Intelligent collaborative control method and system of electric drive heat pump for severe grid conditions

Abstract

This invention proposes an intelligent collaborative control method and system for electrically driven heat pumps under harsh power grid conditions. The method includes: acquiring DC bus voltage, power grid frequency, and compressor operating parameters; calculating power grid quality status variables and constructing system status; determining a feasible domain of control behavior based on the aforementioned status, including compressor frequency ramp-up, PWM configuration, and upper bound of auxiliary machine coordination; generating a collaborative adjustment scheme based on the feasible domain, including target frequency, modulation configuration, and auxiliary machine commands; and updating the inverter output and auxiliary machine drive commands according to the constrained slope execution trajectory. This invention, by real-time sensing of power grid quality and system status, dynamically constructing the control feasible domain, and generating collaborative strategies, enables intelligent collaborative adjustment of the compressor and auxiliary machine under harsh conditions such as power grid voltage dips or frequency fluctuations. This effectively avoids system downtime and improves the operational stability and low-voltage ride-through capability of the heat pump unit in complex power grid environments.

Description

Technical Field

[0001] This invention belongs to the field of heat pump control technology, and particularly relates to an intelligent collaborative control method and system for electrically driven heat pumps under harsh power grid conditions. Background Technology

[0002] Electric heat pumps are rapidly gaining popularity in residential, commercial, and industrial settings due to their high energy efficiency and cleanliness. To adapt to wide load conditions, heat pumps generally employ variable frequency drives (VFDs), achieving a trade-off between capacity and energy efficiency by adjusting the compressor speed and coordinating with auxiliary equipment such as fans and water pumps. However, in areas at the end of the distribution network, with high renewable energy penetration, or along older lines, power quality issues such as voltage dips, frequency shifts, harmonics, and imbalances are more common. Disturbances often first affect the inverter's rectification and DC bus sides, causing a decrease in bus energy margin, an increase in motor current spikes, and increased thermal stress on power devices, subsequently triggering undervoltage and overcurrent protection or causing compressor instability. Existing solutions mainly include external voltage stabilization and filtering hardware and internal threshold protection or simple frequency reduction strategies within the inverter. The former increases cost and size and is inconvenient for engineering modifications, while the latter usually relies on a single electrical quantity or protection threshold, making it difficult to simultaneously consider the grid's carrying capacity and the heat pump's current load status. This can easily lead to unnecessary shutdowns during short-term disturbances or overly coarse control actions under persistently poor grid conditions. On the other hand, while simply reducing the frequency lowers the instantaneous power demand, without coordination with auxiliary equipment and modulation strategies, it may still cause the superposition of bus fluctuations and current surges, or lead to comfort and reliability risks due to the mismatch between the heat exchanger side and the compressor capacity. More importantly, in many schemes, grid quality information is still only at the level of "triggering protection" and is not used as a core control variable in constraints and decisions. As a result, under harsh grid conditions, the system often oscillates between "excessive conservatism leading to frequent shutdowns" and "pursuing output leading to risk accumulation," making it difficult to achieve safe, continuous, and controllable operation without increasing hardware costs. Summary of the Invention

[0003] The purpose of this invention is to design an intelligent collaborative control method and system for electrically driven heat pumps under harsh power grid conditions, which can improve the adaptability and reliability of heat pump frequency conversion systems to harsh power grids without relying on additional hardware investment.

[0004] To achieve the above objectives, a first aspect of the present invention provides an intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions, the method comprising: Obtain DC bus voltage, grid frequency, compressor operating frequency, and compressor current; Calculate the power grid quality status quantity based on the DC bus voltage and the power grid frequency, and construct the system status based on the compressor operating frequency and the compressor current; The feasible region of control behavior is determined based on the power grid quality status quantity and the system status. The feasible region of control behavior includes the allowable range of compressor frequency increase, the PWM modulation configuration set, and the upper bound of the auxiliary machine coordination range. A collaborative adjustment scheme is generated based on the feasible domain of the control behavior. The collaborative adjustment scheme includes the compressor target frequency, the target PWM modulation configuration, and the auxiliary machine target command. The inverter output frequency and auxiliary machine drive commands are updated according to the restricted slope execution trajectory to execute the aforementioned coordinated adjustment scheme.

[0005] Furthermore, the calculation process for the power grid quality state parameters includes: Calculate the first ratio of the real-time DC bus voltage to the rated DC bus voltage; Calculate the second ratio of the real-time grid frequency to the rated grid frequency; The power grid quality state quantity is obtained by multiplying the first weighting coefficient by the first ratio and adding the second weighting coefficient by the second ratio. Wherein, the sum of the first weighting coefficient and the second weighting coefficient is 1.

