provided new possibilities for distributed load energy supply.

[0003] Existing technologies are prone to problems such as insufficient heating output, energy waste, and frequent inverter switching when there are large temperature disturbances, large light fluctuations, and drastic changes in wind speed. Furthermore, they have poor adaptability to new energy output for heating loads and cannot effectively reduce grid-side load fluctuations. In addition, there is a lack of adaptation strategies for direct-supply loads and AC loads, which respectively adopt inverter power supply and rectification compensation, thus limiting the overall energy efficiency and heating quality. Summary of the Invention

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

[0005] Therefore, this invention provides a photovoltaic thermal heating control method and system based on wind power new energy stations, which solves the problems of insufficient heating output, energy waste, and frequent inverter switching in the prior art. Furthermore, it addresses the poor adaptability of heating loads to new energy output and the inability to effectively reduce grid-side load fluctuations. Additionally, it lacks an adaptation strategy for direct-supply loads and AC loads, respectively, which limits the overall energy efficiency and heating quality.

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

[0007] In a first aspect, the present invention provides a photovoltaic thermal heating control method based on a wind power new energy station, comprising,

[0008] Power supply equipment, distributed heating terminals and electric heating load modules are deployed in wind power new energy stations. Equipment operation data and temperature data are collected, preprocessed, and standardized datasets are generated.

[0009] Based on a standardized dataset, the output power of the power supply equipment is dynamically adjusted by sliding mode control and fuzzy control respectively. The scheduling weight of the power supply equipment is calculated by using the sliding window weighting method. The temperature control target is set and combined with temperature data to calculate the total heating power. The total heating power is then allocated to the power supply equipment according to the scheduling weight to obtain the preliminary allocation results and the power grid power.

[0010] Based on the preliminary allocation results and grid power, after parameter updates and feedback, the power supply-demand difference and voltage error of the total heating power are calculated, the required working mode is determined, and coordinated control and heating are executed.

[0011] As a preferred embodiment of the photovoltaic and solar thermal heating control method based on wind power new energy stations described in this invention, the method involves dynamically adjusting the output power of the power supply equipment based on a standardized dataset using sliding mode control and fuzzy control, respectively. This adjustment is based on the three-phase rotor current within the standardized dataset, and the standardized three-phase rotor current is decoupled to [the specified value] using the Clark-Parker transform. coordinate system, to obtain shaft current, including shaft current and shaft current Use rules of thumb to set the corresponding reference current, combined with The shaft current is calculated by subtraction to determine the current error between two currents along the same axis. Shaft current error and Shaft current error ,for Shaft current error and Shaft current error Sliding surfaces are constructed separately to obtain the sliding function value for each axis. The time derivative of the current error for each axis is obtained by using the chain rule. The first derivative of the current error is obtained, and the sliding surface derivative expression is constructed. The standard current dynamic equation of the doubly fed induction generator is substituted into the sliding surface derivative expression to obtain the complete expression of the sliding constraint. After applying the constraint that the sliding surface approaches zero according to the sliding condition, the terms are rearranged to form the equivalent control quantity expression, and the output equivalent control quantity is calculated.

[0012] Set critical threshold For each axis, a smooth boundary transformation is performed on the sliding mode function value to obtain the saturation function. Combined with the equivalent control quantity, a control law expression is constructed, and the output for each axis at time point is calculated. The control law is used as the input to the back-to-back pulse width modulation converter, and the output is the actual voltage value applied to the rotor winding. The Euler difference method is applied for discretization to generate the rotor current for each axis at the next time point, including the rotor... Shaft current, rotor The shaft current, based on the rotor current, is approximated using the quasi-steady-state approximation in the rotor-side induced voltage coupling approximation method to generate the stator voltage, including the stator voltage. Shaft voltage and stator Calculate the three-phase output active power of the doubly-fed induction generator by combining the shaft voltage and rotor current.

[0013] The system uses the real-time voltage of the photovoltaic array output terminal within a standardized dataset in parallel. The incremental conductance method is used to obtain the optimal operating point voltage of the photovoltaic array at the current time point. Combined with the real-time voltage of the photovoltaic array output terminal, the initial Boost duty cycle required by the boost converter is calculated. The target Boost control voltage of the boost converter is set according to the rated voltage of the heating equipment in the wind power renewable energy station. The voltage error is calculated using the DC voltage stability value within the standardized dataset. The rate of error change is obtained by differentiating the voltage error and using this rate of change as the input to a fuzzy logic controller. The output Boost duty cycle adjustment is then executed to update the Boost duty cycle. After obtaining the updated Boost duty cycle, the output voltage of the boost converter and the output power of the photovoltaic array output terminal at the next time point are calculated by combining the standardized real-time voltage and current of the photovoltaic array output terminal.

[0014] As a preferred embodiment of the photovoltaic and solar thermal heating control method based on wind power new energy stations described in this invention, the method of calculating the scheduling weight of power supply equipment using the sliding window weighting method refers to defining the doubly fed induction generator and photovoltaic array as energy paths, integrating the three-phase output active power and output power as input items, backtracking from the current time point to form a sliding window, extracting the input items to calculate the time decay weighted sum, obtaining the exponential weight, normalizing it, and generating the scheduling weight.

[0015] As a preferred embodiment of the photovoltaic and solar thermal heating control method based on wind power new energy stations described in this invention, the following steps are taken: Setting temperature control targets and combining them with temperature data to calculate the total heating power, and allocating the total heating power to power supply equipment according to scheduling weights to obtain preliminary allocation results and grid power indicators. Temperature control targets are set for each region using a temperature setter, and the real-time temperature of each region is obtained through temperature sensors. Based on the corresponding temperature control targets, the temperature difference of each region at a given time point is calculated. A thermal conductivity coefficient is set for each region based on its insulation performance. After calculating the required heating power for each region based on the temperature difference, the total heating power is obtained using a summation formula. Power allocation for each energy path is then performed based on scheduling weights to obtain preliminary allocation results.

[0016] The grid interface is defined as a compensation path, and based on the preliminary allocation results, the grid compensation power of the grid interface at the current time point is calculated and limited to obtain the grid power.

[0017] As a preferred embodiment of the photovoltaic thermal heating control method based on wind power new energy stations described in this invention, the step of updating and feeding back parameters based on preliminary allocation results and grid power refers to calculating the energy efficiency ratio at the current time point based on grid power and preliminary allocation results, then calculating the global error based on the temperature difference, and combining the energy efficiency ratio with the time decay coefficient. After the update, it is fed back into the formula of time decay weighted sum, and the exponential weight is recalculated to update the scheduling weight of each energy path. The new scheduling weight is used to execute the preliminary allocation result and obtain the grid power, and the new power allocation result and grid compensation power are obtained.

