Multi-region hierarchical PID parameter adaptive adjustment method and device

Abstract

The invention provides a multi-region hierarchical PID parameter adaptive adjustment method and device, and the method comprises the steps: collecting the load deviation of a generator set in real time, and comparing the load deviation with a preset first threshold value and a preset second threshold value (the first threshold value is greater than the second threshold value); when the load deviation absolute value is larger than a first threshold value, entering an emergency adjusting area, when the load deviation absolute value is larger than a second threshold value and smaller than or equal to the first threshold value, entering a rapid adjusting area, and when the load deviation absolute value is smaller than or equal to the second threshold value, entering a high-precision adjusting area. And the proportional gain is dynamically adjusted according to the current adjusting area and the load deviation, and the output control pulse width is calculated by combining the adjusted proportional gain, the load deviation, the control period and the maximum output of the unit. In the high-precision adjusting area, the control period is adjusted according to the output control pulse width and the preset period growth coefficient, and precise control over the generator set is achieved. The adaptability, the stability and the adjusting precision of the control system can be improved, and the operation performance of the generator set is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control of generator sets, and more specifically, to a multi-region hierarchical PID parameter adaptive adjustment method and device. Background Art

[0002] In the control systems of modern generator sets, automatic generation control (AGC) technology is widely used to achieve stable operation and optimized scheduling of power systems. Traditional AGC control systems typically use PID (proportional-integral-derivative) controllers to adjust the output of the generator set to meet the changing demands of the grid load. However, due to the complex and changeable dynamic characteristics of the grid load, the parameters of traditional PID controllers are often difficult to adapt to the regulation requirements under different operating conditions. This results in insufficient response speed when the load changes are large, and difficulty in achieving high-precision regulation when the load changes are small. In addition, the parameters of traditional PID controllers are usually fixed and cannot be dynamically adjusted according to real-time load deviations and system status. This, to a certain extent, limits the control performance and regulation accuracy of the generator set.

[0003] While existing PID parameter adjustment methods can improve control effectiveness to a certain extent, most employ a single adjustment strategy and are unable to effectively address the complex adjustment requirements across multiple regions and operating conditions. For example, large load deviations require a rapid response to stabilize the system, while smaller load deviations require high-precision adjustment to ensure stable operation of the generator set. Furthermore, existing PID parameter adjustment methods often lack systematicity and adaptability, and are unable to optimize and adjust based on real-time assessment scores and historical data, making it difficult to further improve control system performance.

[0004] In the process of implementing the embodiments of the present invention, there are at least the following problems or defects in the existing technology: the existing PID parameter adjustment method cannot effectively adapt to the complex adjustment requirements under multiple regions and multiple working conditions, lacks dynamic adjustment capabilities and adaptability, and is difficult to meet the requirements of rapid response and high-precision adjustment at the same time, and cannot be optimized and adjusted according to real-time assessment scores and historical data, which limits the performance improvement of the generator set control system. Summary of the Invention

[0005] The present invention provides a multi-region hierarchical PID parameter adaptive adjustment method and device.

[0006] In a first aspect of the present invention, a multi-region hierarchical PID parameter adaptive adjustment method is provided, comprising: Real-time collection of current load deviation of generator sets; Comparing the absolute value of the load deviation with a first preset threshold and a second preset threshold, wherein the first preset threshold is greater than the second preset threshold and both are positive numbers; Determine the current adjustment area based on the comparison result: when the absolute value of the load deviation is greater than the first preset threshold, enter the emergency adjustment area; when the absolute value of the load deviation is greater than the second preset threshold and less than or equal to the first preset threshold, enter the fast adjustment area; when the absolute value of the load deviation is less than or equal to the second preset threshold, enter the high-precision adjustment area; Dynamically adjust the proportional gain based on the current regulation area and load deviation; Calculate the output control pulse width based on the adjusted proportional gain, load deviation, control cycle and maximum output of the unit; When in the high-precision adjustment area, the control period is adjusted according to the output control pulse width and the preset period growth coefficient.

[0007] Furthermore, the step of dynamically adjusting the proportional gain specifically includes: Get the current load deviation value; An exponential response term is calculated based on the load deviation value, which approaches 1 when the load deviation increases and approaches 0 when the load deviation decreases; Interpolation calculation is performed between the preset minimum proportional gain and maximum proportional gain based on the exponential response term; The final proportional gain is determined by the following formula:

[0008] in is the load deviation, is the minimum proportional gain preset value, is the preset value of the maximum proportional gain, and α is the preset value of the exponential adjustment coefficient.

[0009] Furthermore, the step of calculating the output control pulse width specifically includes: Obtain the current proportional gain value, load deviation value, control period value and maximum output value of the unit; Multiply the proportional gain and the load deviation to obtain the preliminary control amount; Multiply the initial control amount by the control period and divide it by the maximum output of the unit to obtain the standardized pulse width output; The specific calculation formula is:

[0010] in To control the output pulse width, is the proportional gain, is the load deviation, T is the control period, The maximum output of the unit.

[0011] Furthermore, the step of adjusting the control period specifically includes: When in the high-precision adjustment area, the collaborative working mode of repetitive control and proportional-integral-differential control is enabled; Get the currently calculated output control pulse width value; Multiply the output control pulse width by the preset cycle growth coefficient to obtain the adjusted control cycle; The specific adjustment formula is:

[0012] in is the output control pulse width, C is the preset cycle growth coefficient, and the value range is 2.0 to 3.0.

[0013] Furthermore, the method further includes the step of dynamically adjusting the integration time: Get the current load deviation value; Select the corresponding integral time adjustment strategy according to the current adjustment area; In the emergency adjustment area, the strategy of reducing the integral time is adopted to accelerate the system response; In the high-precision adjustment area, the strategy of increasing the integral time is adopted to improve the steady-state accuracy; The integration time is calculated using a preset area adaptation function:

[0014] in is the integration time, It is the preset value of the base integration time. is the integral time dynamic function.

[0015] Furthermore, the method further includes the step of dynamically adjusting the differential time: Get the current load deviation value; In the fast adjustment area, the derivative effect is enhanced to suppress the system overshoot; The derivative time is calculated using the preset slope prediction function:

[0016] in is the differential time, is the preset value of the base differential time, is the differential time dynamic function.

[0017] Furthermore, the method further includes the step of dynamically adjusting the differential gain: Obtain the load deviation value at the current sampling moment and the load deviation value at the previous sampling moment; Calculate the load deviation change between two consecutive sampling points as the error change slope; Adjust the differential gain according to the positive and negative sign and size of the error change slope; The specific calculation and adjustment process is as follows:

[0018]

[0019] Where ΔP[k] is the load deviation at the current sampling moment, ΔP[k-1] is the load deviation at the previous sampling moment, is the differential gain, is the preset value of the reference differential gain, and η is the preset value of the slope response coefficient.

