A hydroelectric power station oil press device and automatic air supplement method

Abstract

The application discloses a kind of hydroelectric power station pressure oil device and automatic air supply method, device at least includes pressure oil tank and control unit, pressure oil tank is equipped with multiple parameter acquisition module, control unit is connected with multiple parameter acquisition module data, multiple parameter acquisition module at least includes pressure sensor, oil level gauge, temperature sensor, air supply valve opening degree instrument and gas flowmeter.Method through multiple parameter fusion real-time calculation temperature compensation after predicted pressure, and with pressure deviation, deviation change rate and oil level change rate as input, by fuzzy reasoning self-adapting setting PID parameter, closed-loop regulation air supply valve opening degree, realize accurate air supply.The application eliminates the misjudgment caused by temperature drift and oil level fluctuation, realizes the high matching of air supply amount and demand, reduces artificial intervention to near zero, reduces compressed air energy consumption, improves the stability, energy efficiency and reliability of hydroelectric power station pressure oil system.

Description

Technical Field

[0001] This invention relates to the field of hydropower station technology, and in particular to a hydropower station oil pressurization device and an automatic air replenishment method. Background Technology

[0002] The core energy storage device of hydropower station oil pressure device speed regulation system, cylindrical valve and other equipment, the internal oil and gas balance is crucial to the system pressure stability, and pressure stability is generally maintained by gas replenishment.

[0003] Traditional gas replenishment methods often rely on manual operation or simple pressure and liquid level threshold control, which has the following problems:

[0004] 1. Low stability: A single pressure sensor is easily affected by oil level fluctuations, which can lead to inaccurate judgment of the timing of air replenishment, potentially causing a sudden drop in pressure or over-replenishment of air.

[0005] 2. Low energy efficiency: Dynamic factors such as ambient temperature and gas solubility are not considered during the gas replenishment process, resulting in redundant or insufficient gas replenishment.

[0006] 3. Poor reliability: Frequent manual intervention, low degree of automation, and easy to cause system failure due to operational errors.

[0007] 4. Low level of intelligence: Traditional gas replenishment methods are based on pressure feedback or adopt timed gas replenishment strategies, which cannot solve the problem of dynamic oil-gas balance. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides a hydropower station oil pressure device and an automatic air replenishment method. By real-time monitoring of multi-dimensional parameters such as pressure, oil level, and temperature, combined with a dynamic prediction model, precise air replenishment is achieved, ensuring the long-term stable operation of the hydropower station's oil pressure tank.

[0009] This invention provides a hydraulic oil pressurization device for hydropower stations, the specific technical solution of which is as follows:

[0010] It includes at least a pressure oil tank and a control unit. The pressure oil tank is equipped with a multi-parameter acquisition module. The control unit is connected to the multi-parameter acquisition module for data transfer. The multi-parameter acquisition module includes at least a pressure sensor, an oil level gauge, a temperature sensor, a gas supply valve opening gauge, and a gas flow meter.

[0011] The pressure sensor is used to collect the gas pressure P inside the oil tank in real time.

[0012] The oil level gauge is used to collect the oil level height H in real time;

[0013] The temperature sensor is used to collect the ambient temperature T in real time;

[0014] The air supply valve opening gauge is used to collect the valve opening degree Kp of the air supply valve in real time;

[0015] The gas flow meter is used to collect the gas replenishment volume Q in real time, provide the real-time gas replenishment volume Q to the dynamic balance prediction model, and then correct the gas mole number n online to achieve model self-calibration.

[0016] This invention also discloses an automatic air replenishment method, based on the aforementioned hydropower station oil pressurization device, the method comprising:

[0017] S1: Real-time acquisition of internal gas pressure P, oil level H, and ambient temperature T in the pressure tank;

[0018] S2: Construct a dynamic equilibrium prediction model and input the gas pressure P inside the pressure tank, the oil level H, and the ambient temperature T into the model to obtain the predicted pressure after temperature compensation. ;

[0019] S3: Set pressure Ps, obtain the predicted pressure after temperature compensation. The difference ΔP between the set pressure Ps and the set pressure;

[0020] S4: Obtain the rate of change of ΔP over time, the rate of change of oil level, and the current valve opening. Input these into the fuzzy PID control algorithm. Based on the fuzzy subset and fuzzy rules, update the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller. The PID controller then outputs the air replenishment valve control signal to adjust the air replenishment valve opening in a closed loop, making ΔP approach zero.