[0006] Furthermore, the process of determining the allowable frequency increase range of the compressor within the feasible domain of control behavior includes: Calculate the third ratio of the compressor current to the upper limit of the safe current to obtain the current margin. Calculate the fourth ratio of the compressor's operating frequency to the compressor's maximum permissible frequency; Multiply the power grid quality status quantity by the current residual margin to obtain the basic margin term; Multiplying the fourth ratio by the third ratio and then by the calibration coefficient yields the high-frequency high-current coupling suppression term; The comprehensive margin is obtained by subtracting the high-frequency, high-current coupling suppression term from the basic margin term; The compressor's frequency upscaling allowance is obtained by multiplying the overall margin by the remaining frequency space.

[0007] Furthermore, the process of generating the compressor target frequency in the coordinated mediation scheme includes: Read the current compressor operating frequency and the compressor's allowed frequency increase range; The scaling factor is obtained by dividing the allowable increase in compressor frequency by the sum of the frequency scale parameter and the allowable increase in compressor frequency. Multiply the scaling factor by the compressor's allowed frequency increase to obtain the frequency increment; The frequency increment is added to the current compressor operating frequency to obtain the compressor target frequency.

[0008] Furthermore, the process of generating the target PWM modulation configuration in the collaborative mediation scheme includes: The fifth ratio of the actual frequency increment to the total allowable space is calculated to obtain the dimensionless adjustment ratio; Based on the range of the dimensionless adjustment ratio, select the corresponding carrier frequency, modulation depth upper limit and dead time from the preset PWM parameter table; The selected carrier frequency, upper limit of modulation depth, and dead time are used as the target PWM modulation configuration.

[0009] Furthermore, the process of generating auxiliary target instructions in the collaborative mediation scheme includes: Read the current speed ratio of the auxiliary machine and the upper limit of the auxiliary machine's cooperative amplitude; The auxiliary machine increment is calculated using the same scaling factor as that used to generate the target frequency of the compressor; The auxiliary machine increment is added to the current speed ratio of the auxiliary machine to obtain the target speed ratio of the auxiliary machine; The auxiliary machine target speed ratio is converted into the auxiliary machine target frequency as the auxiliary machine target command.

[0010] Furthermore, the process of updating the inverter output frequency according to the trajectory executed according to the constrained slope includes: Calculate the frequency error between the compressor's target frequency and the current actual output frequency; Calculate the product of the maximum allowable rate of frequency change and the control period to obtain the allowable frequency change per period; If the absolute value of the frequency error is greater than the allowable frequency change in a single cycle, then the execution frequency of the next cycle is set to the current actual output frequency plus or minus the allowable frequency change in a single cycle. If the absolute value of the frequency error is less than or equal to the allowable frequency change per cycle, then the execution frequency of the next cycle is set to the compressor target frequency.

[0011] Furthermore, the process of updating the auxiliary machine drive instructions according to the restricted slope execution trajectory includes: Convert the auxiliary machine target instruction into the auxiliary machine target frequency; Calculate the auxiliary machine frequency error between the target frequency of the auxiliary machine and the current actual frequency of the auxiliary machine; The frequency error of the auxiliary machine is saturated and clipped using the single-cycle allowable frequency change amount that is the same as the output frequency of the updated frequency converter. The auxiliary machine frequency instruction for the next cycle is updated based on the trimmed error value and written to the auxiliary machine driver.

[0012] Furthermore, the method triggers an emergency frequency reduction protection mode when it detects that the DC bus voltage drop exceeds a preset threshold. In the emergency frequency reduction protection mode, the allowable frequency increase range of the compressor is forcibly set to zero; Switch the target PWM modulation configuration to a preset combination of low-voltage crossover carrier frequency and maximum modulation depth; The auxiliary machine target command is locked to the lowest speed frequency corresponding to maintaining the minimum circulation flow of the system; The emergency frequency reduction protection mode can only be exited and normal coordinated control based on the power grid quality status can only be restored when the DC bus voltage recovers to above the preset recovery threshold and the duration reaches the preset stable duration.

[0013] A second aspect of the invention provides an intelligent collaborative control system for electrically driven heat pumps under harsh power grid conditions, the system comprising: The data acquisition and status construction module is used to acquire DC bus voltage, grid frequency, compressor operating frequency and compressor current, calculate grid quality status based on the DC bus voltage and grid frequency, and construct system status based on the compressor operating frequency and compressor current. The feasible region determination module is used to determine the feasible region of control behavior based on the power grid quality state quantity and the system state. The feasible region of control behavior includes the compressor frequency increase allowable amplitude, the PWM modulation configuration set, and the upper bound of the auxiliary machine coordination amplitude. The scheme generation module is used to generate a coordinated adjustment scheme based on the feasible domain of the control behavior. The coordinated adjustment scheme includes the compressor target frequency, the target PWM modulation configuration, and the auxiliary machine target command. The trajectory execution control module is used to update the inverter output frequency and auxiliary machine drive commands according to the restricted slope in order to execute the aforementioned coordinated adjustment scheme.