[0020] Furthermore, it also includes the steps of optimizing multi-cycle assessment feedback: Obtain historical scoring data for multiple cycles from the automatic power generation control assessment platform; Calculate the weighted average of recent assessment scores as the score trend value; Proportionally adjust the minimum proportional gain reference value and the maximum proportional gain reference value according to the score trend value; The specific update formula is:

[0021]

[0022] in is the updated minimum proportional gain, is the updated maximum proportional gain, β and γ are the minimum / maximum gain adjustment coefficients respectively, It is the weighted average of recent assessment scores.

[0023] Furthermore, the method further includes the step of regional transition smoothing: When a regulation area switching event is detected, the current area boundary threshold is obtained; Calculate the relative distance between the current load deviation and the area boundary threshold; An exponential transition factor is calculated based on the relative distance, which is close to 1 when far from the boundary and close to 0 when close to the boundary; Use transition factors to perform weighted fusion on proportional gains to achieve smooth parameter transition; The specific calculation process is:

[0024]

[0025] Where φ is the transition factor, is the current region boundary threshold, is the smoothing coefficient, is the proportional gain during the transition period, is the target gain for the new interval, is the original interval gain.

[0026] In a second aspect of the present invention, a multi-region hierarchical PID parameter adaptive adjustment device is provided, comprising: Load detection module, used to collect the current load deviation of the generator set in real time; a region discrimination module, configured to compare the absolute value of the load deviation with a first preset threshold and a second preset threshold, and determine a current adjustment region based on the comparison result: when the absolute value of the load deviation is greater than the first preset threshold, the current adjustment region is entered; when the absolute value of the load deviation is greater than the second preset threshold and less than or equal to the first preset threshold, the current adjustment region is entered; and when the absolute value of the load deviation is less than or equal to the second preset threshold, the current adjustment region is entered; Parameter adjustment module, used to dynamically adjust the proportional gain based on the current regulation area and load deviation; Pulse width calculation module, used to calculate the output control pulse width based on the adjusted proportional gain, load deviation, control period and maximum output of the unit; A cycle control module, used to adjust the control cycle according to the output control pulse width and a preset cycle growth coefficient when in the high-precision adjustment area; The execution output module is used to output the calculated control pulse width to the generator set actuator.

[0027] The above embodiments of the present invention have at least the following beneficial effects: 1. By setting up a multi-zone hierarchical regulation strategy, dynamically dividing the regulation zones and adjusting the PID parameters based on the size of the load deviation, this effectively solves the problem of traditional PID controllers struggling to achieve both fast response and high-precision regulation using a single parameter. The emergency regulation zone enables rapid response to large load deviations, the rapid regulation zone suppresses overshoot and accelerates system stability, and the high-precision regulation zone achieves high-precision control, significantly improving the adaptability and control performance of the generator set under different operating conditions.

[0028] 2. Dynamically adjust parameters such as proportional gain, integral time, and derivative time. Combined with methods such as exponential response terms, regional adaptive functions, and slope prediction functions, this achieves adaptive optimization of PID parameters. This dynamic adjustment mechanism flexibly adjusts control parameters based on real-time load deviations and system status, avoiding the regulation limitations of fixed parameters and further improving the stability and accuracy of the control system.

[0029] 3. A multi-cycle assessment feedback optimization mechanism was introduced. By analyzing historical assessment score data, the baseline values ​​of the PID parameters were dynamically adjusted. This solved the existing problem of PID parameters being unable to be optimized based on the long-term performance of the system. This assessment feedback-based optimization method can continuously improve the performance of the control system, making it better adapted to grid operation requirements, while also enhancing the system's self-learning and self-adaptive capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which: Figure 1 A schematic flow chart of a multi-region hierarchical PID parameter adaptive adjustment method provided in one embodiment of the present invention; Figure 2 A schematic diagram of the structure of a multi-region hierarchical PID parameter adaptive adjustment device provided by an embodiment of the present invention; Figure 3 The figure schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions of this application will be described clearly and completely below, in conjunction with the accompanying drawings. It should be understood that the described embodiments represent only a portion of the embodiments of this application, and not all of them. The components of this application, generally described and illustrated in the drawings herein, may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but rather merely represents selected embodiments of this application. All other embodiments derived by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application. It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. Furthermore, in the description of this application, the terms "first," "second," etc., are used solely to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0032] like Figure 1As shown, the present application proposes a multi-region hierarchical PID parameter adaptive adjustment method, comprising the following steps: S1, real-time collection of the current load deviation of the generator set; S2, comparing the absolute value of the load deviation with a first preset threshold and a second preset threshold, wherein the first preset threshold is greater than the second preset threshold and both are positive numbers; S3, determining the current adjustment area according to the comparison result: when the absolute value of the load deviation is greater than the first preset threshold, entering the emergency adjustment area; when the absolute value of the load deviation is greater than the second preset threshold and less than or equal to the first preset threshold, entering the fast adjustment area; when the absolute value of the load deviation is less than or equal to the second preset threshold, entering the high-precision adjustment area; S4, dynamically adjusting the proportional gain based on the current adjustment area and load deviation; S5, calculating the output control pulse width according to the adjusted proportional gain, load deviation, control period and maximum output of the unit; S6, when in the high-precision adjustment area, adjusting the control period according to the output control pulse width and the preset period growth coefficient.

[0033] Real-time acquisition of the generator set's current load deviation refers to continuously monitoring the difference between the generator set's actual output and its target output. This can be achieved using high-precision sensors or data acquisition systems. This allows for timely detection of system state changes, providing a data basis for subsequent adjustments. Comparing the absolute value of the load deviation with a first and second preset thresholds involves setting two judgment boundaries at different magnitudes. This can be achieved using a numerical comparator or a logical judgment algorithm. This divides the system's operating state into different regulation zones to match the appropriate control strategy. Determining the current regulation zone involves selecting the corresponding control mode based on the magnitude of the deviation. This can be achieved using a threshold trigger mechanism. This prioritizes response speed for large deviations and control accuracy for small deviations, achieving adaptability to multiple operating conditions. Dynamically adjusting the proportional gain refers to changing the proportional coefficient based on the real-time deviation and regulation zone. This can be achieved using an exponential function interpolation algorithm. This enhances the system's regulation capability across different deviation ranges through adaptive gain. Calculating the output control pulse width combines the proportional gain, deviation, time parameters, and unit capacity to generate an execution instruction. This can be achieved using a product normalization formula. This converts the control variable into an executable pulse width instruction. Adjusting the control cycle refers to changing the update frequency of the control instructions in the high-precision area. It can be implemented by using an algorithm that multiplies the pulse width by the growth coefficient. Its function is to extend the adjustment cycle to suppress high-frequency disturbances and improve steady-state control accuracy.

[0034] The core innovation of this application lies in the use of a multi-region hierarchical regulation strategy, combined with a dynamic proportional gain adjustment and control cycle optimization mechanism, to adopt differentiated control parameters and response modes in different load deviation ranges, effectively balancing the needs of fast response and high-precision control, and solving the contradiction between dynamic performance and steady-state accuracy caused by the fixed traditional PID parameters.