[0021] Furthermore, the dynamic equilibrium prediction model is based on the ideal gas law and incorporates a temperature compensation factor. To build.

[0022] Furthermore, the construction process of the dynamic equilibrium prediction model is as follows:

[0023] S201: Based on the ideal gas law and the relationship between oil and gas volume, a dynamic equation for the pressure of the oil tank is established. The dynamic equation for the pressure of the oil tank is expressed as follows:

[0024]

[0025] in, Let t be the pressure inside the oil tank, n be the number of moles of gas, and R be the gas constant. Let Vt be the real-time temperature at time t, and V0 be the total volume of the tank. Let t be the oil level height in the pressure tank at time t, and A be the cross-sectional area of ​​the pressure tank.

[0026] S202: Introducing a temperature compensation factor The correction is as follows:

[0027]

[0028] in, Indicates reference temperature. This represents the predicted pressure after temperature compensation.

[0029] Furthermore, the temperature compensation factor [0.002, 0.005] K -1 .

[0030] Furthermore, the fuzzy subset is specifically as follows:

[0031] ΔP: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large};

[0032] dΔP / dt: {rapid decrease, slow decrease, stable, slow increase, rapid increase};

[0033] dH / dt: {Rapid rise, stable, rapid fall}.

[0034] Furthermore, step S5 also includes adaptive adjustment of PID parameters;

[0035] Specifically, the proportional coefficient is adjusted to control the valve opening based on the magnitude of ΔP; the integral coefficient is adjusted based on the cumulative deviation; and the derivative coefficient is adjusted based on the rate of change of deviation.

[0036] Furthermore, the adjustment of the proportional coefficient valve opening based on the magnitude of ΔP is as follows:

[0037] When ΔP is a positive value of the fuzzy subset, increase the valve opening Kp to quickly approach the target opening value; when ΔP is a negative value of the fuzzy subset, decrease the valve opening Kp to avoid overshoot.

[0038] Furthermore, the integral coefficient is adjusted based on the accumulated deviation, as follows:

[0039] When ΔP is a positive value of the fuzzy subset, Ki is reduced to avoid premature accumulation of integral terms leading to overshoot.

[0040] When ΔP is a negative value of the fuzzy subset and the rate of change of the deviation is a rapid decrease of the fuzzy subset, increase Ki to accelerate the elimination of steady-state error.

[0041] When the rate of change of oil level increases rapidly in the fuzzy subset, Ki is reduced to avoid conflict between the integral term and the change of oil level.

[0042] Furthermore, the adjustment of the differential coefficient based on the rate of change of deviation is as follows:

[0043] When the rate of change of the deviation increases rapidly in the fuzzy subset, increasing Kd enhances the suppression effect;

[0044] When the rate of change of oil level is rapidly increasing in the fuzzy subset, increase Kd;

[0045] When the pressure deviation is zero in the fuzzy subset, reduce Kd to avoid differential noise interference.

[0046] The beneficial effects of this invention are as follows:

[0047] 1. This invention integrates five-dimensional parameters—pressure, oil level, temperature, valve opening, and flow rate—eliminating misjudgments caused by oil level fluctuations or temperature drift from a single sensor. This significantly improves the accuracy of air replenishment timing, greatly reduces system pressure fluctuations, avoids sudden pressure drops or over-replenishment, and solves the problem of poor stability in traditional air replenishment methods.

[0048] 2. This invention constructs a dynamic equilibrium prediction model based on the ideal gas law and introduces a temperature compensation factor, which enables advance prediction of the actual gas demand, improves the matching degree between the replenished gas volume and the demand, reduces ineffective replenishment, and lowers compressed air energy consumption.

[0049] 3. This invention uses a fuzzy PID parameter adaptive adjustment strategy to correct the pressure in real time according to the working conditions. Even in scenarios with rapid changes in oil level or sudden temperature changes, it can still maintain the target pressure without overshoot or oscillation. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0051] The technical solutions in the embodiments of the present invention are clearly and completely described in the following description. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0052] In the description of the embodiments of the present invention, it should be noted that the indicated orientation or positional relationship is based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is conventionally placed during use, or the orientation or positional relationship in which those skilled in the art conventionally understand it during use. This is only for the convenience of describing the present invention and simplifying the description, and is not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of the present invention. Furthermore, the terms "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0053] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0054] Example 1

[0055] Embodiment 1 of the present invention discloses a hydraulic oil pressurization device for a hydropower station, which is as follows: the device includes at least a hydraulic oil pressurization tank and a control unit. The hydraulic oil pressurization tank is equipped with a multi-parameter acquisition module. The control unit is connected to the multi-parameter acquisition module for data transfer. The control unit embeds a dynamic balance prediction model and a fuzzy PID algorithm to execute the control flow of the method described in Embodiment 1. The multi-parameter acquisition module includes at least a pressure sensor, an oil level gauge, a temperature sensor, a gas supply valve opening meter, and a gas flow meter.