[0018] Based on the new power allocation results, including time points The power allocation of the doubly-fed induction generator energy path and the power allocation of the photovoltaic array energy path are calculated. According to the power allocation in the new power allocation result, the power allocation of each energy path is taken as the set value and sent to the sliding mode controller and the fuzzy controller to generate new three-phase output active power and photovoltaic array output power. Combined with the new grid compensation power, the power is summed to generate the total available power.

[0019] As a preferred embodiment of the photovoltaic thermal heating control method based on wind power new energy stations described in this invention, the step of determining the current required working mode and executing coordinated control and heating instructions involves configuring a power controller for the electric heating load module, using the total available power as the input of the power controller, executing the drive of the electric heating load module, and performing heating operation on the area. During the heating process, the supply-demand difference and voltage error between the total available power and the total heating power at the next time point are calculated, the required working mode of the bidirectional inverter is determined, and the corresponding operation is executed to obtain the final control basis.

[0020] When supply and demand are different Greater than or equal to the power balance threshold And voltage error Less than or equal to the tolerance threshold If the current total available power is sufficient, the bidirectional inverter is set to DC→AC inverter mode and corresponding operations are performed, including starting the bidirectional inverter, inverting the excess DC active power of the DC bus to the AC bus, prioritizing the allocation of AC power to AC load equipment, and maintaining the normal power supply of direct-supply electric heating equipment.

[0021] When supply and demand are different Less than the power balance threshold And voltage error Greater than the tolerance threshold If the current total available power or DC voltage is insufficient, the bidirectional inverter is set to the rectification compensation mode AC→DC, and corresponding operations are performed, including starting the bidirectional inverter to perform the rectification function, absorbing AC active power from the AC bus, injecting the rectified power into the DC bus, and supplying it to the direct-supply electric heating equipment, and then delaying or limiting the power supply to the AC load equipment.

[0022] When supply and demand are different Less than the power balance threshold And voltage error Less than or equal to the tolerance threshold If the current total available power is short but in a stable state, the bidirectional inverter is set to the balanced mode to perform corresponding operations, including monitoring the operating mode at subsequent time points. If there is a continuous balanced mode, the scheduling weight is updated again.

[0023] If any condition is not met, the current power supply will be interrupted and an alarm message will be sent to the control personnel.

[0024] As a preferred embodiment of the photovoltaic and thermal heating control method based on wind power new energy stations described in this invention, the following steps are taken: Deploying power supply equipment, distributed heating terminals, and electric heating load modules within the wind power new energy station; collecting equipment operation data and temperature data for preprocessing; and generating a standardized dataset. This includes deploying power supply equipment within the wind power new energy station, including doubly-fed induction generators, photovoltaic arrays, and grid interfaces; deploying distributed heating terminals in the operation and maintenance living area and wind power main control room of the wind power new energy station; setting electric heating load modules for each area, including direct-supply electric heating equipment and AC load equipment; and equipping each area with a temperature sensor and a temperature setting device.

[0025] Voltage and current data are collected by multiple sensors. All data are denoised, outlier removed, and standardized to obtain a standardized dataset. This dataset includes the standardized real-time voltage and current at the output of the photovoltaic array, the real-time output voltage of the boost converter, and the RMS values ​​of the AC voltage at the AC and DC buses. and DC voltage stability value The stator three-phase voltage and three-phase rotor current on the stator side of the doubly fed induction generator.

[0026] Secondly, the present invention provides a photovoltaic thermal heating control system based on a wind power new energy station, comprising,

[0027] The data acquisition module is deployed to collect equipment operation data and temperature data from power supply equipment, distributed heating terminals and electric heating load modules, and preprocesses them to generate a standardized dataset.

[0028] The adjustment generation module is used to dynamically adjust the output power of the power supply equipment through sliding mode control and fuzzy control respectively, and to calculate the scheduling weight of the power supply equipment using the sliding window weighting method.

[0029] The allocation module is used to set temperature control targets, combine temperature data, calculate the total heating power, and allocate the total heating power to the power supply equipment according to the scheduling weight, so as to obtain the preliminary allocation results and grid power.

[0030] The update feedback module is used to perform parameter updates and feedback based on the initial allocation results and grid power.

[0031] The heating control module is used to calculate the power supply and demand difference and voltage error of the total heating power, and to determine the current required working mode and perform coordinated control and heating.

[0032] The beneficial effects of this invention are as follows: By constructing a sliding mode control law to regulate the output of wind power generation, and combining it with fuzzy logic control to achieve precise control of photovoltaic output, it ensures high adaptability and high responsiveness under different meteorological conditions. Furthermore, by using sliding window weighting and temperature difference feedback to update the time decay factor, the scheduling weight of wind and solar energy paths is dynamically adjusted to achieve on-demand allocation of heating power. By combining the grid interface as a compensation path and adjusting the power ratio of each path based on the energy efficiency ratio and global temperature difference closed loop, this invention has self-correction capabilities. Secondly, in the terminal execution, three power supply modes (inverter power supply, rectifier compensation, and balancing mode) are set for direct-supply and AC load equipment to improve the stability and efficiency of heating operation. Therefore, this invention can be widely applied to the clean heating needs of wind farms in high-altitude, remote, and complex climatic environments. Attached Figure Description

[0033] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a flowchart of the photovoltaic thermal heating control method based on wind power new energy stations in Example 1.

[0035] Figure 2 This is a structural diagram of the photovoltaic thermal heating control system based on a wind power new energy station in Example 1.

[0036] Figure 3 This is a flowchart of the pattern determination and coordination control in Example 1. Detailed Implementation

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0038] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0039] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention.

[0040] Example 1, referring to Figures 1-3 This is the first embodiment of the present invention, which provides a photovoltaic thermal heating control method based on a wind power new energy station, including the following steps:

[0041] S1. Deploy power supply equipment, distributed heating terminals and electric heating load modules in wind power new energy stations, collect equipment operation data and temperature data for preprocessing, and generate standardized datasets.

[0042] Specifically, power supply equipment, distributed heating terminals, and electric heating load modules are deployed within wind power renewable energy stations. Equipment operation data and temperature data are collected, preprocessed, and standardized datasets are generated. Power supply equipment, including doubly-fed induction generators, photovoltaic arrays, and grid interfaces, is deployed within wind power renewable energy stations. Distributed heating terminals are deployed in the operation and maintenance living areas and wind power main control rooms of wind power renewable energy stations. Electric heating load modules, including direct-supply electric heating equipment and AC load equipment, are set up in each area. Temperature sensors and temperature setting devices are also provided in each area.

