Linkage control method of water energy recovery unit and flow and pressure regulating valve

Abstract

The application discloses a linkage control method for a water energy recovery unit and a flow-regulating pressure-regulating valve, belongs to the technical field of hydropower generation control systems, and comprises the following steps: obtaining water energy recovery unit operation parameters and flow-regulating pressure-regulating valve state parameters; identifying a current system working condition type based on the operation parameters; calculating dynamic coupling model parameters according to the system working condition type and the state parameters; performing fuzzy adaptive control based on the dynamic coupling model parameters and a flow pressure deviation; and selecting and executing a corresponding multi-modal control strategy according to control parameters and the system working condition type, establishing a dynamic coupling model of a flow-regulating valve flow characteristic-guide vane opening-response delay, realizing fuzzy rule-driven PID parameter adaptive adjustment, and constructing a multi-modal collaborative control mechanism for the unit and the valve. The application effectively solves the flow-pressure dynamic imbalance problem in the water plant unit operation conversion and load rejection working conditions.

Description

Technical Field

[0001] This invention relates to the field of hydropower generation control system technology, and in particular to a linkage control method between a hydropower recovery unit and a flow and pressure regulating valve, which is applicable to dynamic flow-pressure control under special operating conditions such as high and low head switching, main and standby unit switching, and load shedding in water plants. Background Technology

[0002] In water supply systems, hydropower recovery units are often used to generate electricity from excess water, while flow and pressure regulating valves are responsible for regulating system flow and maintaining pressure stability. In actual operation, water plants frequently face conditions such as high / low head switching, main / standby unit switching, and load shedding. These special conditions pose significant challenges to flow and pressure control.

[0003] Existing technologies typically employ traditional PID control methods, which have the following shortcomings: First, traditional PID control parameters are fixed, resulting in a slow response and difficulty in adapting to dynamic operating conditions; second, the flow regulating valve and the water recovery unit lack an effective coordinated control mechanism, leading to system pressure imbalance when the flow rate changes abruptly; and finally, the guide vane opening degree and valve response delay are not coupled and modeled, resulting in low control accuracy.

[0004] These problems lead to technical defects in the system, such as significant flow overshoot (error up to ±500 m³ / h), large pressure fluctuations (causing water hammer risk in pipelines), and excessively long system recovery time (more than 300 seconds) during unit switching or load shedding. Summary of the Invention

[0005] The purpose of this invention is to provide a linkage control method for water energy recovery units and flow regulation and pressure regulating valves, which aims to solve the problem of dynamic imbalance between flow and pressure under the operation switching and load shedding conditions of water plant units, and realize the coordinated control of water energy recovery units and flow regulation and pressure regulating valves.

[0006] This invention proposes a linkage control method for a water energy recovery unit and a flow and pressure regulating valve, including:

[0007] Obtain the operating parameters of the water recovery unit and the status parameters of the flow and pressure regulating valves;

[0008] Based on the aforementioned operating parameters, the current system operating condition type is identified;

[0009] Calculate the dynamic coupling model parameters based on the system operating condition type and the state parameters;

[0010] Based on the dynamic coupling model parameters and the flow-pressure deviation, fuzzy adaptive control is performed to obtain the control parameters;

[0011] Based on the control parameters and the system operating condition type, the corresponding multimodal control strategy is selected and executed to achieve the linkage control between the water energy recovery unit and the flow and pressure regulating valve.

[0012] Preferably, the acquisition of the operating parameters of the hydropower recovery unit and the status parameters of the flow regulating and pressure regulating valve includes:

[0013] Obtain the current flow rate, system pressure, guide vane opening, and power change rate of the water recovery unit;

[0014] Obtain the current opening degree and flow characteristic parameters of the flow regulating and pressure regulating valve.

[0015] Preferably, the identification of the current system operating condition type includes:

[0016] When the power drop exceeds a preset threshold, the current system operating condition is identified as a load shedding condition.

[0017] When a unit switching command is received, the current system operating condition is identified as the unit operation switching condition;

[0018] When the system parameters are within the normal fluctuation range, the current system operating condition is identified as the normal operating condition.

[0019] Preferably, the calculation of the dynamic coupling model parameters includes:

[0020] Calculate the valve flow gain coefficient based on the current flow rate, system pressure, and guide vane opening.