[0056] The pressure sensor is used to collect the gas pressure P inside the oil tank in real time; the oil level gauge is used to collect the oil level H in real time; the temperature sensor is used to collect the ambient temperature T in real time; the gas replenishment valve opening meter is used to collect the valve opening Kp of the gas replenishment valve in real time; and the gas flow meter is used to collect the gas replenishment amount Q in real time, providing the dynamic balance prediction model with the real-time gas replenishment amount Q, thereby correcting the gas molar number n online and realizing model self-calibration.

[0057] Example 2

[0058] Embodiment 2 of the present invention discloses an automatic air replenishment method, based on the hydropower station oil pressurization device described in Embodiment 1 above, such as... Figure 1 As shown, the specific steps are as follows:

[0059] S1: Real-time acquisition of internal gas pressure P, oil level H, and ambient temperature T in the pressure tank;

[0060] S2: Construct a dynamic equilibrium prediction model and input the gas pressure P inside the pressure tank, the oil level H, and the ambient temperature T into the model to obtain the predicted pressure after temperature compensation. ;

[0061] As a preferred embodiment, the dynamic equilibrium prediction model is based on the ideal gas equation of state and incorporates a temperature compensation factor. To build;

[0062] By introducing a temperature compensation factor This avoids the nonlinear effect of temperature changes on the solubility of gases in the oil tank on pressure changes.

[0063] Specifically, the temperature compensation factor It can be obtained through experimental calibration, and no specific limitation is made here. In this embodiment, the temperature compensation factor is... [0.002, 0.005] K -1 .

[0064] The construction process of the dynamic equilibrium prediction model is as follows:

[0065] S201: Based on the ideal gas law and the relationship between oil and gas volume, a dynamic equation for the pressure of the oil tank is established. The dynamic equation for the pressure of the oil tank is expressed as follows:

[0066]

[0067] in, Let t be the pressure inside the oil tank, n be the number of moles of gas, and R be the gas constant. Let Vt be the real-time temperature at time t, and V0 be the total volume of the tank. Let t be the oil level height in the pressure tank at time t, and A be the cross-sectional area of ​​the pressure tank.

[0068] S202: Introducing a temperature compensation factor The correction is as follows:

[0069]

[0070] in, Indicates reference temperature. This represents the predicted pressure after temperature compensation.

[0071] In this embodiment, the reference temperature is the base temperature, i.e., 25°C.

[0072] S3: Set pressure Ps, obtain the predicted pressure after temperature compensation. The pressure deviation ΔP is the difference between the current pressure and the set pressure Ps. ΔP reflects the degree to which the current pressure deviates from the target and is a direct feedback signal of the control system. For example, if ΔP > 0, it indicates insufficient pressure and air replenishment is required; if ΔP < 0, air venting or air replenishment should be stopped.

[0073] S4: Obtain the rate of change of ΔP over time, the rate of change of oil level, and the current valve opening. Input these into the fuzzy PID control algorithm. Based on the fuzzy subset and fuzzy rules, generate the PID parameter increments ΔKp, ΔKi, and ΔKd. Update the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller based on ΔKp, ΔKi, and ΔKd. The PID controller then outputs the air replenishment valve control signal to adjust the air replenishment valve opening in a closed loop, making ΔP approach zero.

[0074] Specifically, the deviation change rate dΔP / dt = the rate of change of pressure deviation per unit time, is used to predict future pressure change trends; for example, if ΔP is negative and dΔP / dt continues to increase, it indicates that the pressure is about to exceed the limit and gas injection needs to be suppressed in advance.

[0075] The rate of change of oil level dH / dt = the time derivative of oil level height, which reflects the impact of dynamic changes in oil volume on gas volume. For example, a rapid rise in oil level (dH / dt > 0) will compress the gas space, causing a sudden increase in pressure, requiring timely adjustment of the gas replenishment strategy.