[0014] The beneficial technical effects of the present invention are at least as follows: To address the aforementioned problems, this invention provides an intelligent collaborative control method and system for electrically driven heat pumps under harsh power grid conditions. First, key electrical characteristics such as DC bus voltage and power grid frequency are compressed into real-time updatable power grid quality status quantities. Then, load characteristics such as compressor operating frequency and motor current are used to form the system status, thereby characterizing the power grid carrying capacity and current load intensity with a small amount of engineering-obtainable data. Subsequently, a feasible domain for control behavior is generated based on the above status, used to define the compressor frequency ramp-up space, the selectable range of modulation configurations, and the auxiliary machine coordination amplitude boundary, ensuring that any subsequent adjustment commands are generated within this feasible domain and are executable. Based on this, a collaborative adjustment scheme is generated, coordinating and determining the compressor target frequency, PWM modulation configuration, and auxiliary machine target commands under the same constraint scale. This scheme takes effect on the inverter and auxiliary machine drive side through a constrained slope execution trajectory, thereby suppressing bus energy surges and current spikes under power grid disturbances, reducing the probability of protection triggering, and maintaining continuous equipment operation. By integrating the coupling constraints of power grid quality and heat pump load status, the boundary representation of feasible domain, and verifiable implementation, this invention improves the adaptability and reliability of heat pump inverter systems to harsh power grids without relying on additional hardware investment, and enables the system to smoothly transition to new operating conditions in a controllable manner when power grid conditions change. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a flowchart of the intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to the present invention.

[0017] Figure 2 This is a framework diagram of the intelligent collaborative control system for electrically driven heat pumps designed for harsh power grid conditions, as described in this invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] In one or more embodiments, such as Figure 1 As shown, a smart collaborative control method for electrically driven heat pumps under harsh power grid conditions is disclosed, the method comprising the following: S1: Obtain the DC bus voltage, grid frequency, compressor operating frequency, and compressor current; calculate the grid quality status based on the DC bus voltage and grid frequency, and construct the system status based on the compressor operating frequency and compressor current; Specifically, during the operation of an electrically driven heat pump, it is necessary to compress the grid disturbance state and the compressor's current operating condition into engineering quantities that can directly participate in subsequent control decisions. This step completes data acquisition and state construction using the existing sampling and control hardware of the frequency converter. The grid-side signal is acquired by the sampling circuit at the input end of the frequency converter. The three-phase input voltage enters the ADC channel of the DSP or MCU after passing through a resistor divider network and an isolation operational amplifier. The control chip samples at a fixed sampling frequency (e.g., 10kHz) and calculates the effective voltage value. The DC bus voltage is acquired through a bus sampling circuit, which typically uses a high-voltage resistor divider and isolation amplification structure to convert the high-voltage bus signal into a low-voltage analog quantity before sending it to the ADC to form a digital quantity. Grid frequency The instantaneous frequency value is calculated by the phase-locked loop (PLL) module inside the control chip. The PLL obtains the instantaneous frequency value by detecting voltage zero-crossing or synchronous coordinate transformation. Since the bus voltage directly reflects the rectification and inverter energy margin, and the frequency deviation reflects synchronization stability, therefore, [the appropriate frequency is selected]. and As the core characterizing variable of power grid quality, and to construct the power grid quality state quantity. : ; in, This is the real-time DC bus voltage, in units of... It is obtained by the bus sampling circuit via ADC; Rated DC bus voltage, in units of For the factory-calibrated parameters of the equipment (e.g.) ); Real-time power grid frequency, in units of Calculated by the PLL module; Rated grid frequency, unit: (For example ); and The coefficients are dimensionless weighted coefficients, satisfying The equipment is calibrated based on its sensitivity to bus voltage and frequency. Dimensional analysis shows... for , for All of them are dimensionless quantities, therefore It is a dimensionless state quantity. In a set of rated bus voltages... Rated frequency is In the equipment, if real-time detection , ,Pick , ,but , , thus calculate This value can intuitively reflect the degree of deviation of the current power grid state from the rated operating condition, and it decreases as the bus voltage drops or the frequency deviation increases.

[0020] In obtaining Next, the status of the electrically driven heat pump system was further constructed. The selection of system status is directly related to two quantities: the current operating frequency of the compressor and electrical safety and load-bearing capacity. With the compressor's current load current .in The unit is The frequency is read in real time from the inverter's output frequency register; The unit is The current is obtained by the motor phase current sampling module. The current sampling is performed by detecting the three-phase current through Hall sensors or sampling resistors, and the effective value is calculated by the internal algorithm of the control chip. Indicates the mechanical operating range of the compressor. The current electrical load intensity and the system state together reflect the current load-bearing capacity of the system under the current power grid conditions. Therefore, the system state is defined as follows: For example, when , hour, This represents the set of states at that operating point. Finally, this step outputs the power grid quality state parameters. Status of electrically driven heat pump system This provides a sufficient input basis for determining the feasible region of subsequent control behaviors based on power grid quality.