[0035] The working process and principle of the present application are as follows: first, the current load deviation of the generator set is collected in real time. The absolute value of the load deviation is then compared with two preset thresholds, which are the first preset threshold and the second preset threshold, respectively, wherein the first preset threshold is greater than the second preset threshold and both are positive numbers. The current regulation area is determined based on the comparison result: when the absolute value of the load deviation is greater than the first preset threshold, the emergency regulation area is entered; when the absolute value of the load deviation is greater than the second preset threshold and is less than or equal to the first preset threshold, the fast regulation area is entered; when the absolute value of the load deviation is less than or equal to the second preset threshold, the high-precision regulation area is entered. Then, the proportional gain is dynamically adjusted based on the current regulation area and the load deviation. The output control pulse width is calculated based on the adjusted proportional gain, load deviation, control cycle and maximum output of the unit. Finally, when in the high-precision regulation area, the control cycle is adjusted based on the output control pulse width and the preset cycle growth coefficient.

[0036] By dividing load deviations into different regulation zones, appropriate regulation strategies can be adopted for deviations of varying magnitude. In the emergency regulation zone, large deviations require a quick response to stabilize the system, so a larger proportional gain is used. In the fast regulation zone, deviations are relatively small, requiring a balance between fast response and stability. In the high-precision regulation zone, small deviations require fine-tuning to maintain system stability, so a smaller proportional gain is used and the control cycle is dynamically adjusted.

[0037] Dynamically adjusting the proportional gain adaptively changes the controller's response based on the magnitude of the real-time load deviation. When the deviation is large, the proportional gain is increased to speed up the response; when the deviation is small, the proportional gain is reduced to avoid overshoot. This adaptive adjustment maintains excellent control performance under varying operating conditions.

[0038] When calculating the output control pulse width, multiple factors, such as proportional gain, load deviation, control cycle, and maximum unit output, are considered. This allows for more reasonable and accurate control instructions. The control pulse width directly affects the generator set's regulation range. By comprehensively considering these factors, over-regulation or under-regulation can be avoided.

[0039] In the high-precision regulation range, regulation accuracy can be further improved by dynamically adjusting the control cycle based on the output control pulse width and a preset cycle growth factor. When fine adjustments are required, increasing the control cycle allows the actuator sufficient time to complete fine movements, avoiding mechanical wear caused by frequent starts and stops.

[0040] This multi-region hierarchical PID parameter adaptive adjustment method can achieve a balance between fast response and high-precision regulation within different load deviation ranges by dynamically adjusting the proportional gain and control period, thereby improving the overall performance of the automatic power generation control system of the generator set.

[0041] As a preferred embodiment, the solution of this application is specifically implemented as follows: First, the current load deviation of the generator set is collected in real time through a load detection device. The load detection device can be a power sensor or a frequency measurement device, and the sampling period is set to 100 milliseconds.

[0042] Next, the absolute value of the collected load deviation is compared with two pre-set thresholds. The first threshold is set at 5% of the rated load, and the second threshold is set at 1% of the rated load. For example, for a 100MW generator set, the first threshold is 5MW, and the second threshold is 1MW.

[0043] The current regulation area is determined based on the comparison results: when the absolute value of the load deviation is greater than 5MW, it enters the emergency regulation area; when the absolute value of the load deviation is greater than 1MW and less than or equal to 5MW, it enters the fast regulation area; when the absolute value of the load deviation is less than or equal to 1MW, it enters the high-precision regulation area.

[0044] Then, the proportional gain is dynamically adjusted based on the current regulation area and load deviation. In the emergency regulation area, the proportional gain is set to the maximum value. ; In the fast adjustment area, the proportional gain is and In the high-precision adjustment area, the proportional gain is set to the minimum value. .

[0045] The output control pulse width is calculated based on the adjusted proportional gain, load deviation, control cycle and maximum output of the unit. The calculation formula for the control pulse width is: , where W is the control pulse width, is the proportional gain, is the load deviation, T is the control period, The maximum output of the unit.

[0046] Finally, when in the high-precision adjustment area, the control period is adjusted according to the output control pulse width and the preset period growth coefficient. The preset period growth coefficient is set to 2.5, and the adjusted control period T'=W*2.5.

[0047] In some of the above-mentioned schemes of the present application, a method is proposed for determining the regulation area based on the absolute value of the load deviation and dynamically adjusting the proportional gain. However, in the process of dynamically adjusting the proportional gain, if the proportional gain is switched only by simple linear interpolation or a fixed threshold, it is easy to cause a sudden change in the gain value, thereby causing system oscillation or response lag, affecting the smoothness and stability of the regulation process.

[0048] The application further proposes a specific implementation mode of dynamically adjusting the proportional gain, including obtaining a current load deviation value, calculating an exponential response term according to the load deviation value, performing interpolation calculation between a preset minimum proportional gain and a maximum proportional gain based on the exponential response term, and finally determining the proportional gain through a formula.

[0049] In the formula, the calculation of the exponential response term adopts an exponential function form, and a core parameter thereof is an exponential adjustment coefficient, which controls the sensitivity of the response term to the load deviation. In the interpolation calculation process, the minimum proportional gain and the maximum proportional gain are preset as reference parameters, and correspond to a conservative mode and an aggressive mode of system adjustment respectively. The absolute value operation is introduced in the formula, so as to ensure that the positive and negative directions of the load deviation do not affect the amplitude of the gain adjustment.

[0050] Specifically, the absolute value of the current load deviation value is input into the exponential function to generate an exponential response term between 0 and 1. When the load deviation increases, the exponential response term tends to 1, and at this time, the proportional gain is close to the maximum preset value, thereby enhancing the rapid response capability of the system. When the load deviation decreases, the exponential response term tends to 0, and at this time, the proportional gain is close to the minimum preset value, thereby avoiding the over-adjustment phenomenon. The interpolation calculation converts the exponential response term into an actual gain value through linear mapping, for example, when the exponential adjustment coefficient is set to 0.5, the response term value is when the load deviation is 2 units, and at this time, the proportional gain is . This calculation mode makes the gain adjustment process continuous and adaptive, and effectively balances the adjustment requirements in different deviation intervals.

[0051] As a preferred embodiment, the scheme of the application is implemented as follows: The step of dynamically adjusting the proportional gain includes: Obtaining a current load deviation value. For example, by collecting the system in real time to obtain the difference between the current load value and the target load value of the generator set, as the current load deviation value.

[0052] Calculating an exponential response term according to the load deviation value. The response term tends to 1 when the load deviation increases, and tends to 0 when the load deviation decreases. Specifically, an exponential function may be used for calculation, where α is a preset exponential adjustment coefficient, and ΔP is the load deviation value.

[0053] Performing interpolation calculation between a preset minimum proportional gain and a maximum proportional gain based on the exponential response term. The calculated exponential response term can be used as an interpolation coefficient to perform linear interpolation between the minimum proportional gain and the maximum proportional gain .