[0043] The stator winding of the doubly fed induction generator is connected to the AC bus via the main AC contactor, and the rotor winding is connected to the input terminal of the back-to-back pulse width modulation converter. After the photovoltaic array is connected to the boost converter, the output terminal of the boost converter is connected to the DC bus. A bidirectional inverter is used to perform bidirectional flow between the AC bus and the DC bus.

[0044] The multi-sensor data acquisition refers to acquiring real-time voltage and current at the output of the photovoltaic array using voltage and current sensors, acquiring the real-time output voltage of the boost converter using a DC bus voltage sensor, and acquiring the effective values ​​of AC and DC voltages using voltage transformers. and DC voltage stability value The stator three-phase voltage on the stator side of the doubly-fed induction generator is collected using a three-phase voltage transformer, and the three-phase rotor current of the doubly-fed induction generator is collected using a Hall current sensor.

[0045] Voltage and current data are collected by multiple sensors. All data is then denoised, outlier removed, and standardized to obtain a standardized dataset. This dataset includes the standardized real-time voltage and current at the photovoltaic array output, the real-time output voltage of the boost converter, and the RMS values ​​of the AC and DC voltages at the AC and DC bus. and DC voltage stability value The stator three-phase voltage and three-phase rotor current on the stator side of the doubly fed induction generator.

[0046] By deploying doubly-fed induction generators, photovoltaic arrays, and grid interfaces in wind power renewable energy stations to form a "three-source synergy" architecture, the heating system possesses strong energy redundancy and regulation capabilities. In climatic conditions with drastic wind speed changes or insufficient sunlight, dynamic grid compensation paths can be used to prevent heating failures due to localized energy shortages, improving system reliability. Back-to-back pulse-width modulation converters inject current into the rotor according to the control law output by the sliding mode controller, enabling dynamic adjustment of the active power output of the wind turbine. The bidirectional inverter acts as a system "regulating valve," adjusting the power flow direction based on bus status and load conditions, achieving adaptive switching between DC→AC or AC→DC. This structure enables real-time coupling of multiple power sources and multi-zone loads. The use of multiple sensors allows for high-precision acquisition of the operating status of photovoltaic, grid, and wind power subsystems. Combined with subsequent sliding mode control and fuzzy controllers, a global feedback loop can be constructed, providing highly reliable data support for refined control. The "direct supply + AC" load architecture facilitates precise deployment by region. For example, living areas can prioritize the use of temperature-controlled equipment such as electric underfloor heating to ensure basic temperature control; while the main control room can deploy fast-response air curtains and electric heaters to quickly raise the temperature and improve the comfort of maintenance personnel. Combined with temperature setpoints and sensors, power allocation based on temperature difference feedback can be implemented on demand, supporting more intelligent regional scheduling strategies. Secondly, by performing noise reduction, outlier removal, and standardization preprocessing on the collected data, errors caused by signal distortion, sensor drift, or environmental interference are effectively eliminated, enabling subsequent control algorithms to operate in a stable and predictable input environment.

[0047] S2. Based on a standardized dataset, the output power of the power supply equipment is dynamically adjusted by sliding mode control and fuzzy control respectively. The scheduling weight of the power supply equipment is calculated by using a sliding window weighting method. The temperature control target is set and combined with temperature data to calculate the total heating power. The total heating power is then allocated to the power supply equipment according to the scheduling weight to obtain the preliminary allocation results and the power grid power.

[0048] Specifically, based on a standardized dataset, the output power of the power supply equipment is dynamically adjusted using sliding mode control and fuzzy control, respectively. This is based on the three-phase rotor current within the standardized dataset, and the standardized three-phase rotor current is decoupled to [the specified value] using the Clark-Parker transform. coordinate system, to obtain shaft current, including shaft current and shaft current ;

[0049] During the Clark-Parker transformation, the three-phase rotor currents are first subjected to the Clark transformation to obtain their projection values ​​in a two-phase stationary coordinate system, including... and , Indicates a point in time At that time Projected values ​​in the stationary coordinate system. Indicates a point in time At that time The projected values ​​in the static coordinate system are used to perform a Park transformation on the projected values ​​to generate... Shaft current, formula:

[0050]

[0051]

[0052] In the formula, Indicates at a point in time hour shaft current, Represents the sine function. Represents the cosine function. Indicates at a point in time The position angle of the rotor flux linkage of the doubly fed induction generator at that time (can be obtained in real time by the stator voltage phase angle through a phase-locked loop (PLL) and calculated based on the synchronization frequency of the DFIG);

[0053] Use rules of thumb to set the corresponding reference current, including Shaft reference current and Shaft reference current , combined The shaft current is calculated by subtraction to determine the current error between two currents along the same axis. Shaft current error and Shaft current error ;

[0054] for Shaft current error and Shaft current error Construct sliding surfaces separately to obtain the sliding function value for each axis, as shown in the formula:

[0055]

[0056] In the formula, Indicates at a point in time Time The sliding mode function value along each axis, Indicates at a point in time Time Current error in each axis, Indicates the sliding surface scaling factor. Represents the integral variable. Representing the time point of the integral variable Upper Current error in each axis;

[0057] The time derivative of the current error in each axis is obtained by using the chain rule (Leibniz rule), and the first derivative of the current error is obtained. The sliding surface derivative expression is constructed. The standard current dynamic equation of the doubly fed induction generator is substituted into the sliding surface derivative expression to obtain the complete expression of the sliding constraint. After applying the constraint that the sliding surface approaches zero according to the Sliding Mode Condition, the terms are rearranged to form the equivalent control quantity expression, and the output equivalent control quantity is calculated.

[0058] The derivative expression for constructing the sliding surface is given by the following formula:

[0059]

[0060] In the formula, Indicates at a point in time Time The first derivative of each axial sliding mode function value Indicates at a point in time Time First derivative of current error in each axis;

[0061] The standard current dynamic equation for the doubly-fed induction generator is as follows:

[0062]

[0063] In the formula, Indicates at a point in time Time The first derivative of the current along each axis, This indicates the rotor-side inductance (the nominal inductance value can be obtained from the equipment documentation). Indicates at a point in time Time Control laws for each axis (parameters to be solved). This indicates the rotor-side resistance (the nominal value can be read from the datasheet provided by the doubly-fed induction generator manufacturer). Indicates at a point in time Time Current in each axis, The frequency representing the grid angle (can be generated by differentiating the phase angle of the grid obtained by a phase-locked loop (PLL)). Indicates at a point in time Rotor number The flux linkage values ​​for each axis (which can be generated by the flux linkage observer);

[0064] The complete expression for the sliding mode constraint is as follows:

[0065]

[0066] The specific steps for the subsequent item relocation are as follows:

[0067]

[0068]

[0069] The formula for forming the equivalent control quantity expression is:

[0070]

[0071] In the formula, Indicates at a point in time Time The equivalent control quantity of each axial sliding mode function value;

[0072] It should be noted that the first derivative of the sliding mode function value While occupying a central position in the derivation of the control law, its essence lies in the applied convergence condition constraint. According to sliding mode control theory, when the state approaches and slides into the sliding surface, it should satisfy the following condition: Under these conditions, it can be used to substitute and inversely solve the differential dynamic equation, ultimately yielding a unique and closed-loop control law expression. Secondly, This is a sliding condition compensation term used to accelerate error convergence, and the term " "This is to apply an error-driving force, accelerate convergence, and maintain sliding mode stability. If this term is taken as " "It will deviate from the sliding surface, which is equivalent to applying a force in the opposite direction, resulting in a decrease in approach speed and inability to enter the sliding zone;

[0073] Set critical threshold (In existing technologies, the range of values ​​for sliding mode error is typically set as follows:) The minimum stable normalized sliding mode error range defined according to industry standards or mature controllers (e.g., ABB, Siemens DFIG controllers) can serve as the critical threshold for this invention. (Taking 1 as the default value), a smooth boundary transformation is performed on the sliding mode function value for each axis to obtain the saturation function. Combined with the equivalent control quantity, a control law expression is constructed, and the output for each axis at time point is calculated. Time control law;

[0074] The saturation function is obtained, and the formula is:

[0075]

[0076] In the formula, Represents the saturation function. Represents absolute value. Indicates the first The sliding mode function value for each axis;

[0077] The formula for constructing the control law is as follows:

[0078]

[0079] In the formula, Indicates at a point in time Time The control law for each axis (the parameters have been solved). This represents the jitter suppression coefficient, and the standard industry range for this coefficient is... The present invention can set this coefficient based on the ratio of grid voltage fluctuation to rotor-side inductance. For example, when the grid voltage fluctuation is 20V and the rotor-side inductance is 0.2H, the present invention can set the default value of the chattering suppression coefficient to 2.5.

[0080] The control law is used as the input to the back-to-back pulse width modulation converter, and the output is the actual voltage value applied to the rotor winding. The Euler difference method is applied for discretization to generate the rotor current at the next time point for each axis, including the rotor... shaft current Rotor shaft current ;

[0081] Based on the rotor current, the quasi-steady-state approximation in the rotor-side induced voltage coupling approximation method is used to approximate the rotor current, generating the stator voltage, including the stator voltage. shaft voltage and stator shaft voltage Based on the rotor current, the three-phase output active power of the doubly-fed induction generator is calculated using the following formula:

[0082]

[0083] In the formula, This indicates the time point of the doubly-fed induction generator. The three-phase output active power at that time, stator The axis at time point Voltage at that time Indicates rotor The axis at time point Current at that time stator The axis at time point Voltage at that time Indicates rotor The axis at time point Current at that time;

[0084] Parallel analysis of real-time voltages at the output of photovoltaic arrays within a standardized dataset, using the incremental conductance method to obtain the optimal operating point voltage of the photovoltaic array at the current time. (That is, by calculating the ratio of voltage to current at the current time point, a conductance value is generated; and based on the ratio of the difference between current and voltage at the current time point and the previous time point, an incremental conductance value is generated. The current conductance value and the incremental conductance value are summed, and the summation value is judged according to the classical maximum power point criterion (i.e., "0") of the incremental conductance method. When the summation value is greater than the maximum power point criterion, it means that the current time point is to the left of the maximum power point, and the voltage should be increased; when the summation value is less than the maximum power point criterion, it means that the current time point is to the right of the maximum power point, and the voltage should be decreased; when the summation value is equal to the maximum power point criterion, it means that the current time point corresponds to the maximum power point, which is the optimal operating point voltage.) Based on the real-time voltage at the current output of the photovoltaic array, calculate the initial Boost duty cycle required for the boost converter. The formula is:

[0085]

[0086] In the formula, Indicates the initial Boost duty cycle. Indicates at a point in time The real-time voltage at the output of the photovoltaic array. This represents the optimal operating point voltage of the photovoltaic array;

[0087] The target voltage for the boost control of the step-up converter is set based on the rated voltage of the heating equipment in the wind power plant. Combined with DC voltage stability values ​​in the standardized dataset The voltage error is calculated, and its derivative is taken to obtain the rate of error change. This rate of error change is then used as the basis for a fuzzy logic controller (which employs a standard five-level trigonometric membership function, including NB (negative large), NS (negative small), Z (zero), PS (positive small), and PB (positive large). Logical rules are then constructed to cover the entire set and generate the input space. For example, if the voltage error... It is NB and the error change rate If it is NB, then the adjustment amount If voltage error For Z and the rate of change of error If it is Z, then the adjustment amount is... If voltage error The value is PB and the error rate of change is... If it is PB, then the adjustment amount The input is the Boost duty cycle adjustment amount, which is then used to perform the Boost duty cycle update and obtain the updated Boost duty cycle.

[0088] The formula for calculating the voltage error is:

[0089]

[0090] In the formula, Indicates at a point in time Voltage error at that time This indicates the target voltage for Boost control;

[0091] The derivative of the voltage error is given by the following formula:

[0092]

[0093] In the formula, Indicates at a point in time The rate of change of error over time;

[0094] The formula for performing the Boost duty cycle update is:

[0095]

[0096] In the formula, Indicates at a point in time Update Boost duty cycle at time Indicates at a point in time Adjustment amount at the time;

[0097] Based on the updated Boost duty cycle, and further combined with the real-time voltage and current at the output of the standardized photovoltaic array, the output voltage of the boost converter at the next time point and the output power of the photovoltaic array at the next time point are calculated respectively.

[0098] The formula for calculating the output voltage of the boost converter at the next time point is:

[0099]

[0100] In the formula, Indicates at a point in time The output voltage of the boost converter;

[0101] The formula for calculating the output power of the photovoltaic array at the next time point is:

[0102]

[0103] In the formula, Indicates the output of the photovoltaic array at a specific time point. Output power at that time Indicates the output of the photovoltaic array at a specific time point. The current at that time.