[0021] S2: Determine the feasible domain of control behavior based on the power grid quality state quantity and the system state. The feasible domain of control behavior includes the compressor frequency increase allowable amplitude, the PWM modulation configuration set, and the upper bound of the auxiliary machine coordination amplitude. Specifically, this step receives the power quality state quantity output from step one. With system status Based on this, the feasible domain of heat pump variable frequency control behavior is generated. This feasible region is used to limit the upper bound of the compressor's frequency ramp-up behavior and to provide the set of PWM modulation configurations allowed under the current grid quality, as well as the upper bound of the auxiliary machine's coordination amplitude. This ensures that subsequent steps, when generating a coordinated adjustment scheme, always remain within the control space that is "allowed by both the grid's carrying capacity and the current load intensity." The engineering definition of the "feasible region" here is: the set of acceptable control behaviors and their boundary parameters for the controller within a control cycle. The boundary parameters can be directly written into the inverter's control register or scheduling module, thus ensuring verifiability during field commissioning.

[0022] The core quantification of the feasible region originates from the classic concept of "normalized margin," which initially stems from the expression of safe operating boundaries in control engineering and power electronic systems. On one hand, using the rated upper limit as a reference, real-time quantities are normalized to obtain dimensionless proportions; on the other hand, "remaining margin" characterizes the controllable space beyond the upper limit. In the scenario of this application, the most direct representation of the compressor load intensity is the effective value of the current. Its upper limit The upper limit of the long-term safe operating current for power devices and compressors (factory-fixed, unit) The grid carrying capacity is formed by step one. Characterized (dimensionless). Therefore, with current margin... Basic terms, and using This margin is scaled to obtain an "available margin" that automatically shrinks when the power grid deteriorates. Considering the unique characteristics of electrically driven heat pumps, the compressor is more sensitive to bus voltage margin when operating in the high-frequency range. Simultaneously, the high-frequency, high-current combined operating conditions are more likely to trigger switching losses and thermal stress accumulation in power devices. Therefore, a "high-frequency, high-current coupling suppression term" is introduced on top of the aforementioned basic terms. The initial construction of this suppression term is derived from a product-type penalty in mathematics (similar to the idea of ​​a regularization term). It reflects the situation where "two risk factors are simultaneously high" through the product of two normalized quantities, and adjusts the suppression strength with a calibration coefficient to form a comprehensive margin. : ; in, The power grid quality state quantity (dimensionless) is output from step one. The effective value of the compressor current (unit) The three-phase current is obtained by the motor phase current sampling module and the effective value is calculated in the controller. The factory-set safe current limit (unit) ); The current operating frequency of the compressor (unit) (), read from the inverter output frequency register; The maximum allowable frequency (in units) of the compressor is factory-preset. ); This is a dimensionless calibration coefficient used to characterize the strength of the feasible region contraction under high-frequency, high-current combined operating conditions. The dimension check is as follows: for Dimensionless for Dimensionless Since it is dimensionless, both terms on the right-hand side are also dimensionless. It is a dimensionless quantity, consistent with the left side. The logical relationship of this formula is: the first term gives the "proportional margin for increasing the load" under the current power grid quality, and the second term further compresses this margin when the compressor is at high frequency and the current is high, so that the feasible region is closer to the actual risk distribution of the electric-driven heat pump under poor power grid conditions.

[0023] After obtaining the overall margin Then, it is combined with the remaining frequency space. By combining these factors, the allowable frequency increase range of the compressor can be obtained. This transforms the dimensionless margin into boundary parameters that can be directly written into the target frequency planner. The initial source of this mapping is the "proportional step size based on remaining space" concept in control engineering: the step size naturally decreases as the upper limit approaches, and becomes relatively larger as the margin increases. Therefore, we obtain: ; in, The unit is ; It is a dimensionless factor, ensuring that the allowable up-frequency amplitude is not negative; The unit is Dimensionality check: Dimensionless multiplied by get Consistent with the left side. The derivation relationship between the two equations is as follows: First, the first equation compresses the power grid quality and load status into a comprehensive margin. Then, the second formula will... Mapped to specific up-frequency boundaries This creates a hard constraint on "up-frequency behavior" within the feasible region.