[0076] As a preferred embodiment, the fuzzy subset is specifically as follows:

[0077] Pressure deviation ΔP: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large};

[0078] The rate of change of deviation dΔP / dt: {rapid decrease, slow decrease, stable, slow increase, rapid increase};

[0079] Oil level change rate dH / dt: {rapid rise, stable, rapid fall}.

[0080] In a preferred embodiment, step S5 further includes adaptive adjustment of PID parameters;

[0081] Specifically, the response speed is adjusted according to the magnitude of ΔP. For example, when ΔP is large, the valve opening Kp is increased to quickly approach the target opening value; when ΔP is small, the valve opening Kp is decreased to avoid overshoot.

[0082] Specifically, when ΔP is a positive value of the fuzzy subset, the valve opening Kp is increased; when ΔP is a negative value of the fuzzy subset, the valve opening Kp is decreased.

[0083] Adjust the steady-state error elimination capability based on the cumulative deviation; for example, enhance the integral action when the long-term pressure is insufficient.

[0084] Specifically, when ΔP is large, that is, when ΔP is a positive value of the fuzzy subset, Ki is reduced to avoid premature accumulation of integral terms leading to overshoot.

[0085] The rule is: IF ΔP = positive THEN decrease Ki.

[0086] When ΔP is small but persists, and when ΔP is a negative value of the fuzzy subset and the rate of change of the deviation is a rapid decrease of the fuzzy subset, increase Ki to accelerate the elimination of steady-state error.

[0087] The rule is: IF ΔP = positively small and dΔP / dt≈0 THEN, increase Ki.

[0088] When the oil level changes rapidly (dH / dt is large), that is, when the rate of change of oil level is a rapid increase of the fuzzy subset, the gas volume change is predicted, the excessive effect of Ki is suppressed, Ki is appropriately reduced, and the integral term is avoided from conflicting with the oil level change.

[0089] The rule is: IF dH / dt = rapidly increases THEN, then appropriately decreases Ki.

[0090] This adaptive adjustment is generally applied in the following scenarios:

[0091] For persistently low pressure (ΔP remains positive): Increase Ki to enhance the integral effect and quickly replenish Qi;

[0092] Frequent pressure fluctuations (drastic changes in dΔP / dt): Reduce Ki to decrease integral accumulation and avoid oscillations.

[0093] Adjust the damping effect according to the rate of change of deviation; for example, increase Kd to suppress oscillations when pressure fluctuates rapidly.

[0094] Specifically, when the pressure deviates rapidly from the target (dΔP / dt is large), that is, when the rate of change of the deviation is rapidly increasing in the fuzzy subset, Kd is increased to enhance the inhibition effect;

[0095] The rule is: IF dΔP / dt = rapidly increases THEN Kd.

[0096] When the oil level rises rapidly (dH / dt>0), that is, when the rate of change of the oil level is a rapid increase of the fuzzy subset, the pressure surge caused by gas volume compression is predicted, and Kd is increased in advance.

[0097] The rule is: IF dH / dt = rapidly increases THEN significantly increases Kd.

[0098] When the system approaches steady state (ΔP≈0), that is, when the pressure deviation is zero in the fuzzy subset, reduce Kd to avoid differential noise interference;

[0099] The rule is: IF ΔP = zero and dΔP / dt ≈ 0 THEN, decrease Kd.

[0100] This adaptive adjustment is generally applied in the following scenarios:

[0101] Sudden pressure drop (ΔP suddenly increases): suppress overshoot of the air supply valve by increasing Kd;

[0102] Sudden drop in oil level (negative change in dH / dt): Predicting that gas volume expansion will cause a pressure drop, increase Kd to slow down the closing speed of the gas replenishment valve and prevent a sudden pressure drop.

[0103] Based on the above method and process, the following example is provided.

[0104] Assume the current state of the pressure oil tank system of a governor in a hydropower station is as follows:

[0105] ΔP = +10 kPa (insufficient pressure); dΔP / dt = +2 kPa / s (pressure deviation is widening); dH / dt = +0.05 m / s (oil level is rising rapidly).

[0106] Fuzzy reasoning and parameter adjustment process:

[0107] Blur:

[0108] ΔP is mapped to "positive and large", dΔP / dt is mapped to "positive and large", and dH / dt is mapped to "rapid rise".

[0109] Triggering rules:

[0110] If ΔP = PB and dΔP / dt = PB and dH / dt = rapid increase, then:

[0111] Kp = Maximum (rapid Qi replenishment);

[0112] Ki = Minimal (to avoid overcompensation of the integral term due to rising oil level);

[0113] Kd = large (suppresses the tendency of sudden pressure rise caused by rising oil level).