[0021] Estimate the guide vane response delay time based on historical response data;

[0022] Calculate the inertial time constant, which characterizes the dynamic properties of the system;

[0023] The valve flow gain coefficient, the guide vane response delay time, and the inertial time constant are integrated into a dynamic coupling model.

[0024] Preferably, the fuzzy adaptive control includes:

[0025] Calculate the flow error and the rate of change of error;

[0026] The flow rate error and the rate of change of error are fuzzed.

[0027] Based on a pre-set fuzzy rule base, the adjustment amount of the PID controller parameters is determined;

[0028] Based on the adjustment amount, the proportional, integral, and derivative parameters of the PID controller are updated to obtain the control parameters.

[0029] Preferably, the fuzzy rule base contains 49 rules, which use an IF-THEN structure to describe the mapping relationship between flow error, error change rate and PID parameter adjustment.

[0030] Preferably, when the system operating condition is a unit operation transition condition, the multimodal control strategy includes:

[0031] Adjust the opening of the flow regulating and pressure regulating valve in advance to the preset percentage of the target flow rate;

[0032] The guide vanes are controlled to close gradually according to an exponential decay law;

[0033] The opening of the flow regulating valve is made to track the flow rate changes of the guide vane in real time, so as to maintain the stability of the system flow rate;

[0034] Monitor the pipeline pressure and adjust the valve opening to compensate when the pressure is abnormal.

[0035] Preferably, when the system operating condition is a load shedding condition, the multimodal control strategy includes:

[0036] Start the traffic tiered interception program;

[0037] Perform multi-level traffic reduction at preset time intervals;

[0038] After each flow rate adjustment, fine-tuning is performed based on the pressure feedback signal;

[0039] Once the traffic has stabilized, the system will switch to a fine-tuning phase to gradually restore it to a steady state.

[0040] Preferably, the multi-level traffic reduction includes:

[0041] The first stage involves rapidly closing the valve to 70% of the current flow rate;

[0042] The second level, after a preset time, adjusts to 50% of the current flow rate based on pressure feedback;

[0043] After the third level continues for the preset time, it is adjusted to the final target flow rate.

[0044] As a preferred option, it also includes:

[0045] Monitor real-time changes in system flow and pressure;

[0046] When flow overshoot or pressure fluctuations exceed the safety threshold, the emergency adjustment mechanism is triggered.

[0047] The emergency adjustment mechanism includes temporarily increasing the derivative parameter of the PID controller and decreasing the proportional parameter to suppress system oscillations.

[0048] The linkage control method between the water energy recovery unit and the flow and pressure regulating valve provided by this invention has the following beneficial effects:

[0049] 1. Significantly improves the dynamic control performance of the system, reducing flow overshoot by 80% (from ±500m³ / h to ±100m³ / h), and reducing pressure fluctuation by 70%, effectively reducing the risk of water hammer;

[0050] 2. Enhance the system's adaptive capability by using fuzzy adaptive control to achieve real-time adjustment of PID parameters, thereby improving the system's adaptability to different operating conditions and increasing response delay compensation accuracy by 60%.

[0051] 3. Improved system coordination and control performance; the time for the unit to recover to steady state after load switching / shedding is reduced from more than 300 seconds to less than 150 seconds, significantly improving system operating efficiency. Attached Figure Description

[0052] Figure 1 This is a flowchart of the linkage control method between the water energy recovery unit and the flow and pressure regulating valve of the present invention;

[0053] Figure 2 This is a structural diagram of the fuzzy adaptive control system of the present invention;

[0054] Figure 3 This is a flowchart illustrating the execution of the multimodal control strategy of this invention. Detailed Implementation

[0055] Please refer to the attached document. Figure 1 - Figure 3 The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Those skilled in the art should understand that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0056] like Figure 1 As shown, the linkage control method between the water energy recovery unit and the flow and pressure regulating valve provided by the present invention includes the following steps:

[0057] Step 1: Obtain the operating parameters of the water energy recovery unit and the status parameters of the flow and pressure regulating valves.

[0058] This invention first acquires the operating parameters of the hydropower recovery unit and the status parameters of the flow and pressure regulating valve through a sensor network and a data acquisition system. Preferably, the acquired operating parameters of the hydropower recovery unit include the current flow rate, system pressure, guide vane opening, and power change rate; the acquired status parameters of the flow and pressure regulating valve include the current opening and flow characteristic parameters.