[0054] The final proportional gain is determined by the following formula:

[0055] in is the load deviation, is the minimum proportional gain preset value, is the preset value of the maximum proportional gain, and α is the preset value of the exponential adjustment coefficient.

[0056] Furthermore, according to the actual operation of the system, 、 and α are optimized. For example, Set to 0.5, Set to 2.0 and α to 0.1. This method can achieve dynamic adjustment of the proportional gain with the load deviation. When the load deviation is large, the proportional gain is increased to speed up the response speed, and when the load deviation is small, the proportional gain is reduced to improve the regulation accuracy.

[0057] Some of the aforementioned solutions in this application propose a method for dynamically adjusting the proportional gain based on the regulation region. However, in practice, adjusting the proportional gain alone cannot directly translate into an executable control signal; instead, the gain parameter must be combined with the system state to generate specific execution instructions. However, the calculation of the control pulse width in traditional methods lacks standardization, making it prone to over-limit control or insufficient response due to differences in unit capacity, thus affecting regulation accuracy.

[0058] The present application further proposes that the steps of calculating the output control pulse width include: obtaining the current proportional gain value, load deviation value, control period value and maximum output value of the unit; multiplying the proportional gain by the load deviation to obtain a preliminary control amount; multiplying the preliminary control amount by the control period and dividing it by the maximum output of the unit to obtain a standardized pulse width output; the specific calculation formula is: × ×T / ,in is the proportional gain, is the load deviation, T is the control period, The maximum output of the unit.

[0059] Among them, the product operation of proportional gain and load deviation combines the dynamically adjusted gain parameter with the real-time deviation to form a preliminary control quantity. By introducing the maximum output parameter of the unit, the preliminary control quantity is normalized. For example, when the maximum output of the unit is 500MW, the preliminary control quantity is converted into a pulse width value that matches the unit capacity after standardization. The control period is included in the calculation as a time dimension parameter to ensure that the control pulse width is synchronized with the regulation rhythm. In the specific implementation, when the load deviation is 20MW, the proportional gain is 1.5, the control period is 2 seconds, and the maximum output of the unit is 600MW, the calculated standardized pulse width is 0.1 seconds.

[0060] Specifically, based on the dynamic adjustment of the proportional gain, the gain value is combined with the real-time load deviation through multiplication to form a preliminary control quantity that is positively correlated with the deviation magnitude. This control quantity is further multiplied by the control period, introducing the time dimension into the calculation process and generating an intermediate quantity with a time accumulation effect. By dividing the intermediate quantity by the maximum output of the unit, the impact of unit capacity differences on the control quantity is eliminated. For example, a large-capacity unit will obtain a smaller standardized pulse width under the same deviation. The final output control pulse width is converted by the actuator into the opening duration of the regulating valve, which directly affects the output adjustment of the generator unit. This calculation process achieves accurate quantification of the control quantity under multivariable coupling conditions through the linear combination of mathematical formulas, avoiding the adjustment errors caused by the inconsistent parameter dimensions in traditional methods. At the same time, through standardization, the comparability of control instructions for units of different capacities is ensured.

[0061] As a preferred embodiment, the solution of this application is specifically implemented as follows: The steps for calculating the output control pulse width include the following specific operations: First, the current proportional gain value, load deviation value, control period value, and maximum output value of the unit are obtained. These parameters can be collected in real time by the monitoring system of the generator set.

[0062] Next, the proportional gain is multiplied by the load deviation to obtain the initial control variable. This step applies the proportional control action to the system deviation.

[0063] Then, the initial control variable is multiplied by the control period and divided by the maximum output of the unit to obtain the standardized pulse width output. This step converts the control variable into a standardized pulse width signal relative to the maximum output of the unit.

[0064] The specific calculation formula is:

[0065] in To control the output pulse width, is the proportional gain, is the load deviation, T is the control period, The maximum output of the unit.

[0066] For example, assuming the current proportional gain is 0.5, load deviation The maximum output of the unit is 100MW, the control period T is 5 seconds, If the output power is 1000MW, the output control pulse width can be calculated. 0.25 seconds.

[0067] This calculation method converts the control variable into a standardized pulse width signal, which facilitates the subsequent operation of the control actuator. By associating the control variable with the maximum output of the unit, the control output can be adapted to generator sets of different capacities.

[0068] In some of the above-mentioned schemes of the present application, when in the high-precision adjustment area, although precise control is achieved by dynamically adjusting the proportional gain, the fixed control cycle may cause the actuator to be frequently triggered during the adjustment process, affecting the steady-state accuracy and increasing mechanical losses.

[0069] The present application further proposes that when in the high-precision adjustment area, a collaborative working mode of repetitive control and proportional-integral-differential control is enabled; the output control pulse width value obtained by the current calculation is obtained; the output control pulse width is multiplied by a preset period growth coefficient to obtain an adjusted control period; the specific adjustment formula is: ,in is the output control pulse width, C is the preset cycle growth coefficient, and its value range is 2.0 to 3.0.

[0070] When repetitive control and proportional-integral-derivative control are enabled in collaborative mode, the repetitive control module generates a periodic reference signal based on historical control pulse widths, while the proportional-integral-derivative control module dynamically compensates for real-time load deviations. After the output control pulse width is generated by the pulse width calculation module, the period control module multiplies it by a preset period growth factor to dynamically extend the control period. The period growth factor is set to 2.0 to 3.0, a range that has been experimentally verified to extend the control period while avoiding response lag.

[0071] Specifically, in the high-precision adjustment zone, the repetitive control module forms a periodic reference signal by storing historical control pulse width data. This is then superimposed on the dynamic correction value of the proportional-integral-differential control output to form the final execution instruction. Once the output control pulse width is calculated, it is multiplied by the preset coefficient C to obtain the adjusted control period. For example, when the output control pulse width is 0.5 seconds and C is 2.5, the adjusted control period is extended to 1.25 seconds. By extending the control period, the actuator's operating frequency is reduced, mechanical wear is reduced, and steady-state adjustment accuracy is improved. In this process, the periodic reference signal of the repetitive control complements the dynamic correction of the proportional-integral-differential control, maintaining rapid response capabilities while enhancing steady-state control effects.

[0072] As a preferred embodiment, the solution of this application is specifically implemented as follows: When in the high-precision adjustment zone, the collaborative working mode of repetitive control and proportional-integral-differential control is enabled. First, obtain the currently calculated output control pulse width value. Then multiply the output control pulse width by the preset cycle growth coefficient to obtain the adjusted control period. The specific adjustment formula is:

[0073] wherein is the output control pulse width, C is a preset period growth coefficient, and the value range is 2.0-3.0.

[0074] For example, assuming that the currently calculated output control pulse width is 0.5 seconds, and the preset period growth coefficient C is 2.5, then the adjusted control period T = 0.5 x 2.5 = 1.25 seconds. In this way, the control period can be dynamically adjusted according to actual control requirements, improving the adaptability of the system.