[0104] The sliding surface is constructed based on the d / q-axis current error, rather than the traditional PID control path centered on voltage deviation. This step is based on the sliding surface formed by the integral of the rotor current error, which not only reflects the operating state of the equipment but also inherently includes the system's inertia and disturbance characteristics. This design combines the control derivative expansion of the Leibniz chain rule, resulting in an equivalent control law formula with interpretable physical parameter mapping, not derived from empirical parameter tuning, thus ensuring the model's broad adaptability and engineering feasibility. The input space is established through the voltage error change rate, and its output variable directly drives the Boost duty cycle update, combined with... The optimal operating point on the photovoltaic side is generated by tracking. This differs from existing static methods that primarily rely on open-circuit voltage proportionality or conventional conductivity increment methods. By introducing fuzzy logic, this invention achieves stable and dynamic adjustment path selection for the boost circuit under complex operating conditions (such as intermittent cloud cover and sudden wind speed changes), significantly improving the system's energy efficiency ratio and load response consistency. Furthermore, by using the control law as the input driving variable for back-to-back PWM converters, it directly generates the stator and rotor responses at the next time point, and further feeds back to form sliding window scheduling weights, giving this invention time-domain adaptability and memory. This "predictive control → current output → feedback relearning" structure breaks through the existing passive adjustment paradigm under single-point operating conditions, giving this invention significant real-time advantages. Moreover, the calculation of voltage error is not a signal difference in the general sense, but rather guides the system to respond to "changing trends" rather than "static deviations." This trend prediction driving logic is specially designed to address the dual challenges of "source-load instability + response delay" in new energy power plants. Secondly, this step as a whole forms a "function-power-thermal load" technical link, where the internal data flow direction, control action, and physical energy flow are dynamically coupled synchronously.

[0105] Furthermore, the sliding window weighting method is used to calculate the scheduling weight of power supply equipment, defining the doubly-fed induction generator and photovoltaic array as energy paths, and the three-phase output active power... and output power Integrate into input items Starting from the current time point and going back to form a sliding window, the input items are extracted, the time decay weighted sum is calculated, the exponential weight is obtained and normalized to generate the scheduling weight;

[0106] The formula for calculating the time decay weighted sum of the extracted input items is as follows:

[0107]

[0108] In the formula, Indicates the first The exponential weights of each energy path, Indicates the length of the sliding window. Indicates the time decay coefficient. The setting determines the degree of memory for historical cycle power input, therefore, The larger the value, the more emphasis is placed on recent data, and the more sensitive the response becomes. The smaller the value, the more emphasis is placed on cyclical trends, resulting in a slower but smoother response. Therefore, the standard value range of 0.75 to 0.95 in the existing technology is adopted. However, to prevent subsequent scheduling oscillations, this invention uses 0.85 as the default value. Indicates the window index, when =1 indicates the earliest window. = The time indicates the current window. Indicates the first The energy source in the first Output power within a window;

[0109] The obtained exponential weights are normalized using the following formula:

[0110]

[0111] In the formula, Indicates the first The scheduling weight of each energy path, This indicates the scheduling weight of the photovoltaic array's energy path. This represents the scheduling weight of the energy path of the doubly-fed induction generator.

[0112] By passing each energy path in the past The output power within each cycle is weighted and integrated using a time-decay method. This strategy enhances the influence of the current cycle data, making the scheduling weights highly sensitive to recent changes in output power. This allows for rapid scheduling adjustments in scenarios such as changes in solar radiation and wind speed fluctuations, ensuring the continuity of the heating system's power. Furthermore, by unifying the dual-path outputs into a scheduling weight space between (0,1), it ensures that regardless of changes in the absolute output power of photovoltaic or wind power, the final allocation mechanism still reflects the relative contribution between the paths, effectively avoiding the bias problem of "one energy source dominating scheduling." Moreover, unlike traditional strategies that use a fixed allocation ratio (such as "60% photovoltaic, 40% wind power"), this scheme has real-time evolution capabilities. When continuous cloudy and rainy weather causes a significant reduction in photovoltaic power, the weight of the wind power path will be dynamically increased to prioritize heating stability; while in sunny weather, the photovoltaic path weight will automatically increase, achieving precise control of the "solar-wind complementarity" mechanism. Secondly, the scheduling weights are not only used for immediate power allocation but also serve as fundamental variables for subsequent system energy efficiency analysis, supply-demand difference determination, and inverter mode selection. It can be connected to the energy efficiency ratio function and temperature difference feedback module to form a closed-loop control path of scheduling-execution-evaluation-update, which comprehensively improves the system's autonomy.

[0113] Furthermore, by setting temperature control targets and combining them with temperature data, the total heating power is calculated, and the total heating power is allocated to the power supply equipment according to the scheduling weights to obtain preliminary allocation results and grid power indicators. Temperature control targets are set for each area through temperature setters, and the real-time temperature of each area is obtained through temperature sensors. Based on the corresponding temperature control targets, the temperature difference of each area at a given time is calculated. The thermal conductivity coefficient of each area is set according to its insulation performance. After calculating the heating power required for each area based on the temperature difference, the total heating power is obtained using a summation formula. Power allocation for each energy path is then performed based on the scheduling weights to obtain preliminary allocation results.

[0114] The formula for calculating the temperature difference of each region at a given time point is:

[0115]

[0116] In the formula, Indicates the first Each region at a given time point Temperature difference over time Indicates the first Temperature control targets for each region Indicates the first Each region at a given time point Real-time temperature at that time;

[0117] The formula for calculating the heating power required for each area based on temperature difference is as follows:

[0118]

[0119] In the formula, Indicates the first Each region at a given time point The required heating power at that time Indicates the first Thermal conductivity coefficient of each region;

[0120] The total heating power is obtained using a summation formula, which is:

[0121]

[0122] In the formula, Indicates at a point in time Total heating power at that time Indicates the total number of regions;

[0123] The power allocation for each energy path is performed by combining scheduling weights, and the formula is as follows:

[0124]

[0125] In the formula, Indicates the first Each energy path at a given time point Power allocation during the process;

[0126] The grid interface is defined as a compensation path, and based on the preliminary allocation results, the grid compensation power of the grid interface at the current time point is calculated and limited to obtain the grid power, as shown in the formula:

[0127]

[0128]

[0129] In the formula, Indicates at a point in time The power compensation of the grid along the compensation path. This indicates the operation of retrieving the maximum value. This indicates the energy path of the doubly-fed induction generator at time point. Power distribution at time, Indicates the energy path of the photovoltaic array at a given time point. Power distribution at time, This indicates the maximum power capacity of the compensation path (which can be read from the power data interface to read the main transformer parameters in the wind power renewable energy station).