[0024] A set of reproducible computational examples are given to illustrate the operability of the algorithm. Suppose the factory-fixed parameters of a certain electrically driven heat pump device are... , Step one outputs during a certain control cycle. and provide the system status. ,in , Calibration coefficients are taken as follows: First, calculate the normalized current ratio. Current margin ; Calculate the normalized frequency ratio Substituting into the first equation, we get the first term. The second item ,therefore Then calculate the remaining frequency space. Substituting into the second equation, we get The resulting feasible region Clear boundaries are given regarding "compressor frequency increase behavior": frequency increase is permitted, but the allowable range does not exceed approximately [missing information]. "Compressor frequency reduction behavior" is permitted within the feasible region to reduce load; the PWM modulation configuration set is determined by a preset configuration table within the controller. Select and write the interval. Configuration index or bitmask; upper bound of auxiliary machine coordination amplitude and Linkage settings and writing This ensures that the auxiliary equipment adjustments align with the compressor's potential frequency increase range. Ultimately, this step defines the feasible domain for output control behavior. Its interior contains at least The PWM configuration allows the set index (or bitmask) and the upper bound of the auxiliary coordinating amplitude to be directly used as boundary parameters for subsequent scheme generation.

[0025] S3: Generate a collaborative adjustment scheme based on the feasible domain of the control behavior. The collaborative adjustment scheme includes the compressor target frequency, the target PWM modulation configuration, and the auxiliary machine target command. Specifically, the feasible domain for controlling behavior has been obtained in step two. Under the premise that this step is based on The boundary parameters given in the text generate a collaborative mediation scheme. . The compressor frequency upscaling range has already been included. Parameters such as the PWM configuration allowable set index and the upper bound of the auxiliary machine coordination amplitude are essentially quantitative expressions of the "space of mechanical and electrical loads that can be increased under the current power grid quality and load conditions." To generate specific adjustment commands within this space, this step uses the "saturated proportional mapping" principle, commonly used in control engineering, as the basic model. The original form of this principle comes from the combination of proportional control and a saturation function, i.e., within the allowable range... We construct a mapping function that is monotonically increasing, automatically shrinks in small intervals, and approaches its upper limit in large intervals. The original linear mapping form is... This form, when the allowable space is small, still directly approaches the boundary, which is not conducive to a smooth transition under harsh power grid conditions. Therefore, an improved form is formed by introducing a "scale normalization denominator term" based on the proportional mapping. The derivation idea is: to increase the allowable amplitude... With a fixed scale parameter Normalization is performed so that when The output automatically decreases when it approaches zero, and when... When the scale is much larger than this, it approaches the upper linear bound. This leads to the target frequency generator formula: ; in, The current compressor operating frequency, in units It is read from the inverter's output frequency register; The upsampling allowable amplitude output from step two, in units of... ; These are factory-preset frequency scale parameters, in units of... , usually take This is used to define the "small margin contraction range under severe power grid conditions". Dimensional verification is as follows: for Dimensionless, multiplied by Later still ,and Adding them together gives The dimensions are consistent. Mathematically, this formula is equivalent to resizing the linear increment. Apply a weight function that varies with itself. This function originates from the standard family of bounded increasing functions. hour ,exist hour Therefore, it ensures that the output is naturally compressed under small margin conditions and approaches the upper limit of linearity under large margin conditions.

[0026] In terms of computational logic, this step first starts from... Read from Then read the current frequency. Continuing the calculation using the example given in step two: when , ,Pick Then the scaling factor is The frequency upsampling increment is ,therefore If the power grid deteriorates further, leading to... Reduced to Then the scaling factor is The frequency upsampling increment is The target frequency is approximately This indicates that the adjustment range is automatically reduced within a small margin range, reflecting the smooth adjustment characteristics under adverse power grid conditions.

[0027] In determining After that, this step needs to be done A specific configuration can be selected from the allowed set of PWM configurations. The PWM configuration set is stored in the controller as a structure array, with each element containing parameters such as carrier frequency, upper limit of modulation depth, and dead time. To ensure that the PWM selection is consistent with the frequency regulation intensity, a dimensionless regulation ratio is introduced. ; Wherein, the numerator is the actual frequency increment used (unit: ). The denominator is the sum of the allowable space and scale parameters (units). ),therefore Dimensionless. This ratio is derived from the normalized increment expression in proportional control and is used to characterize "how much allowable space was actually used in this period". In the calculation example above, the numerator is... The denominator is ,get The controller, according to Select the corresponding configuration from the allowed set, for example when When choosing a conservative PWM configuration, When choosing medium configuration, The standard configuration is selected at this time. This selection process is completed by reading the array index and writing it to the PWM module register, which can be verified by checking the register value during debugging.

[0028] The auxiliary machine coordination target is generated using the same proportion as the compressor. If step two provides an upper bound for the auxiliary machine's cooperative amplitude... (Dimensionless ratio), the current auxiliary machine speed ratio is The target ratio This expression is consistent with the compressor target frequency generation logic, ensuring that the mechanical load and heat exchange capacity of the electrically driven heat pump change synchronously under harsh power grid conditions. , For example, the auxiliary machine increment is This means increasing the speed ratio by approximately 1.24% from the current ratio. This result can be verified by reading the auxiliary engine control register. During actual debugging, different... and Calculated under the conditions , Configure the PWM index and observe bus voltage fluctuations and current peak changes.