[0059] In a preferred embodiment of the present invention, the collected data includes the following categories: system flow rate is collected in real time by an electromagnetic flowmeter with a sampling frequency of 10Hz, an effective measurement range of 0-2000 m³ / h, and an accuracy of ±0.5% of full scale; system pressure is collected by a pressure sensor with a sampling frequency of 10Hz, a measurement range of 0-2.0 MPa, and an accuracy of ±0.2% of full scale; guide vane opening is measured by a position sensor with an accuracy of 0.1% and a response time of no more than 50 ms; the power change rate is obtained by calculating the power difference between two adjacent sampling periods, i.e.

[0060] ,in The power change value is (kW). The sampling time interval (s) is used; the opening degree of the flow regulating and pressure regulating valve is measured by an electrical angle sensor with an accuracy of 0.5%; the flow characteristic parameter is determined by the valve characteristic curve, which reflects the relationship between the valve opening degree and the flow rate.

[0061] After these parameters are collected, they undergo data preprocessing, including outlier removal and smoothing filtering, to ensure the data quality used in subsequent processing. The data preprocessing employs a moving median filter with a window size of 5 sampling points, effectively eliminating short-duration pulse interference. Furthermore, the collected data is normalized to convert different physical quantities into a unified dimensional space, facilitating subsequent fuzzy control processing.

[0062] Step 2: Identify the current system operating condition type based on the operating parameters.

[0063] This invention identifies the current system operating condition type based on acquired operating parameters, particularly the power change rate and system control commands. Specifically, it includes:

[0064] When the power drop exceeds a preset threshold, the current system operating condition is identified as a load shedding condition. Preferably, this preset threshold is set to 20%, meaning that when the power drops by more than 20% of the rated power within a short period (usually within 1 second), the system determines it to be a load shedding condition. This threshold setting is based on hydropower station operating experience and can effectively distinguish between normal power fluctuations and load shedding events. In practical engineering applications, this threshold can be adjusted within the range of 15% to 25% according to the specific characteristics of the hydropower plant. Larger hydropower stations may require a smaller threshold (e.g., 15%) to improve sensitivity, while smaller hydropower stations can use a larger threshold (e.g., 25%) to reduce false positives.

[0065] When a unit switching command is received, the current system operating condition is identified as a unit operation switching condition. These commands typically come from the host computer or central control system, indicating that a switch from one unit to another is required, or a high-low head switch is needed.

[0066] When system parameters are within the normal fluctuation range, the current system operating condition is identified as normal operating condition. The normal fluctuation range is usually defined as: flow fluctuation not exceeding ±5% of the set value, and pressure fluctuation not exceeding ±3% of the set value. These parameters can be adjusted appropriately according to the specific characteristics of the water plant. For more stable systems, the range can be narrowed to ±3% for flow and ±2% for pressure, while for systems with larger fluctuations, it can be widened to ±8% for flow and ±5% for pressure.

[0067] The operating condition identification employs a rule-based judgment method, which features low computational complexity and rapid response, making it suitable for the needs of real-time control systems. Preferably, the judgment period for operating condition identification is 100ms, ensuring that the system can respond promptly to changes in operating conditions.

[0068] Step 3: Calculate the parameters of the dynamic coupling model based on the system operating condition type and state parameters.

[0069] After identifying the operating condition type, this invention calculates the parameters of the dynamic coupling model based on the current system operating conditions and state parameters. This step is one of the core innovations of this invention, integrating the dynamic characteristics of the flow regulating valve and the water energy recovery unit into a unified mathematical model.

[0070] The dynamic coupling model is represented by the following transfer function:

[0071] ,

[0072] in: is the valve flow gain coefficient, which is dimensionless and characterizes the degree of influence of valve opening changes on flow rate. The guide vane response delay time, in seconds (s), represents the delay from the issuance of a command to the actual movement of the guide vane; For the Laplace transform variables, , , Let be the system's inertial time constant, expressed in seconds (s), describing the system's dynamic response characteristics; numerator term Represents the advanced characteristics of the system, denominator term This indicates the system's hysteresis characteristic.

[0073] The specific calculation method is as follows:

[0074] Based on current traffic System pressure and guide vane opening Calculate the valve flow gain coefficient :

[0075] ,

[0076] in, For changes in valve opening in historical data The corresponding flow rate change is expressed in cubic meters per hour per degree (m³ / h·°). The pressure is for reference only, and the unit is megapascals (MPa), usually taken as 1.0 MPa. This represents the current system pressure, expressed in megapascals (MPa).