[0075] In some of the above schemes of the present application, the multi-region hierarchical regulation method realizes the regulation requirements of different regions by dynamically adjusting the proportional gain and the control period, but the integral time parameter remains a fixed value, resulting in the inability to quickly eliminate steady-state errors in the emergency regulation region and effectively suppress overshoot in the high-precision regulation region.

[0076] The present application further proposes a step of dynamically adjusting the integral time, including obtaining a current load deviation value; selecting a corresponding integral time adjustment strategy according to the current regulation region; adopting a strategy of reducing the integral time in the emergency regulation region to speed up the system response; adopting a strategy of increasing the integral time in the high-precision regulation region to improve the steady-state accuracy; and calculating the integral time through a preset region adaptive function: wherein is the integral time, is a preset value of the reference integral time, is an integral time dynamic function.

[0077] The load deviation value is obtained by real-time acquisition of the difference between the generator output and the target load. The determination of the regulation region is based on the comparison result of the absolute value of the load deviation and the preset threshold, and is divided into the emergency regulation region, the fast regulation region, and the high-precision regulation region. The selection of the integral time adjustment strategy is based on the region discrimination result, the integral time is reduced to 50%-70% of the reference value in the emergency regulation region by using a linear decreasing function, and the integral time is increased to 130%-150% of the reference value in the high-precision regulation region by using an exponential increasing function. The region adaptive function is constructed according to the absolute value of the load deviation, which is set to =0.7-0.2x(|ΔP|-H1) / (H1-H2) in the emergency regulation region, and is set to =1.3+0.3x(H2- |ΔP|) / H2 wherein H1 is a first preset threshold, and H2 is a second preset threshold.

[0078] Specifically, when the system is in the emergency regulation zone, the integral time is dynamically shortened to 70% of the reference value, so that the integral term is enhanced to accelerate the elimination of steady-state error caused by large load deviation. For example, when the reference integral time is set to 10 seconds, the integral time in the emergency regulation zone can be reduced to 7 seconds. When the system enters the high-precision regulation zone, the integral time gradually increases to 1.3 times the reference value, at which time the integral effect is weakened to avoid overshoot caused by integral saturation. The dynamic function of the load deviation The smooth transition of the integral time is realized by real-time calculation of the relative distance between the load deviation and the zone boundary. The adjustment process cooperates with the dynamic adjustment of the control period, realizing fast response by shortening the integral time and the control period in the emergency regulation stage, and ensuring the regulation accuracy by lengthening the integral time and the control period in the steady-state regulation stage.

[0079] As a preferred embodiment, the scheme of the application is implemented as follows: In the dynamic adjustment of the integral time, the current load deviation value is first obtained. According to the regulation zone currently in, the corresponding integral time adjustment strategy is selected. In the emergency regulation zone, the strategy of reducing the integral time is adopted to accelerate the system response. In the high-precision regulation zone, the strategy of increasing the integral time is adopted to improve the steady-state accuracy. The integral time is calculated by a pre-set zone adaptive function: . Wherein is the integral time, is the reference integral time preset value, is the integral time dynamic function.

[0080] Specifically, the reference integral time can be set to 10 seconds. It can be defined as a piecewise function: when , =0.5; when , =1; when , =2. In this way, the integral time is reduced to 5 seconds in the emergency regulation zone to speed up the response; it remains unchanged at 10 seconds in the fast regulation zone; and it is increased to 20 seconds in the high-precision regulation zone to improve the steady-state accuracy.

[0081] In some of the above schemes of the application, the multi-zone hierarchical regulation method realizes the balance between fast response and steady-state accuracy by dynamically adjusting the proportional gain and the integral time. However, when the parameters are adjusted by a large amplitude in the fast regulation zone, the load deviation changes rapidly, which easily causes system overshoot and leads to the risk of oscillation in the regulation process.

[0082] This application further proposes the steps of dynamically adjusting the differential time: obtaining the current load deviation value; enhancing the differential effect in the fast adjustment area to suppress system overshoot; and calculating the differential time using a preset slope prediction function: ,in is the differential time, is the preset value of the base differential time, is the differential time dynamic function.

[0083] The load deviation value is collected in real time by the sensor and transmitted to the control unit. The basis for determining the rapid adjustment zone is that the absolute value of the load deviation is between the second preset threshold and the first preset threshold. The differential time dynamic function adopts a piecewise linear function or an exponential function. When the load deviation change rate increases, the function output value increases with the increase of the slope, so that the differential time dynamically expands. The preset value of the benchmark differential time is set according to the inertia time constant of the unit, and the value range is 0.5 seconds to 2.0 seconds. The input parameter of the slope prediction function is the difference calculation result of the current load deviation and the historical data, and the output coefficient range is controlled in the range of 0.8 to 1.5 times the benchmark value.

[0084] Specifically, during the operation in the fast adjustment zone, the control unit continuously monitors the load deviation change trend. When it is detected that the absolute value of the load deviation is between the second threshold and the first threshold, the differential time dynamic adjustment mechanism is activated. By calculating the load deviation difference between the current sampling period and the previous period, the error change slope is obtained. .Will Enter the slope prediction function , the function output value is multiplied by the reference differential time to generate the real-time differential time parameter. For example, when When the preset threshold is exceeded, An exponential function is used to rapidly increase the derivative time, enhancing the differential link's ability to suppress deviation changes. The adjusted derivative time parameter acts on the differential control term, creating a damping effect that matches the rate of deviation change, effectively suppressing overshoot during regulation. By dynamically matching the derivative time with the actual system response characteristics, the risk of oscillation is reduced while maintaining regulation speed.

[0085] As a preferred embodiment, the solution of this application is specifically implemented as follows: When dynamically adjusting the differential time, the current load deviation value is first obtained. Furthermore, the differential effect is enhanced in the rapid adjustment zone to suppress system overshoot. Specifically, the differential time is calculated using a preset slope prediction function. The calculation formula for the differential time is:

[0086] In this formula, represents the differential time, Indicates the preset value of the base differential time. represents the differential time dynamic function.

[0087] For example, the base differential time can be Set to 0.5 seconds. Differential time dynamic function It can be designed as a piecewise function. When the absolute value of is less than or equal to the second preset threshold, =1; when load deviation When the absolute value of is greater than the second preset threshold and less than or equal to the first preset threshold, Follow increases linearly with the increase of load deviation, and can reach a maximum of 2; When the absolute value of is greater than the first preset threshold, Keep it at 2.

[0088] Therefore, in the rapid regulation zone, as the load deviation increases, the differential time will increase accordingly, thereby enhancing the differential action and effectively suppressing system overshoot. When entering the emergency regulation zone, the differential time is kept at a larger value to maintain a stronger differential action.

[0089] In some of the above-mentioned schemes of the present application, the dynamic adjustment of the differential gain only relies on the current load deviation value, which cannot accurately reflect the dynamic trend of load changes, resulting in the differential action being unable to respond to the rapid changes in the load deviation in a timely manner, easily causing system overshoot or adjustment lag.