[0130] By calculating the difference between the real-time temperature and the target temperature of each region, accurate identification of individual heat demand is achieved. A heat power calculation formula is formed using the thermal conductivity coefficient, avoiding overheating and underheating phenomena in traditional centralized heating, thus improving heat energy utilization and local comfort. Furthermore, the power demand of each region is aggregated using a summation formula, achieving a unified assessment of the global load of the heating system and providing accurate demand references for subsequent dynamic scheduling. This step establishes a smooth transmission path between end-point sensing and energy dispatch, effectively improving the overall system's response speed and robustness. In multi-energy heating scenarios, a scheduling weight is introduced as a proportional factor to automatically adjust the participation ratio of wind power and photovoltaic power in heating tasks. Compared to a fixed power ratio strategy, this mechanism can respond in real-time to factors such as weather fluctuations and equipment status changes, achieving optimal resource synergy. Secondly, a grid interface is designed as a flexible compensation path, combined with the formula... It can dynamically determine whether to introduce compensation power from the grid, and is limited by... This ensures safe operation. This mechanism enables seamless switching of multi-energy coordinated heating, avoiding insufficient heat supply due to fluctuations in renewable energy sources. Furthermore, by using a maximum value function to filter negative values, it ensures that the grid supplies energy only when necessary, avoiding over-reliance or frequent switching, effectively reducing system energy consumption and maintenance costs.

[0131] S3. Based on the preliminary allocation results and grid power, after performing parameter updates and feedback, calculate the power supply and demand difference and voltage error of the total heating power, determine the current required working mode, and perform coordinated control and heating.

[0132] Specifically, based on the preliminary allocation results and grid power, the parameter update and feedback process calculates the energy efficiency ratio at the current time point based on the grid power and preliminary allocation results, then calculates the global error based on the temperature difference, and combines the energy efficiency ratio with the time decay coefficient. After the update, it is fed back into the formula of time decay weighted sum, and the exponential weight is recalculated to update the scheduling weight of each energy path. The new scheduling weight is used to execute the preliminary allocation result and obtain the grid power, and the new power allocation result and grid compensation power are obtained.

[0133] The formula for calculating the energy efficiency ratio at the current time point is:

[0134]

[0135] In the formula, Indicates at a point in time Energy efficiency ratio at that time;

[0136] The formula for calculating the global error is as follows:

[0137]

[0138] In the formula, Indicates at a point in time Global error at time Represents absolute value;

[0139] The combined energy efficiency ratio execution time decay coefficient The update formula is:

[0140]

[0141] In the formula, Indicates at a point in time The time decay coefficient at that time, Indicates at a point in time The time decay coefficient at that time, Indicates the weight of energy efficiency ratio. Indicates the baseline energy efficiency ratio. Indicates the weight of temperature difference. Indicates the upper limit of the temperature difference;

[0142] Energy efficiency ratio weighting and temperature difference weight The energy efficiency ratio can be obtained by constructing a simulation model and applying a step perturbation to test the weighting of the energy efficiency ratio. and temperature difference weight For time decay coefficient The response curve is adjusted; if the response curve is too large, it indicates oscillation; if the response curve is too small, it indicates slow convergence. During the experiment, a test energy efficiency ratio weight is generated. and temperature difference weight The range of values ​​for are respectively , The present invention, based on the actual heating characteristics of wind farms, can weight the energy efficiency ratio. and temperature difference weight The default values ​​are set to 0.05 and 0.06 respectively, which can improve the energy efficiency tracking speed and enhance temperature control stability;

[0143] Benchmark energy efficiency ratio The 10- or 30-day average energy efficiency setting can be obtained by statistically analyzing the SCADA system of the wind power new energy plant;

[0144] upper limit of temperature difference It can be set according to wind power industry standards. For example, in standard wind power industry standards, the allowable error for main control room temperature is... The equipment temperature control requirements are in To ensure unified scheduling across all regions, this invention can take... As the default value;

[0145] Based on the new power allocation results, including time points The power allocation of the doubly-fed induction generator energy path and the power allocation of the photovoltaic array energy path are calculated. According to the power allocation in the new power allocation result, the power allocation of each energy path is taken as the set value and sent to the sliding mode controller and the fuzzy controller to generate new three-phase output active power and photovoltaic array output power. Combined with the new grid compensation power, the power is summed to generate the total available power.

[0146] By employing dual-channel control based on energy efficiency ratio and temperature difference feedback, adaptive adjustment of the dynamic scheduling factor is achieved. Unlike traditional fixed scheduling schemes, this mechanism can reflect the system's energy efficiency status and temperature control deviation in real time, thereby dynamically updating the scheduling weights and ensuring optimal coordination of energy path outputs at different time points. This structure significantly improves system robustness and is particularly suitable for renewable energy power plants with large solar radiation fluctuations and unstable wind conditions. By controlling the weight distribution of historical inputs and dynamically updating in response to system state, this invention establishes an adaptive closed-loop link of "scheduling-feedback-rescheduling". This mechanism can maintain the overall thermal load responsiveness of the system under nonlinear operating conditions and variable boundary conditions, and suppress excessive bias or response lag, demonstrating excellent dynamic controllability. Secondly, the new power allocation results send wind power and photovoltaic power as target inputs to the sliding mode and fuzzy controller, and combine them with grid power to generate total available power, constructing a coordinated operation strategy of "self-supply-led + grid compensation". This integrated path selection effectively solves the problem of insufficient heating capacity during extreme low wind / no-sunlight periods, ensuring heating continuity and temperature control consistency, thus making this invention particularly suitable for heating scenarios in remote areas.

[0147] Furthermore, the power supply and demand difference and voltage error of the total heating power are calculated, and the required working mode is determined. Coordinated control and heating instructions are executed. A power controller is configured for the electric heating load module, and the total available power is used as the input of the power controller to drive the electric heating load module and perform heating operation on the area.

[0148] During the heating process, calculate the supply-demand difference between the total available power and the total heating power at the next time point. and voltage error Determine the required operating mode of the bidirectional inverter and execute the corresponding operation to obtain the final control basis;

[0149] The determination of the required operating mode of the bidirectional inverter and the execution of corresponding operations refer to situations where there is a supply-demand imbalance. Greater than or equal to the power balance threshold (By querying the maximum starting power fluctuation and overload power of the electric heating equipment, for example, 400W to 1100W, 120% to 150%, the value range is set to 500W to 1500W. In this invention, 1000W can be taken as the power balance threshold.) (default value) and voltage error Less than or equal to the tolerance threshold (By querying the safe operating voltage range and the allowable error range of the voltage, the value range can be set to 1.5V to 6V. In this invention, 3.2V can be used as the tolerance threshold.) When the default value is set, it indicates that the current total available power is surplus. Set the working mode of the bidirectional inverter to inverter power supply (i.e., DC→AC) and perform corresponding operations, including starting the bidirectional inverter, inverting the excess DC active power of the DC bus to the AC bus, prioritizing the allocation of AC power to AC load equipment, and maintaining the normal power supply of direct-supply electric heating equipment.