[0077] In practical applications, The value is usually between 0.8 and 1.2, and the specific value is adjusted according to the operating conditions. The physical meaning of is the change in flow rate caused by a one-degree change in valve opening under standard pressure.

[0078] Estimate guide vane response delay time based on historical response data. :

[0079] ,

[0080] in, The base delay time is measured in seconds (s) and is typically 0.5 to 1.0 seconds. This is an additional delay, measured in seconds (s), typically 0.3 to 0.8 seconds; The current guide vane opening is expressed in degrees (°) or percentages (%). Maximum guide vane opening, unit: The same applies. This formula shows that the smaller the guide vane opening, the greater the response delay, which aligns with the actual operating characteristics of hydraulic turbine units. In large hydropower stations, It may need to be adjusted to 1.0 to 1.5 seconds, while for small hydropower stations it may be 0.3 to 0.6 seconds, depending on the characteristics of the equipment.

[0081] Calculate the system's inertial time constant , , :

[0082] ,

[0083] ,

[0084] ,

[0085] in, The characteristic pipe length is expressed in meters (m). This refers to the cross-sectional area of ​​the pipe, in square meters (m²). The acceleration due to gravity is taken as 9.8 m / s². The system pressure is expressed in Pascals (Pa). The flow rate is expressed in cubic meters per second (m³ / s). The moment of inertia is expressed in kilograms per square meter (kg·m²). Angular velocity, measured in radians per second (rad / s); Rated torque is expressed in Newton-meters (N·m); V is the pipe volume, expressed in cubic meters (m³). This is the density of water, expressed in kilograms per cubic meter (kg / m³), and is approximately 1000 kg / m³ at room temperature. This is the bulk modulus of water, measured in Pascals (Pa), which is approximately 2.2 × 10^9 Pa at room temperature and pressure.

[0086] After these parameters are calculated, they are integrated into a dynamic coupling model, providing a mathematical foundation for subsequent control. This model can accurately describe the dynamic coupling relationship between the flow and pressure regulating valve and the water energy recovery unit, laying the foundation for precise control. Preferably, the model parameter update cycle is 200ms to ensure that the model can reflect changes in system state in a timely manner.

[0087] Step 4: Based on the parameters of the dynamic coupling model and the flow pressure deviation, perform fuzzy adaptive control.

[0088] After obtaining the dynamic coupling model, this invention performs fuzzy adaptive control to achieve real-time optimization and adjustment of the PID controller parameters. This is another core innovation of this invention, breaking through the limitation of fixed parameters in traditional PID controllers.

[0089] First, calculate the flow error and the rate of change of error:

[0090] ,

[0091] ,

[0092] in, The target flow rate is expressed in cubic meters per hour. ) For actual flow rate, the unit is . same; This represents the current flow error, in the same unit as the flow rate. This represents the flow rate error from the previous moment, in units of... same; The error rate is expressed in units of flow rate per sampling period. ).

[0093] To facilitate fuzzy control processing, the error and the rate of change of error need to be normalized:

[0094] ,

[0095] ,

[0096] in, The normalized error is dimensionless and typically ranges from [-1, 1]. The maximum error value, unit and The same applies, typically taken as 10% of the system's rated flow rate; The normalized rate of change of error is dimensionless and typically ranges from [-1, 1]. The maximum rate of change of error, in units of and . Same, usually taken Divide by the sampling period.

[0097] Next, the flow error and error change rate The error and the rate of change of error are divided into seven fuzzy sets: negative large (NB), negative medium (NM), negative small (NS), zero (ZO), positive small (PS), positive medium (PM), and positive large (PB).

[0098] Fuzzification uses a triangular membership function. For the error e, the membership function parameters for each fuzzy set are as follows:

[0099] NB: [-1.0, -1.0, -0.75] represents the x-coordinates of the three vertices of the triangle membership function;

[0100] NM: [-0.9, -0.6, -0.3];

[0101] NS:[-0.5,-0.25,0];

[0102] ZO: [-0.2, 0, 0.2];

[0103] PS: [0, 0.25, 0.5];

[0104] PM: [0.3, 0.6, 0.9];

[0105] PB: [0.75, 1.0, 1.0];

[0106] Error change rate The membership function parameters are similar to those of the error, but the normalization range is usually narrower to reflect the characteristics of the rate of change.