[0114] Ultimately, the air replenishment valve is opened significantly, but the integral action is limited while the derivative action is enhanced, achieving the effect of rapid air replenishment while avoiding pressure runaway due to rising oil level.

[0115] This invention is not limited to the specific embodiments described above. The invention extends to any new feature or combination disclosed in this specification, as well as any new method or process step or combination disclosed herein.

Claims

1. An automatic air replenishment method, characterized in that, Based on the hydropower station oil pressure device, the hydropower station oil pressure device includes at least an oil pressure tank and a control unit. The oil pressure tank is equipped with a multi-parameter acquisition module. The control unit is connected to the multi-parameter acquisition module for data transfer. The multi-parameter acquisition module includes at least a pressure sensor, an oil level gauge, a temperature sensor, a gas supply valve opening meter, and a gas flow meter. The methods include: S1: Real-time acquisition of internal gas pressure P, oil level H, and ambient temperature T in the pressure tank; S2: Construct a dynamic equilibrium prediction model and input the gas pressure P inside the pressure tank, the oil level H, and the ambient temperature T into the model to obtain the predicted pressure after temperature compensation. ; S3: Set pressure Ps, obtain the predicted pressure after temperature compensation. The difference ΔP between the set pressure Ps and the set pressure; S4: Obtain the rate of change of ΔP over time, the rate of change of oil level, and the current valve opening, input them into the fuzzy PID control algorithm, update the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller according to the fuzzy subset and fuzzy rules, and output the air replenishment valve control signal from the PID controller to adjust the air replenishment valve opening in a closed loop so that ΔP approaches zero. The fuzzy subset is specifically as follows: ΔP: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}; dΔP / dt: {rapid decrease, slow decrease, stable, slow increase, rapid increase}; dH / dt: {Rapid rise, stable, rapid fall}.

2. The automatic air replenishment method according to claim 1, characterized in that, In step S2, the dynamic equilibrium prediction model is based on the ideal gas law and incorporates a temperature compensation factor. To build.

3. The automatic air replenishment method according to claim 2, characterized in that, The construction process of the dynamic equilibrium prediction model is as follows: S201: Based on the ideal gas law and the relationship between oil and gas volume, a dynamic equation for the pressure of the oil tank is established. The dynamic equation for the pressure of the oil tank is expressed as follows: in, Let t be the pressure inside the oil tank, n be the number of moles of gas, and R be the gas constant. Let Vt be the real-time temperature at time t, and V0 be the total volume of the tank. Let t be the oil level height in the pressure tank at time t, and A be the cross-sectional area of ​​the pressure tank. S202: Introducing a temperature compensation factor The correction is as follows: in, Indicates reference temperature. This represents the predicted pressure after temperature compensation.

4. The automatic air replenishment method according to claim 3, characterized in that, The temperature compensation factor [0.002, 0.005] K -1 .

5. The automatic air replenishment method according to any one of claims 1-4, characterized in that, Step S5 also includes adaptive adjustment of PID parameters; Specifically, the proportional coefficient is adjusted to control the valve opening based on the magnitude of ΔP; the integral coefficient is adjusted based on the cumulative deviation; and the derivative coefficient is adjusted based on the rate of change of deviation.

6. The automatic air replenishment method according to claim 5, characterized in that, The valve opening is adjusted according to the magnitude of ΔP, as follows: When ΔP is a positive value of the fuzzy subset, increase the valve opening Kp; when ΔP is a negative value of the fuzzy subset, decrease the valve opening Kp.

7. The automatic air replenishment method according to claim 5, characterized in that, The integral coefficient is adjusted based on the accumulated deviation, as follows: When ΔP is a positive value of the fuzzy subset, decrease Ki; When ΔP is a negative value of the fuzzy subset and the rate of change of the deviation is a rapid decrease of the fuzzy subset, increase Ki. When the rate of change of oil level increases rapidly in the fuzzy subset, reduce Ki.

8. The automatic air replenishment method according to claim 5, characterized in that, The adjustment of the differential coefficient based on the rate of change of deviation is as follows: When the rate of change of the deviation increases rapidly in the fuzzy subset, increase Kd; When the rate of change of oil level is rapidly increasing in the fuzzy subset, increase Kd; When the pressure deviation is zero for the fuzzy subset, reduce Kd.

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