[0090] This application further proposes a step for dynamically adjusting the differential gain, including: obtaining the load deviation value at the current sampling moment and the load deviation value at the previous sampling moment; calculating the load deviation change between two consecutive sampling points as the error change slope; and adjusting the differential gain according to the sign and size of the error change slope. The specific calculation and adjustment process is as follows: , ,in is the load deviation at the current sampling moment, is the load deviation at the last sampling moment, is the differential gain, is the preset value of the reference differential gain, and η is the preset value of the slope response coefficient.

[0091] The error slope is calculated by comparing the load deviation differences between adjacent sampling points. This difference reflects the instantaneous rate of change of the load deviation. The slope response coefficient, η, is typically set between 0.1 and 0.5 and controls the adjustment range of the differential gain. When the error slope is positive, indicating an increasing load deviation, the differential gain is adjusted positively to enhance suppression. When the error slope is negative, indicating a decreasing load deviation, the differential gain is adjusted negatively to avoid over-suppression.

[0092] Specifically, load deviation data is continuously collected during the control cycle, and the real-time error change slope is obtained by calculating the deviation difference between two adjacent sampling points. This slope is input into the differential gain adjustment formula and superimposed with the preset reference differential gain. For example, when the load deviation increases rapidly in a short period of time, the error change slope is positive. At this time, the differential gain is increased to enhance the damping effect of the control system and effectively suppress overshoot. When the load deviation tends to stabilize, the error change slope approaches zero, and the differential gain returns to the reference value to maintain the steady-state performance of the system. By tracking the changing trend of the load deviation in real time, the intensity of the differential action is dynamically adjusted to adapt to the control requirements under different working conditions.

[0093] As a preferred embodiment, the solution of this application is specifically implemented as follows: In the multi-region hierarchical PID parameter adaptive adjustment method, the steps of dynamically adjusting the differential gain include: First, the load deviation value at the current sampling moment and the load deviation value at the previous sampling moment are obtained. For example, the load detection module collects the load data of the generator set in real time and calculates the load deviation ΔP[t] at the current sampling moment t and the load deviation ΔP[t-1] at the previous sampling moment t-1.

[0094] Secondly, the load deviation change between two consecutive sampling points is calculated as the error change slope. Specifically, the error change slope Δe can be calculated using the following formula: = [t]- [t-1] Furthermore, the differential gain is adjusted according to the sign and magnitude of the error change slope. The adjustment process can be achieved using the following formula: = +η×Δe in, is the adjusted differential gain, is the preset value of the reference differential gain, and η is the preset value of the slope response coefficient.

[0095] In specific implementation, the appropriate reference differential gain can be set according to the system characteristics and control requirements. and slope response coefficient η. For example, for a certain type of generator set, Set to 0.5 and η to 0.1. When the error change slope is detected to be positive and large, the differential gain will be increased accordingly to enhance the system's rapid response capability; when the error change slope is negative or small, the differential gain will be reduced accordingly to avoid over-regulation.

[0096] In some of the aforementioned solutions of this application, the multi-region hierarchical PID parameter adaptive adjustment method achieves optimized control of different adjustment regions by dynamically adjusting the proportional gain, integral time, and differential time. However, during long-term operation, the fixed minimum and maximum proportional gain reference values ​​cannot be dynamically optimized based on the actual control effect of the system. As a result, the control parameters cannot adapt to the changing trends of power grid assessment indicators, affecting the system's continuous optimization capabilities.

[0097] This application further proposes steps for optimizing multi-cycle assessment feedback, including obtaining scoring data for multiple historical cycles from the automatic power generation control assessment platform; calculating the weighted average of recent assessment scores as a scoring trend value; and proportionally adjusting the minimum proportional gain reference value and the maximum proportional gain reference value according to the scoring trend value.

[0098] Among them, the scoring data acquisition process periodically reads the historical assessment scores within the preset time window through the assessment platform interface, and the time window length is set to 5 to 10 control cycles. The weighted average calculation adopts the exponential weighted moving average algorithm, the recent score weight coefficient is set to 0.6 to 0.8, and the historical score weight coefficient decreases according to the time attenuation coefficient of 0.2 to 0.4. The gain baseline value adjustment process controls the adjustment range through the minimum gain adjustment coefficient β and the maximum gain adjustment coefficient γ respectively. The value range of β is 0.05 to 0.1, and the value range of γ is 0.1 to 0.15. Score trend value Normalization is performed to a value between -1 and 1. When the score trend value is positive, the gain baseline value is increased; when it is negative, the gain baseline value is decreased.

[0099] Specifically, between control cycles, the assessment platform interface is called to retrieve the scoring data for the last 10 AGC assessment cycles. Each scoring data point is assigned a decaying weight in chronological order: the weight coefficient for the most recent cycle is set to 0.8, the weight coefficient for the previous cycle is 0.8 × 0.3, and so on. After the weighted average is calculated, the score trend value is converted into a gain adjustment ratio through linear mapping. When the score trend value reaches 0.5, the minimum proportional gain baseline value is increased by 5%, and the maximum proportional gain baseline value is increased by 15%. The adjusted gain baseline value is immediately applied to the proportional gain calculation module for the next cycle, forming a closed-loop feedback mechanism. Through this dynamic optimization process, the proportional gain adjustment range can be adjusted to match the grid assessment requirements in real time. When the assessment score decreases, the gain adjustment range is automatically expanded to enhance control strength, and when the score increases, the adjustment range is narrowed to maintain system stability.

[0100] As a preferred embodiment, the solution of this application is specifically implemented as follows: An assessment and scoring data acquisition module is deployed in the automatic power generation control system of a 300MW thermal power plant unit. This module obtains assessment scores for the past 12 operating cycles (each cycle lasting 5 minutes) from the automatic power generation control assessment platform of the power grid dispatching center every 15 minutes. The score trend value is calculated using the exponentially weighted moving average method, with a weight of 0.5 for the recent three cycles, a weight of 0.3 for the middle three cycles, and a weight of 0.2 for the far six cycles. When the calculated score trend value reaches the positive threshold of 0.85, the gain reference value adjustment module increases the preset minimum proportional gain reference value from 0.8 to 0.85, and simultaneously increases the maximum proportional gain reference value from 1.2 to 1.25. The gain adjustment coefficients β and γ are set to 0.05 and 0.04, respectively. The parameter update unit writes the new gain reference value into the parameter memory of the PID controller to complete the online optimization of the gain parameters.

[0101] In some of the above-mentioned schemes of this application, a method of dynamically adjusting the proportional gain based on real-time load deviation is proposed to optimize the control performance of the generator set. However, in this process, the fixed preset minimum and maximum proportional gain reference values ​​are difficult to adapt to changes in power grid assessment standards during long-term operation, resulting in the parameter adjustment mechanism being unable to continuously optimize the control effect.

[0102] This application further proposes steps for optimizing multi-cycle assessment feedback, including obtaining scoring data for multiple historical cycles from the automatic power generation control assessment platform, calculating the weighted average of recent assessment scores as the scoring trend value, and proportionally adjusting the minimum proportional gain reference value and the maximum proportional gain reference value according to the scoring trend value.