[0107] Based on a pre-defined fuzzy rule base, the adjustment amount of the PID controller parameters is determined. The fuzzy rule base contains 49 rules (7×7 matrix), which use an IF-THEN structure to describe the mapping relationship between flow error, error change rate, and PID parameter adjustment.

[0108] Examples of some rules:

[0109] IF AND THEN , , ,

[0110] IF AND THEN , , ,

[0111] IF AND THEN , , ,

[0112] IF AND THEN , , ,

[0113] These rules embody a basic control principle: when the error is large and changes rapidly, increase the proportional coefficient, decrease the integral coefficient, and appropriately increase the derivative coefficient to improve the system response speed; when the error is small and changes slowly, decrease the proportional coefficient, increase the integral coefficient, and decrease the derivative coefficient to improve steady-state accuracy.

[0114] Fuzzy inference employs the Mamdani inference method, determining the output fuzzy set through max-min composition operations:

[0115] ,

[0116] in, For output variables The membership degree of the corresponding fuzzy set; Input variables The membership degree of the corresponding fuzzy set; Input variables The membership degree of the corresponding fuzzy set; The membership degree of the fuzzy rule.

[0117] Defuzzification uses the centroid method to calculate PID parameter adjustments:

[0118] ,

[0119] in, For parameter adjustment amount, For the first The output discrete point values, This represents the membership degree value of that point. This represents the number of discrete points. , , Perform the calculations separately.

[0120] Then update the PID controller parameters:

[0121] ,

[0122] ,

[0123] ,

[0124] in, , , The updated PID parameters are dimensionless. , , These are the original PID parameters, dimensionless; , , This is a dimensionless parameter adjustment value.

[0125] In practical implementation, the typical values ​​of the original PID parameters are: , , The parameter adjustment range is usually limited to a certain range, for example... , , To ensure system stability, the parameter update cycle is the same as the control cycle, preferably 100ms.

[0126] The PID parameters change dynamically with the operating conditions. They exhibit significant changes during sudden flow changes, but tend to remain constant after the system stabilizes, demonstrating the adaptive control characteristics of this invention. As shown in the figure, the system experiences significant disturbances and large parameter adjustments in the 0-50 second and 200-250 second ranges; however, the system operates smoothly with minimal parameter changes in the 50-200 second range.

[0127] Step 5: Select and execute the corresponding multimodal control strategy based on the control parameters and system operating conditions.

[0128] After completing the fuzzy adaptive control, this invention selects and executes the corresponding multimodal control strategy based on the current system operating condition, thereby achieving coordinated control between the hydropower recovery unit and the flow and pressure regulating valves. This is the third core innovation of this invention: designing specialized control strategies for different operating conditions.

[0129] When the system operating condition is a unit operation transition condition, the following control strategy is executed:

[0130] First, the opening of the flow regulating and pressure regulating valve is adjusted in advance to a preset percentage of the target flow rate. Preferably, this preset percentage is 90%, meaning that before unit switching, the opening of the flow regulating and pressure regulating valve is adjusted to a position that can provide 90% of the target flow rate. This pre-adjustment strategy can reduce flow fluctuations at the moment of switching. In actual engineering, this preset percentage can be adjusted within the range of 85% to 95%. Larger systems may require a pre-adjustment closer to the target flow rate (e.g., 95%), while smaller systems may use a lower preset value (e.g., 85%).

[0131] Secondly, control the guide vanes to close gradually according to an exponential decay law:

[0132]

[0133] in, For a moment The guide vane opening, expressed in degrees (°) or percentages (%). Initial guide vane opening, unit: same; Time is measured in seconds (s). This is the exponential decay coefficient, measured in seconds (s⁻¹), with a preferred value of 0.02 s⁻¹. (Exponential coefficient) These are values ​​optimized through experiments, achieving a good balance between guide vane closing speed and system stability. Smaller exponential coefficients (such as 0.01 s⁻¹) will result in slow closing, while larger coefficients (such as 0.05 s⁻¹) may cause pressure fluctuations.

[0134] At the same time, the opening of the flow regulating valve tracks the flow rate changes of the guide vane in real time to maintain stable system flow:

[0135] ,

[0136] in, For a moment Valve opening degree, expressed in degrees (°) or percentages (%). Basic opening, unit and same; For the opening adjustment amount, the unit is . The same applies, matching the change in guide vane opening; It is an exponential coefficient, measured in seconds (s^-1), the same as the guide vane exponent, ensuring the coordination between the guide vane and the valve's movement.