[0150] When supply and demand are different Less than the power balance threshold And voltage error Greater than the tolerance threshold If the current total available power or DC voltage is insufficient, the bidirectional inverter is set to the rectification compensation mode (i.e., AC→DC) to perform corresponding operations, including starting the bidirectional inverter to perform the rectification function, absorbing AC active power (from the power grid or wind turbine stator) from the AC bus, injecting the rectified power into the DC bus, and supplying it to the direct-supply electric heating equipment, and then delaying or limiting the power supply to the AC load equipment.

[0151] When supply and demand are different Less than the power balance threshold And voltage error Less than or equal to the tolerance threshold If the current total available power is short but in a stable state, the bidirectional inverter is set to the balanced mode to perform corresponding operations, including monitoring the operating mode at subsequent time points. If there is a continuous balanced mode, the scheduling weight is updated again.

[0152] If any condition is not met, the current power supply will be interrupted and an alarm message will be sent to the control personnel.

[0153] pass Real-time calculation and power balance threshold In comparison, this invention can accurately determine whether the power is in a state of excess or shortage, realizing dynamic adjustment of the load power response and avoiding the resource waste or insufficient power supply problems caused by the static allocation of "planned output" in traditional systems. Voltage error With tolerance threshold The dual-condition judgment constitutes an effective protection mechanism for the DC bus voltage, ensuring that the equipment will not suffer losses, malfunctions, or misoperations due to voltage fluctuations during inverter or rectification switching. Simultaneously, it ensures that direct-supply electric heating equipment always operates within its rated voltage range. The three operating modes of this invention are: Inverter power supply (DC→AC): fully utilizing renewable energy generation capacity when power is surplus, prioritizing the operation of high-energy-consuming AC equipment; Rectification compensation (AC→DC): absorbing energy from the AC side to compensate for the heating demand of the DC system when renewable energy is insufficient, avoiding temperature control disconnection; Balanced mode: used for stable transition when the system is in a critical state, and combined with a predictive mechanism to intervene in scheduling updates in advance. This hierarchical operating mode not only improves system flexibility but also enhances the accuracy of energy scheduling and system fault tolerance. Embedding continuous detection logic in the "Balanced mode" enables the feedback of short-term energy supply deviations to the weighted scheduling layer, achieving closed-loop operation of the control chain. This design makes subsequent energy scheduling closer to real supply and demand fluctuations, improving dynamic response efficiency and adaptive capability. An "interruption and alarm" mechanism is set up in addition to all judgment conditions. When an abnormal state that cannot be classified into any mode occurs, the power supply is terminated in a timely manner and an alarm is triggered to notify the management personnel. This realizes multiple fault-tolerant protection from the control algorithm to the engineering site, and enhances the robustness and stability of the project.

[0154] This embodiment also provides a photovoltaic thermal heating control system based on a wind power renewable energy station, including:

[0155] The data acquisition module is deployed to collect equipment operation data and temperature data from power supply equipment, distributed heating terminals and electric heating load modules, and preprocesses them to generate a standardized dataset.

[0156] The adjustment generation module is used to dynamically adjust the output power of the power supply equipment through sliding mode control and fuzzy control respectively, and to calculate the scheduling weight of the power supply equipment using the sliding window weighting method.

[0157] The allocation module is used to set temperature control targets, combine temperature data, calculate the total heating power, and allocate the total heating power to the power supply equipment according to the scheduling weight, so as to obtain the preliminary allocation results and grid power.

[0158] The update feedback module is used to perform parameter updates and feedback based on the initial allocation results and grid power.

[0159] The heating control module is used to calculate the power supply and demand difference and voltage error of the total heating power, and to determine the current required working mode and perform coordinated control and heating.

[0160] This embodiment also provides a computer device applicable to the photovoltaic and solar thermal heating control method based on wind power new energy stations, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the photovoltaic and solar thermal heating control method based on wind power new energy stations as proposed in the above embodiment.