[0103] The scoring data is obtained using a sliding time window method, with the window length set to 5 to 10 assessment cycles. The weighted average is calculated using the exponentially weighted moving average method, with the weight of recent data set to 0.6 to 0.8. The minimum gain adjustment coefficient β and the maximum gain adjustment coefficient γ are introduced into the gain baseline value adjustment process, and are set to 0.05 to 0.1 and 0.1 to 0.15 respectively. The data is normalized to a value between -1 and 1. A positive value indicates a performance improvement trend, while a negative value indicates a performance degradation trend.

[0104] Specifically, when the automatic power generation control assessment platform continuously records the scoring data of three assessment cycles, the system automatically extracts the scoring data set of the most recent five cycles. The weight coefficient of each cycle score is assigned in reverse chronological order. The weight coefficient of the most recent cycle is set to 0.8, and the weight coefficient of the previous four cycles is set to 0.6, 0.4, 0.2 and 0.1 respectively. The weighted average is calculated by multiplying the scores of each cycle by the corresponding weights, summing them, and then dividing them by the total weights. After obtaining the scoring trend value, the minimum proportional gain reference value is calculated according to the formula Update, the maximum proportional gain reference value is calculated according to the formula Update. When the score trend value is positive, the baseline gain value is increased synchronously to enhance the control system's response speed; when the score trend value is negative, the baseline gain value is appropriately reduced to avoid system oscillation caused by over-adjustment. This mechanism enables the PID parameter adjustment benchmark to be dynamically optimized according to the assessment criteria, continuously improving the comprehensive performance indicators of the automatic power generation control system.

[0105] As a preferred embodiment, the solution of this application is specifically implemented as follows: when the load deviation of the generator set enters the high-precision regulation area from the fast regulation area, a regulation area switching event is detected. At this time, the boundary threshold of the high-precision regulation area is obtained. , calculate the absolute value of the current load deviation and The relative distance is converted into a transition factor φ through an exponential function, where the smoothing coefficient Set to 0.2. The transition factor φ approaches 1 when the load deviation is far away from the boundary threshold and approaches 0 when it is close to the boundary threshold. Proportional gain with target high-precision adjustment area Linearly weighted according to the transition factor to generate the transition period proportional gain This process continues until the load deviation completely enters the new range, achieving a smooth transition of the proportional gain parameters.

[0106] In some of the above-mentioned schemes of this application, although the multi-region hierarchical PID parameter adjustment method realizes dynamic parameter adjustment, it lacks modular device support, resulting in low real-time data processing efficiency and insufficient coordination among functional units, making it difficult to achieve rapid response and precise execution of the closed-loop control system.

[0107] The present application further proposes a multi-region hierarchical PID parameter adaptive adjustment device, which includes a load detection module, a region discrimination module, a parameter adjustment module, a pulse width calculation module, a cycle control module and an execution output module.

[0108] Among them, the load detection module obtains the load deviation signal of the generator set in real time through a high-speed data acquisition unit, with a sampling frequency of not less than 1000Hz; the area discrimination module has a built-in dual-threshold comparator, with the first preset threshold set to 5% of the rated load and the second preset threshold set to 2% of the rated load; the parameter adjustment module integrates an exponential function operation unit, with a minimum proportional gain of 0.8 and a maximum proportional gain of 2.5; the pulse width calculation module contains a multiplier and a divider, and supports floating-point operation accuracy reaching the IEEE754 standard; the period control module sets the period growth coefficient to 2.5 and uses digital phase-locked loop technology to achieve period synchronization; the execution output module is configured with a D / A conversion circuit, and the output resolution reaches 16 bits.

[0109] Specifically, the load detection module converts the collected analog signals into digital signals and transmits them to the zone discrimination module. The zone discrimination module uses a parallel comparison circuit to simultaneously determine the relationship between the absolute value of the load deviation and dual thresholds, completing zone classification within 3μs. The parameter adjustment module receives the zone discrimination results and the load deviation value and uses a preset exponential function parameter library to perform gain calculations, with a calculation cycle of less than 1ms. The pulse width calculation module receives the proportional gain, load deviation, control period, and maximum unit output parameters. A hardware multiplier performs a triple product operation, followed by normalization by a divider to generate a pulse width command. The period control module, activated in the high-precision regulation zone, multiplies the current pulse width value by the period growth coefficient to update the timer set value. The execution output module uses PWM modulation technology to convert the digital pulse width command into a drive signal with an adjustable duty cycle, which is output to the generator speed control mechanism via an optocoupler isolation circuit. Each module interacts with data in real time via a high-speed data bus, ensuring a system response latency of less than 5ms. Under sudden load changes, the complete control loop, from parameter adjustment to execution output, can be completed within 200ms.

[0110] As a preferred embodiment, Figure 2 As shown, the solution of the present application is specifically implemented as follows: The multi-region hierarchical PID parameter adaptive adjustment device includes a load detection module 201, a region discrimination module 202, a parameter adjustment module 203, a pulse width calculation module 204, a period control module 205, and an execution output module 206 connected via a data bus. The load detection module is equipped with a high-precision Hall sensor, which continuously collects the bus current signal of the generator set with a sampling period of 200ms and calculates the real-time load deviation value after analog-to-digital conversion. The region discrimination module has a built-in dual-threshold comparator, which compares the absolute value of the received load deviation with the first threshold of 15MW and the second threshold of 5MW stored in the non-volatile memory in parallel, and outputs three digital signals to the status register to indicate the current adjustment region.

[0111] The parameter adjustment module integrates a floating point operation unit, calls the exponential function calculator to perform formula calculations, and dynamically selects the preset parameter library according to the regional status of the register mark. =0.8 and =2.4 for linear interpolation. The pulse width calculation module performs three-dimensional operations using a hardware multiplier, normalizing the adjusted proportional gain, load deviation, and control period. This is combined with the unit's rated output of 600MW stored in Flash memory to generate PWM waveform parameters. The period control module activates the coprocessor upon detecting the high-precision adjustment zone marker, shift-multiplies the pulse width calculation result by a preset coefficient of 2.5, and outputs the adjusted control period to the timer interrupt controller. The execution output module, equipped with an optocoupler isolation circuit and an IGBT driver chip, converts the pulse width signal into a 12V pulse sequence, which is then transmitted to the generator set's speed regulator servo mechanism via a fiber optic interface.

[0112] Through the above technical solution, this application realizes the regional adaptive adjustment capability of generator set output control. Through the coordinated operation of the modular hardware architecture, the proportional gain is quickly increased to shorten the response delay under emergency conditions, and the control cycle is extended to suppress the actuator jitter under high-precision conditions, effectively solving the problem that traditional PID controllers are difficult to balance dynamic characteristics and steady-state accuracy in wide-range load fluctuation scenarios. Through real-time regional discrimination and parameter linkage adjustment mechanism, the device enables the generator set to automatically switch to the optimal control mode in different deviation ranges, significantly improving the tracking accuracy and anti-interference capability of grid frequency regulation.