[0137] Finally, the pipeline pressure is continuously monitored, and valve openings are adjusted to compensate for abnormal pressure. Specifically, when the pressure deviation exceeds ±5%, the pressure compensation mechanism is triggered, and the valve opening is adjusted.

[0138] ,

[0139] in, The adjusted valve opening is expressed in degrees (°) or percentages (%). This is the pressure compensation coefficient, dimensionless, and typically ranges from 0.5 to 1.0. The target pressure is expressed in megapascals (MPa). For actual pressure, the unit and The same applies. Large water treatment systems may require smaller compensation coefficients (e.g., 0.5) to avoid over-regulation, while small systems may require larger compensation coefficients (e.g., 1.0) to accelerate response.

[0140] When the system operating condition is load shedding, the following control strategy is executed:

[0141] First, the flow tiered cutoff procedure is initiated. Upon detecting a load shedding signal, the system immediately executes flow control to prevent pressure fluctuations.

[0142] Next, multi-level traffic reduction is performed at preset time intervals. This invention employs a three-level traffic reduction strategy:

[0143] The first stage involves rapidly closing the valve to 70% of the current flow rate, a process completed within 2 seconds.

[0144] The second level, after lasting 1.5 seconds, adjusts to 50% of the current flow rate based on pressure feedback;

[0145] After the third stage lasts for another 3 seconds, the flow rate is adjusted to the final target flow rate, which is usually 30% of the rated flow rate.

[0146] These percentages and time intervals are determined based on experience operating hydropower stations and can be adjusted according to the actual system characteristics. Larger systems may require more downshifts and longer time intervals, while smaller systems may be simplified to two downshifts.

[0147] After each flow rate adjustment, fine-tuning is performed based on the pressure feedback signal:

[0148] ,

[0149] in, The adjusted valve opening is expressed in degrees (°) or percentages (%). For the target valve opening, the unit is . Same; other parameters have the same meaning as before.

[0150] Finally, after the flow rate has basically stabilized (usually 15-20 seconds after load shedding), the system switches to the fine-tuning stage, adopting more conservative control parameters (reducing...). Increase This gradually restores the system to steady state. The PID parameter adjustment coefficients during the fine-tuning phase are typically: Multiply by 0.8, Multiply by 1.2, It remains unchanged.

[0151] Furthermore, this invention also includes an emergency adjustment mechanism. The system continuously monitors real-time changes in flow and pressure, and triggers the emergency adjustment mechanism when flow overshoot or pressure fluctuation exceeds a safety threshold. Preferably, the flow overshoot safety threshold is set to ±15%, and the pressure fluctuation safety threshold is set to ±10%. These thresholds are determined based on the hydropower station's safe operation standards and can be adjusted according to specific system characteristics and safety requirements.

[0152] Emergency adjustment mechanisms include temporarily increasing the derivative parameter of the PID controller and decreasing the proportional parameter to suppress system oscillations.

[0153] ,

[0154] ,

[0155] in, , These are the parameters after emergency adjustments, and are dimensionless. This is a proportional parameter adjustment coefficient, dimensionless, with a preferred value of 0.7; This is a dimensionless adjustment coefficient for the differential parameter, with a preferred value of 1.5. Coefficients 0.7 and 1.5 are empirical values ​​and can be adjusted appropriately based on the actual system characteristics. Larger systems may require gentler adjustments (e.g., ...). Smaller systems may require more radical adjustments (such as...). ).

[0156] The emergency adjustment period is typically 3 to 5 times the system characteristic time, after which the system returns to normal control parameters. The characteristic time can be determined using model parameters. , The weighted average estimate is typically in the range of 10 to 30 seconds.

[0157] Through the above technical solution, the linkage control method of the water energy recovery unit and the flow and pressure regulating valve of the present invention achieves the following technical effects:

[0158] 1. Significantly improved dynamic performance: Under unit switching and load shedding conditions, the method of this invention reduces flow overshoot by 80% (from ±500m³ / h to ±100m³ / h) and pressure fluctuation by 70%, effectively reducing the risk of water hammer and improving system safety.

[0159] 2. Significantly enhanced adaptive capability: Through the fuzzy adaptive control mechanism, the PID parameters can be adjusted in real time according to the operating conditions. The parameter change curve is smooth and targeted, greatly improving the system's adaptability to different operating conditions and increasing the response delay compensation accuracy by 60%.