[0161] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0162] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the photovoltaic and solar thermal heating control method based on wind power new energy stations as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A photovoltaic thermal heating control method based on wind power renewable energy stations, characterized in that: include, Power supply equipment, distributed heating terminals and electric heating load modules are deployed in wind power new energy stations. Equipment operation data and temperature data are collected, preprocessed, and standardized datasets are generated. The process of deploying power supply equipment, distributed heating terminals, and electric heating load modules within wind power renewable energy stations, collecting equipment operation data and temperature data for preprocessing, and generating standardized datasets refers to deploying power supply equipment within wind power renewable energy stations, including doubly-fed induction generators, photovoltaic arrays, and grid interfaces; deploying distributed heating terminals for the operation and maintenance living area and wind power main control room within the wind power renewable energy station; setting up electric heating load modules for each area, including direct-supply electric heating equipment and AC load equipment; and equipping each area with temperature sensors and temperature setting devices. Voltage and current data are collected by multiple sensors. All data are denoised, outlier removed, and standardized to obtain a standardized dataset. This dataset includes the standardized real-time voltage and current at the output of the photovoltaic array, the real-time output voltage of the boost converter, and the RMS values ​​of the AC voltage at the AC and DC buses. and DC voltage stability value The stator three-phase voltage and three-phase rotor current on the stator side of the doubly-fed induction generator; Based on a standardized dataset, the output power of the power supply equipment is dynamically adjusted using sliding mode control and fuzzy control, respectively. The aforementioned method, based on a standardized dataset, dynamically adjusts the output power of the power supply equipment using sliding mode control and fuzzy control, respectively. This is based on the three-phase rotor current within the standardized dataset, and the standardized three-phase rotor current is decoupled to [the specified value] using the Clark-Parker transform. coordinate system, to obtain shaft current, including shaft current and shaft current Use rules of thumb to set the corresponding reference current, combined with The shaft current is calculated by subtraction to determine the current error between two currents along the same axis. Shaft current error and Shaft current error ,for Shaft current error and Shaft current error Sliding surfaces are constructed separately to obtain the sliding function value for each axis. The time derivative of the current error for each axis is obtained by using the chain rule. The first derivative of the current error is obtained, and the sliding surface derivative expression is constructed. The standard current dynamic equation of the doubly fed induction generator is substituted into the sliding surface derivative expression to obtain the complete expression of the sliding constraint. After applying the constraint that the sliding surface approaches zero according to the sliding mode condition, the terms are rearranged to form the equivalent control quantity expression, and the output equivalent control quantity is calculated. Set critical threshold For each axis, a smooth boundary transformation is performed on the sliding mode function value to obtain the saturation function. Combined with the equivalent control quantity, a control law expression is constructed, and the output for each axis at time point is calculated. The control law is used as the input to the back-to-back pulse width modulation converter, and the output is the actual voltage value applied to the rotor winding. The Euler difference method is applied for discretization to generate the rotor current for each axis at the next time point, including the rotor... Shaft current, rotor The shaft current, based on the rotor current, is approximated using the quasi-steady-state approximation in the rotor-side induced voltage coupling approximation method to generate the stator voltage, including the stator voltage. Shaft voltage and stator Calculate the three-phase output active power of the doubly-fed induction generator by combining the shaft voltage and rotor current. The system uses the real-time voltage of the photovoltaic array output terminal within a standardized dataset in parallel. It employs the incremental conductance method to obtain the optimal operating point voltage of the photovoltaic array at the current time point. Combined with the real-time voltage of the photovoltaic array output terminal, it calculates the initial Boost duty cycle required by the boost converter. Based on the rated voltage of the heating equipment in the wind power new energy station, it sets the Boost control target voltage of the boost converter. Combined with the DC voltage stability value within the standardized dataset, it calculates the voltage error. The derivative of the voltage error is obtained to get the error change rate. The error change rate is used as the input of the fuzzy logic controller to output the adjustment amount of the Boost duty cycle and perform Boost duty cycle update. After obtaining the updated Boost duty cycle, it further combines the real-time voltage and current of the photovoltaic array output terminal after standardization to calculate the output voltage of the boost converter and the output power of the photovoltaic array output terminal at the next time point. The scheduling weight of the power supply equipment is calculated using a sliding window weighting method. Temperature control targets are set and combined with temperature data to calculate the total heating power. The total heating power is then allocated to the power supply equipment according to the scheduling weights to obtain preliminary allocation results and grid power. Based on the preliminary allocation results and grid power, after parameter updates and feedback, the power supply and demand difference and voltage error of the total heating power are calculated, the required working mode is determined, and coordinated control and heating are executed. The parameter update and feedback based on the preliminary allocation results and grid power refers to calculating the energy efficiency ratio at the current time point based on the grid power and preliminary allocation results, then calculating the global error based on the temperature difference, and combining the energy efficiency ratio with the time decay coefficient. After the update, it is fed back into the formula of time decay weighted sum, and the exponential weight is recalculated to update the scheduling weight of each energy path. The new scheduling weight is used to execute the preliminary allocation result and obtain the grid power, and the new power allocation result and grid compensation power are obtained. Based on the new power allocation results, including time points The power allocation of the doubly fed induction generator energy path and the power allocation of the photovoltaic array energy path are calculated. According to the power allocation in the new power allocation result, the power allocation of each energy path is taken as the set value and sent to the sliding mode controller and the fuzzy controller to generate new three-phase output active power and photovoltaic array output power. The new grid compensation power is combined and summed to generate the total available power. The process of determining the required operating mode and executing coordinated control and heating instructions involves configuring a power controller for the electric heating load module, using the total available power as the input of the power controller, driving the electric heating load module, and performing heating operations on the area. During the heating process, the supply-demand difference and voltage error between the total available power and the total heating power at the next time point are calculated. The required operating mode of the bidirectional inverter is determined and corresponding operations are executed to obtain the final control basis. When supply and demand are different Greater than or equal to the power balance threshold And voltage error Less than or equal to the tolerance threshold If the current total available power is sufficient, the bidirectional inverter is set to DC→AC inverter mode and corresponding operations are performed, including starting the bidirectional inverter, inverting the excess DC active power of the DC bus to the AC bus, prioritizing the allocation of AC power to AC load equipment, and maintaining the normal power supply of direct-supply electric heating equipment. When supply and demand are different Less than the power balance threshold And voltage error Greater than the tolerance threshold If the current total available power or DC voltage is insufficient, the bidirectional inverter is set to the rectification compensation mode AC→DC, and corresponding operations are performed, including starting the bidirectional inverter to perform the rectification function, absorbing AC active power from the AC bus, injecting the rectified power into the DC bus, and supplying it to the direct-supply electric heating equipment, and then delaying or limiting the power supply to the AC load equipment. When supply and demand are different Less than the power balance threshold And voltage error Less than or equal to the tolerance threshold If the current total available power is short but in a stable state, the bidirectional inverter is set to the balanced mode to perform corresponding operations, including monitoring the operating mode at subsequent time points. If there is a continuous balanced mode, the scheduling weight is updated again. If any condition is not met, the current power supply will be interrupted and an alarm message will be sent to the control personnel.

2. The photovoltaic thermal heating control method based on wind power new energy stations as described in claim 1, characterized in that: The method of calculating the scheduling weight of power supply equipment using the sliding window weighting method refers to defining the doubly fed induction generator and photovoltaic array as energy paths, integrating the three-phase output active power and output power as input items, tracing back from the current time point to form a sliding window, extracting the input items, calculating the time decay weighted sum, obtaining the exponential weight, normalizing it, and generating the scheduling weight.

3. The photovoltaic thermal heating control method based on wind power new energy stations as described in claim 2, characterized in that: The process involves setting temperature control targets, combining temperature data, calculating the total heating power, and allocating the total heating power to power supply equipment according to scheduling weights to obtain preliminary allocation results and grid power indicators. Temperature control targets are set for each region using a temperature setter, and the real-time temperature of each region is obtained through temperature sensors. Based on the corresponding temperature control targets, the temperature difference of each region at a given time point is calculated. A thermal conductivity coefficient is set for each region based on its insulation performance. After calculating the required heating power for each region based on the temperature difference, the total heating power is obtained using a summation formula. Finally, power allocation for each energy path is executed based on scheduling weights to obtain preliminary allocation results. The grid interface is defined as a compensation path, and based on the preliminary allocation results, the grid compensation power of the grid interface at the current time point is calculated and limited to obtain the grid power.

4. A photovoltaic thermal heating control system based on a wind power renewable energy plant, based on the photovoltaic thermal heating control method based on a wind power renewable energy plant as described in any one of claims 1 to 3, characterized in that: include, The data acquisition module is deployed to collect equipment operation data and temperature data from power supply equipment, distributed heating terminals and electric heating load modules, and preprocesses them to generate a standardized dataset. The adjustment generation module is used to dynamically adjust the output power of the power supply equipment through sliding mode control and fuzzy control respectively, and to calculate the scheduling weight of the power supply equipment using the sliding window weighting method. The allocation module is used to set temperature control targets, combine temperature data, calculate the total heating power, and allocate the total heating power to the power supply equipment according to the scheduling weight, so as to obtain the preliminary allocation results and grid power. The update feedback module is used to perform parameter updates and feedback based on the initial allocation results and grid power. The heating control module is used to calculate the power supply and demand difference and voltage error of the total heating power, and to determine the current required working mode and perform coordinated control and heating.

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