[0029] S4: Update the inverter output frequency and auxiliary machine drive command according to the restricted slope execution trajectory to execute the aforementioned coordinated adjustment scheme.

[0030] Specifically, this step receives the collaborative mediation scheme output from step three. And it is implemented in the electric drive heat pump inverter system, so that the compressor frequency, PWM modulation configuration and auxiliary machine speed command take effect on the field equipment according to a verifiable trajectory. It exists as an internal structure of the controller and contains at least the compressor target frequency. (unit ), PWM configuration index (corresponding to preset modulation parameter table entries) and auxiliary machine target speed ratio (Dimensionless). The engineering constraints of the execution layer originate from the classical principles of power electronics and motion control: imposing a rate-of-change limit on the reference command can reduce current spikes and bus energy impacts. This concept corresponds to "acceleration limit / slope limit" in servo control and "frequency setpoint slope limit" in inverter control. Its basic form is to limit the desired change within the maximum allowable range of change per unit time, thereby obtaining a smooth and controllable reference trajectory. Based on this classical form, this application applies it to the implementation of a coordinated adjustment scheme, enabling the compressor frequency and auxiliary machine frequency to be updated with the same control cycle and verified by reading from a register.

[0031] The compressor frequency is set at the current output frequency of the inverter. Starting from this point, the execution frequency for the next cycle is updated within each control cycle. Let the maximum permissible frequency change rate be... (unit (Originally obtained from the overall machine calibration and stored in the controller's non-volatile memory), the control cycle is... (unit Determined by the controller timer, for example Then the allowable frequency change per cycle is: (unit To achieve this constraint, a saturation function from control engineering (initialized from the amplitude limiting operator in classical nonlinear control) is used to trim the frequency error, resulting in: ; in, For collaborative mediation scheme The target frequency (unit) is given. ), Current actual output frequency (unit) (Read from the inverter output frequency register) Maximum rate of change of frequency (units) ), For control cycle (unit) ), Will Limited to Internal. Dimensional check is as follows: for ; for ; Saturated output is ,and Adding them together gives The dimensions on both sides are consistent and conform to the physical principle of frequency updates. The relationship between this equation and step three is: the output of step three... The goal is to transform the execution layer into a cycle-by-cycle executable trajectory using this formula. The endpoint of the trajectory converges to [a certain value] during multiple iterations. .

[0032] A computational example is provided to demonstrate operability. Assume step three outputs... Current register read Calibration Control cycle Then the allowable change in a single period is The frequency error is After saturation cutting, the result is ,therefore If the next cycle updates with the same parameters, and Refresh The error is It was still cut into ,get After approximately eight cycles of this progression, Approaching The error in the last cycle is less than The time-saturation function directly outputs the actual error, achieving overshoot-free convergence. This process can be verified by continuously reading the output frequency register through the debug port, and the peak suppression effect can be observed by simultaneously recording the current sampling values.

[0033] The execution of PWM modulation configuration is completed according to the PWM configuration index output in step three. The controller program's hard drive stores the PWM parameter table, with each entry containing the carrier frequency (unit: ...). ), modulation depth upper limit (dimensionless) and dead time (units) Fields such as ) are included. During execution, the controller accesses the array according to the index and writes the corresponding values ​​to the carrier frequency register, modulation depth limit register, and dead zone register according to the register address, so that the PWM modulation configuration takes effect within the same control cycle. This process can be verified by measuring the changes in carrier frequency and pulse width with an oscilloscope, and the PWM register readback value can be read as a debugging record in the debugging software.

[0034] The auxiliary machine executes the target speed ratio output in step three. As input, convert it to the auxiliary machine's target frequency and update it using the same slope constraint. Let the auxiliary machine's rated frequency be... (unit (If the auxiliary machine driver's rated parameters are fixed and stored), then the auxiliary machine's target frequency is... The controller reads the current actual frequency of the auxiliary machine. (unit (Obtained from the auxiliary machine drive feedback register or communication message), then the auxiliary machine frequency instruction for the next cycle is updated using a slope limiting operator of the same shape as the compressor and written to the auxiliary machine driver via PWM output or serial communication. Because The generation of the auxiliary machine frequency is linked to the compressor's adjustment intensity. The smooth progression of the auxiliary machine frequency is consistent with the smooth progression of the compressor frequency on the time scale, thereby reducing the superposition of load sudden changes under harsh power grid conditions.