[0160] 3. Improved collaborative control effect: The recovery time to steady state after unit switching / load shedding is shortened from more than 300 seconds to less than 150 seconds, the system operating efficiency is greatly improved, the energy utilization rate is increased, and the operating cost is reduced.

[0161] The embodiments of the present invention have been described in detail above. However, it should be noted that the present invention is not limited to the above embodiments. Those skilled in the art can make various changes and improvements without departing from the principles and spirit of the present invention, and these changes and improvements should also be considered within the scope of protection of the present invention.

Claims

1. A method for the linkage control of a water energy recovery unit and a flow and pressure regulating valve, characterized in that, include: Obtain the operating parameters of the water recovery unit and the status parameters of the flow and pressure regulating valves; Based on the aforementioned operating parameters, the current system operating condition type is identified; Based on the operating parameters and the state parameters, the dynamic coupling model parameters are calculated, including the valve flow gain coefficient, guide vane response delay time, and inertia time constant. Based on the dynamic coupling model parameters and the flow-pressure deviation, fuzzy adaptive control is performed to obtain the control parameters; Based on the control parameters and the system operating condition type, the corresponding multimodal control strategy is selected and executed to achieve the linkage control between the water energy recovery unit and the flow and pressure regulating valve. The parameters for calculating the dynamic coupling model include: Calculate the valve flow gain coefficient based on the current flow rate, system pressure, and guide vane opening. Estimate the guide vane response delay time based on historical response data; Calculate the inertial time constant, which characterizes the dynamic properties of the system; The valve flow gain coefficient, the guide vane response delay time, and the inertial time constant are integrated into a dynamic coupling model.

2. The method according to claim 1, characterized in that, The acquisition of the operating parameters of the hydropower recovery unit and the status parameters of the flow regulating and pressure regulating valves includes: Obtain the current flow rate, system pressure, guide vane opening, and power change rate of the water recovery unit; Obtain the current opening degree and flow characteristic parameters of the flow regulating and pressure regulating valve.

3. The method according to claim 1, characterized in that, The identification of the current system operating condition type includes: When the power drop exceeds a preset threshold, the current system operating condition is identified as a load shedding condition. When a unit switching command is received, the current system operating condition is identified as the unit operation switching condition; When the system parameters are within the normal fluctuation range, the current system operating condition is identified as the normal operating condition.

4. The method according to claim 1, characterized in that, The execution of fuzzy adaptive control includes: Calculate the flow error and the rate of change of error; The flow rate error and the rate of change of error are fuzzed. Based on a pre-set fuzzy rule base, the adjustment amount of the PID controller parameters is determined; Based on the adjustment amount, the proportional, integral, and derivative parameters of the PID controller are updated to obtain the control parameters.

5. The method according to claim 4, characterized in that, The fuzzy rule base contains 49 rules, which use an IF-THEN structure to describe the mapping relationship between flow error, error change rate and PID parameter adjustment.

6. The method according to claim 1, characterized in that, When the system operating condition is a unit operation transition condition, the multimodal control strategy includes: Adjust the opening of the flow regulating and pressure regulating valve in advance to the preset percentage of the target flow rate; The guide vanes are controlled to close gradually according to an exponential decay law; The opening of the flow regulating valve is made to track the flow rate changes of the guide vane in real time, so as to maintain the stability of the system flow rate; Monitor the pipeline pressure and adjust the valve opening to compensate when the pressure is abnormal.

7. The method according to claim 1, characterized in that, When the system operating condition is a load shedding condition, the multimodal control strategy includes: Start the traffic tiered interception program; Perform multi-level traffic reduction at preset time intervals; After each flow rate adjustment, fine-tuning is performed based on the pressure feedback signal; Once the traffic has stabilized, the system will switch to a fine-tuning phase to gradually restore it to a steady state.

8. The method according to claim 7, characterized in that, The multi-level traffic reduction includes: The first stage involves rapidly closing the valve to 70% of the current flow rate; The second level, after a preset time, adjusts to 50% of the current flow rate based on pressure feedback; After the third level continues for the preset time, it is adjusted to the final target flow rate.

9. The method according to claim 1, characterized in that, Also includes: Monitor real-time changes in system flow and pressure; When flow overshoot or pressure fluctuations exceed the safety threshold, the emergency adjustment mechanism is triggered. The emergency adjustment mechanism includes temporarily increasing the derivative parameter of the PID controller and decreasing the proportional parameter to suppress system oscillations.

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