[0113] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0114] like Figure 3 As shown, electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 302 or programs loaded from a storage device 308 into a random access memory (RAM) 303. RAM 303 also stores various programs and data required for the operation of electronic device 300. Processing device 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to bus 304.

[0115] Generally, the following devices can be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, a magnetic tape, a hard disk, and the like; and communication devices 309. The communication devices 309 can allow the electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it is understood that all of the shown devices are not required to be implemented or present. More or fewer devices can alternatively be implemented or present. Figure 3 Each block shown in the flowcharts of FIGS. 10 and 11 can represent a device or a plurality of devices as necessary.

[0116] Further, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the methods described above, wherein the program instructions can be stored in the storage medium in the form of a software product, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes, or a computer, a server, a mobile phone, a tablet, and the like terminal devices.

[0117] The above only describes the embodiments of the present application and is not used to limit the protection scope of the present application. For those skilled in the art, the present application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A multi-region hierarchical PID parameter adaptive adjustment method, characterized in that: The following steps are involved: Real-time collection of current load deviation of generator sets; Comparing the absolute value of the load deviation with a first preset threshold and a second preset threshold, wherein the first preset threshold is greater than the second preset threshold and both are positive numbers; Determine the current adjustment area based on the comparison result: when the absolute value of the load deviation is greater than the first preset threshold, enter the emergency adjustment area; when the absolute value of the load deviation is greater than the second preset threshold and less than or equal to the first preset threshold, enter the fast adjustment area; when the absolute value of the load deviation is less than or equal to the second preset threshold, enter the high-precision adjustment area; Dynamically adjust the proportional gain based on the current regulation area and load deviation; Calculate the output control pulse width based on the adjusted proportional gain, load deviation, control cycle and maximum output of the unit; When in the high-precision adjustment area, the control period is adjusted according to the output control pulse width and the preset period growth coefficient.

2. The method according to claim 1, characterized in that The step of dynamically adjusting the proportional gain specifically includes: Get the current load deviation value; An exponential response term is calculated based on the load deviation value, which approaches 1 when the load deviation increases and approaches 0 when the load deviation decreases; Interpolation calculation is performed between the preset minimum proportional gain and maximum proportional gain based on the exponential response term; The final proportional gain is determined by the following formula: in is the load deviation, is the minimum proportional gain preset value, is the preset value of the maximum proportional gain, and α is the preset value of the exponential adjustment coefficient.

3. The method according to claim 1, characterized in that The step of calculating the output control pulse width specifically includes: Obtain the current proportional gain value, load deviation value, control period value and maximum output value of the unit; Multiply the proportional gain and the load deviation to obtain the preliminary control amount; Multiply the initial control amount by the control period and divide it by the maximum output of the unit to obtain the standardized pulse width output; The specific calculation formula is: in To control the output pulse width, is the proportional gain, is the load deviation, T is the control period, The maximum output of the unit.

4. The method according to claim 1, wherein The step of adjusting the control period specifically includes: When in the high-precision adjustment area, the collaborative working mode of repetitive control and proportional-integral-differential control is enabled; Get the currently calculated output control pulse width value; Multiply the output control pulse width by the preset cycle growth coefficient to obtain the adjusted control cycle; The specific adjustment formula is: in is the output control pulse width, C is the preset cycle growth coefficient, and the value range is 2.0 to 3.

0.

5. The method according to claim 1, wherein It also includes steps for dynamically adjusting the integration time: Get the current load deviation value; Select the corresponding integral time adjustment strategy according to the current adjustment area; In the emergency adjustment area, the strategy of reducing the integral time is adopted to accelerate the system response; In the high-precision adjustment area, the strategy of increasing the integral time is adopted to improve the steady-state accuracy; The integration time is calculated using a preset area adaptation function: in is the integration time, It is the preset value of the base integration time. is the integral time dynamic function.

6. The method according to claim 1, characterized in that It also includes the steps of dynamically adjusting the derivative time: Get the current load deviation value; In the fast adjustment area, the derivative effect is enhanced to suppress the system overshoot; The derivative time is calculated using the preset slope prediction function: in is the differential time, is the preset value of the base differential time, is the differential time dynamic function.

7. The method according to claim 1, characterized in that It also includes the steps for dynamically adjusting the differential gain: Obtain the load deviation value at the current sampling moment and the load deviation value at the previous sampling moment; Calculate the load deviation change between two consecutive sampling points as the error change slope; Adjust the differential gain according to the positive and negative sign and size of the error change slope; The specific calculation and adjustment process is as follows: Where ΔP[k] is the load deviation at the current sampling moment, ΔP[k-1] is the load deviation at the previous sampling moment, is the differential gain, is the preset value of the reference differential gain, and η is the preset value of the slope response coefficient.

8. The method according to claim 1, characterized in that It also includes steps for optimizing multi-cycle assessment feedback: Obtain historical scoring data for multiple cycles from the automatic power generation control assessment platform; Calculate the weighted average of recent assessment scores as the score trend value; Proportionally adjust the minimum proportional gain reference value and the maximum proportional gain reference value according to the score trend value; The specific update formula is: in is the updated minimum proportional gain, is the updated maximum proportional gain, β and γ are the minimum / maximum gain adjustment coefficients respectively, It is the weighted average of recent assessment scores.

9. The method according to claim 1, characterized in that It also includes the steps of regional transition smoothing: When a regulation area switching event is detected, the current area boundary threshold is obtained; Calculate the relative distance between the current load deviation and the area boundary threshold; An exponential transition factor is calculated based on the relative distance, which is close to 1 when far from the boundary and close to 0 when close to the boundary; Use transition factors to perform weighted fusion on proportional gains to achieve smooth parameter transition; The specific calculation process is: Where φ is the transition factor, is the current region boundary threshold, is the smoothing coefficient, is the proportional gain during the transition period, is the target gain for the new interval, is the original interval gain.

10. A multi-region hierarchical PID parameter adaptive adjustment device, characterized in that: include: Load detection module, used to collect the current load deviation of the generator set in real time; a region discrimination module, configured to compare the absolute value of the load deviation with a first preset threshold and a second preset threshold, and determine a current adjustment region based on the comparison result: when the absolute value of the load deviation is greater than the first preset threshold, the current adjustment region is entered; when the absolute value of the load deviation is greater than the second preset threshold and less than or equal to the first preset threshold, the current adjustment region is entered; and when the absolute value of the load deviation is less than or equal to the second preset threshold, the current adjustment region is entered; Parameter adjustment module, used to dynamically adjust the proportional gain based on the current regulation area and load deviation; Pulse width calculation module, used to calculate the output control pulse width based on the adjusted proportional gain, load deviation, control period and maximum output of the unit; A cycle control module, used to adjust the control cycle according to the output control pulse width and a preset cycle growth coefficient when in the high-precision adjustment area; The execution output module is used to output the calculated control pulse width to the generator set actuator.

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