[0035] To provide verifiable evidence of effectiveness, records can be made during on-site debugging. (Bus voltage sampling channel) (Motor current RMS value) vs. output frequency register variation curve. Taking the primary voltage sag condition as an example, under the same... Performing slope limit updates in the next step is a better approach than directly writing the output frequency register in one step. In this way, the lowest point of the bus voltage is determined by Move up The peak compressor current is from Down to Simultaneously, the output frequency curve exhibits a linear progression and converges to the target frequency within approximately 0.08 seconds, demonstrating the execution layer's suppression effect on transient impacts. Finally, this step, in the form of an execution trajectory, enables... The target frequency, PWM configuration, and auxiliary machine instructions in the system actually take effect on the electric drive heat pump inverter system. The debugging record can be formed by register readback, oscilloscope waveform, and sampling curve, thus completing the actual execution closed loop of the collaborative adjustment scheme.

[0036] In one or more embodiments, such as Figure 2 As shown, an intelligent collaborative control system for an electrically driven heat pump designed for harsh power grid conditions is disclosed. The system includes: The data acquisition and status construction module is used to acquire DC bus voltage, grid frequency, compressor operating frequency and compressor current, calculate grid quality status based on the DC bus voltage and grid frequency, and construct system status based on the compressor operating frequency and compressor current. The feasible region determination module is used to determine the feasible region of control behavior based on the power grid quality state quantity and the system state. The feasible region of control behavior includes the compressor frequency increase allowable amplitude, the PWM modulation configuration set, and the upper bound of the auxiliary machine coordination amplitude. The scheme generation module is used to generate a coordinated adjustment scheme based on the feasible domain of the control behavior. The coordinated adjustment scheme includes the compressor target frequency, the target PWM modulation configuration, and the auxiliary machine target command. The trajectory execution control module is used to update the inverter output frequency and auxiliary machine drive commands according to the restricted slope in order to execute the aforementioned coordinated adjustment scheme.

[0037] It is worth noting that the specific workflow of the intelligent collaborative control system for electric-driven heat pumps under harsh power grid conditions provided in this embodiment of the invention is the same as that of the intelligent collaborative control method for electric-driven heat pumps under harsh power grid conditions described in the above embodiments, and will not be repeated here.

[0038] This invention also provides an intelligent collaborative control device for electrically driven heat pumps under harsh power grid conditions, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps described in the above embodiments of the intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions, for example... Figure 1 The steps S1 to S4 described above; or, when the processor executes the computer program, it implements the functions of each module in the above system embodiments.

[0039] For example, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the intelligent collaborative control device for electrically driven heat pumps oriented towards harsh power grid conditions.

[0040] The intelligent collaborative control device for electric-driven heat pumps under harsh power grid conditions can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the device may also include input / output devices, network access devices, and buses.

[0041] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the intelligent collaborative control device for electric-driven heat pumps under harsh power grid conditions, connecting all parts of the device via various interfaces and lines.

[0042] The memory can be used to store the computer program and / or modules. The processor, by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the intelligent collaborative control device for electrically driven heat pumps under harsh power grid conditions. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created according to the operation of the controller, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0043] The integrated modules of the intelligent collaborative control equipment for electrically driven heat pumps under harsh power grid conditions, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0044] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0045] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for intelligent collaborative control of electrically driven heat pumps oriented to harsh grid conditions, characterized by, The method includes: Obtain DC bus voltage, grid frequency, compressor operating frequency, and compressor current; Calculate the power grid quality status quantity based on the DC bus voltage and the power grid frequency, and construct the system status based on the compressor operating frequency and the compressor current; The feasible region of control behavior is determined based on the power grid quality status quantity and the system status. The feasible region of control behavior includes the allowable range of compressor frequency increase, the PWM modulation configuration set, and the upper bound of the auxiliary machine coordination range. A collaborative adjustment scheme is generated based on the feasible domain of the control behavior. The collaborative adjustment scheme includes the compressor target frequency, the target PWM modulation configuration, and the auxiliary machine target command. The inverter output frequency and auxiliary machine drive commands are updated according to the restricted slope execution trajectory to execute the aforementioned coordinated adjustment scheme.

2. The intelligent collaborative control method for electrically driven heat pump oriented to severe grid conditions according to claim 1, characterized in that, The calculation process for the power grid quality state parameters includes: Calculate the first ratio of the real-time DC bus voltage to the rated DC bus voltage; Calculate the second ratio of the real-time grid frequency to the rated grid frequency; The power grid quality state quantity is obtained by multiplying the first weighting coefficient by the first ratio and adding the second weighting coefficient by the second ratio. Wherein, the sum of the first weighting coefficient and the second weighting coefficient is 1.

3. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 1, characterized in that, The process of determining the allowable compressor frequency increase range within the feasible domain of control behavior includes: Calculate the third ratio of the compressor current to the upper limit of the safe current to obtain the current margin. Calculate the fourth ratio of the compressor's operating frequency to the compressor's maximum permissible frequency; Multiply the power grid quality status quantity by the current residual margin to obtain the basic margin term; Multiplying the fourth ratio by the third ratio and then by the calibration coefficient yields the high-frequency high-current coupling suppression term; The comprehensive margin is obtained by subtracting the high-frequency, high-current coupling suppression term from the basic margin term; The compressor's frequency upscaling allowance is obtained by multiplying the overall margin by the remaining frequency space.

4. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 1, characterized in that, The process of generating the compressor target frequency in the coordinated mediation scheme includes: Read the current compressor operating frequency and the compressor's allowed frequency increase range; The scaling factor is obtained by dividing the allowable increase in compressor frequency by the sum of the frequency scale parameter and the allowable increase in compressor frequency. Multiply the scaling factor by the compressor's allowed frequency increase to obtain the frequency increment; The frequency increment is added to the current compressor operating frequency to obtain the compressor target frequency.

5. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 4, characterized in that, The process of generating the target PWM modulation configuration in the collaborative mediation scheme includes: The fifth ratio of the actual frequency increment to the total allowable space is calculated to obtain the dimensionless adjustment ratio; Based on the range of the dimensionless adjustment ratio, select the corresponding carrier frequency, modulation depth upper limit and dead time from the preset PWM parameter table; The selected carrier frequency, upper limit of modulation depth, and dead time are used as the target PWM modulation configuration.

6. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 1, characterized in that, The process of generating auxiliary target instructions in the collaborative mediation scheme includes: Read the current speed ratio of the auxiliary machine and the upper limit of the auxiliary machine's cooperative amplitude; The auxiliary machine increment is calculated using the same scaling factor as that used to generate the target frequency of the compressor; The auxiliary machine increment is added to the current speed ratio of the auxiliary machine to obtain the target speed ratio of the auxiliary machine; The auxiliary machine target speed ratio is converted into the auxiliary machine target frequency as the auxiliary machine target command.

7. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 1, characterized in that, The process of updating the inverter output frequency according to the trajectory executed according to the constrained slope includes: Calculate the frequency error between the compressor's target frequency and the current actual output frequency; Calculate the product of the maximum allowable rate of frequency change and the control period to obtain the allowable frequency change per period; If the absolute value of the frequency error is greater than the allowable frequency change in a single cycle, then the execution frequency of the next cycle is set to the current actual output frequency plus or minus the allowable frequency change in a single cycle. If the absolute value of the frequency error is less than or equal to the allowable frequency change per cycle, then the execution frequency of the next cycle is set to the compressor target frequency.

8. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 1, characterized in that, The process of updating the auxiliary machine drive instructions according to the restricted slope execution trajectory includes: Convert the auxiliary machine target instruction into the auxiliary machine target frequency; Calculate the auxiliary machine frequency error between the target frequency of the auxiliary machine and the current actual frequency of the auxiliary machine; The frequency error of the auxiliary machine is saturated and clipped using the single-cycle allowable frequency change amount that is the same as the output frequency of the updated frequency converter. The auxiliary machine frequency instruction for the next cycle is updated based on the trimmed error value and written to the auxiliary machine driver.

9. The intelligent collaborative control method for electrically driven heat pumps under harsh power grid conditions according to claim 1, characterized in that, The method triggers an emergency frequency reduction protection mode when it detects that the DC bus voltage drop exceeds a preset threshold. In the emergency frequency reduction protection mode, the allowable frequency increase range of the compressor is forcibly set to zero; Switch the target PWM modulation configuration to a preset combination of low-voltage crossover carrier frequency and maximum modulation depth; The auxiliary machine target command is locked to the lowest speed frequency corresponding to maintaining the minimum circulation flow of the system; The emergency frequency reduction protection mode can only be exited and normal coordinated control based on the power grid quality status can only be restored when the DC bus voltage recovers to above the preset recovery threshold and the duration reaches the preset stable duration.

10. An intelligent collaborative control system for electrically driven heat pumps under harsh power grid conditions, characterized in that: The system includes: The data acquisition and status construction module is used to acquire DC bus voltage, grid frequency, compressor operating frequency and compressor current, calculate grid quality status based on the DC bus voltage and grid frequency, and construct system status based on the compressor operating frequency and compressor current. The feasible region determination module is used to determine the feasible region of control behavior based on the power grid quality state quantity and the system state. The feasible region of control behavior includes the compressor frequency increase allowable amplitude, the PWM modulation configuration set, and the upper bound of the auxiliary machine coordination amplitude. The scheme generation module is used to generate a coordinated adjustment scheme based on the feasible domain of the control behavior. The coordinated adjustment scheme includes the compressor target frequency, the target PWM modulation configuration, and the auxiliary machine target command. The trajectory execution control module is used to update the inverter output frequency and auxiliary machine drive commands according to the restricted slope in order to execute the aforementioned coordinated adjustment scheme.

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