CN-122018609-A - Self-adaptive PID control system for water conservancy reservoir environment

CN122018609ACN 122018609 ACN122018609 ACN 122018609ACN-122018609-A

Abstract

The invention relates to the technical field of water conservancy automation control and edge calculation, in particular to a self-adaptive PID control system for a water conservancy reservoir environment, which comprises an edge calculation gateway, a water level sensor, a flow sensor and a servo executing mechanism; the method comprises the steps that a topology mapping module obtains fluid static geometrical parameters and establishes an initial state matrix of a fluid momentum state observer, a momentum decoupling module inputs dam front water level, upstream inflow water flow and opening data to update the state matrix, transient fluctuation energy characteristics and real reserve residual characteristics are generated through decoupling, a priori fitting module calculates proportional, integral and differential coefficients according to the transient fluctuation energy characteristics and the real reserve residual characteristics, and a closed loop evolution module generates control instructions and corrects bottom resistance parameters; the invention realizes the physical separation of the real reservoir capacity change and the surface wave disturbance, reduces the ineffective oscillation of the gate, and improves the control stability and the service life of the actuating mechanism under the flood peak working condition.

Inventors

WANG SHOUGUI
CHEN WEI
ZHANG CHENGFA
CHEN WENQIANG
YANG LINMEI

Assignees

安徽研控工业自动化有限公司

Dates

Publication Date: 20260512
Application Date: 20260407

Claims (9)

1. The self-adaptive PID control system for the water conservancy reservoir environment is characterized by comprising an edge computing gateway, a water level sensor, a flow sensor and a servo executing mechanism for driving a reservoir gate to act; the water level sensor, the flow sensor and the servo executing mechanism are respectively used for collecting real-time reservoir water level data, real-time warehouse-in flow data and current executing mechanism opening data, and sending the collected data to the edge computing gateway; The topology mapping module is used for obtaining preset fluid static geometrical parameters including bottom resistance parameters, constructing a fluid space topology model, and establishing an initial state matrix of a fluid momentum state observer according to the fluid space topology model; The momentum decoupling module is used for inputting the received real-time reservoir water level data, the real-time warehouse-in flow data and the current actuator opening data into the fluid momentum state observer, driving the initial state matrix to perform state update to obtain a current state matrix, and performing frequency domain decomposition on the current state matrix to separate a fast variable component representing surface waves and a slow variable component representing reservoir capacity change, so as to generate transient fluctuation energy characteristics and real reserve residual characteristics in a decoupling mode; the prior fitting module is used for calculating a proportional coefficient based on the transient fluctuation energy characteristic, calculating an integral coefficient based on the real reserve residual characteristic, and extracting the time change rate of the real-time warehouse-in flow data to calculate a differential coefficient; The closed-loop evolution module is used for generating a control instruction according to the proportional coefficient, the integral coefficient and the differential coefficient, sending the control instruction to the servo executing mechanism, and extracting steady-state deviation of the transient fluctuation energy characteristic so as to correct the bottom resistance parameter in the fluid static geometrical parameter; The servo executing mechanism is also used for receiving the control instruction and driving the reservoir gate to act.
2. The adaptive PID control system of a water conservancy reservoir environment of claim 1, wherein the topology mapping module is configured to, when constructing a fluid space topology model: dividing the reservoir fluid space into a preset number of virtual nodes; Assigning the fluid static geometry parameters to each of the virtual nodes; establishing a physical connection relation between the virtual nodes, and generating the fluid space topology model; wherein the fluid static geometry parameters further comprise a reservoir capacity curve parameter.
3. The adaptive PID control system of a water conservancy reservoir environment according to claim 1, wherein the momentum decoupling module is configured to, when decoupling to generate a transient fluctuation energy characteristic and a real reserve residual characteristic: performing frequency domain decomposition on the current state matrix of the fluid momentum state observer by adopting an orthogonal decomposition algorithm; extracting high-frequency rapid change components as the transient fluctuation energy characteristics; Extracting a low-frequency slow-change component as the real reserve residual characteristic; the transient fluctuation energy characteristic represents the kinetic energy and potential energy of the fluid surface waves, and the real reserve residual characteristic represents the difference value between the total volume of the current fluid and the target volume preset based on the scheduling requirement.
4. The adaptive PID control system of a water conservancy reservoir environment according to claim 1, wherein the prior fitting module is configured to, when calculating a scaling factor based on the transient fluctuation energy characteristics: Acquiring a preset reference proportionality coefficient and a preset attenuation factor; calculating the product of the attenuation factor and the transient fluctuation energy characteristic; Taking a natural constant as a base number, taking the negative number of the product as an index, and calculating to obtain negative exponential decay weight; Multiplying the reference scaling factor by the negative exponential decay weight to obtain the scaling factor.
5. The adaptive PID control system of a water conservancy reservoir environment according to claim 1, wherein the prior fitting module is adapted to, when calculating an integral coefficient based on the real reserve residual characteristic: Acquiring a preset reference integral coefficient; Inputting the real reserve residual characteristics into a preset Sigmoid nonlinear driving function to generate integral driving weights; Multiplying the reference integral coefficient by the integral driving weight to obtain the integral coefficient.
6. The adaptive PID control system of a water conservancy reservoir environment according to claim 1, wherein the prior fitting module is configured to, when extracting the time rate of change of the real-time input flow data to calculate the differential coefficient: calculating the time derivative of the real-time warehousing flow data in the current control period to obtain the time change rate; and taking the time change rate as a feedforward suppression compensation term, adding the feedforward suppression compensation term with a preset reference differential coefficient, and calculating to obtain the differential coefficient.
7. The adaptive PID control system of a water conservancy reservoir environment according to claim 2, wherein the closed loop evolution module is configured to, when extracting a steady state deviation of the transient fluctuation energy characteristic and correcting a bottom resistance parameter of the fluid static geometry parameters using the steady state deviation: acquiring fluctuation frequency characteristics of the transient fluctuation energy characteristics in a preset time window; Calculating a difference value between the fluctuation frequency characteristic and a preset reference frequency characteristic as the steady-state deviation; updating the bottom resistance parameter corresponding to the virtual node in the fluid space topology model according to the steady-state deviation so as to adaptively compensate the boundary morphology evolution trend of the fluid bottom.
8. An adaptive PID control system for a water conservancy reservoir environment according to claim 1, wherein the system is applied to a water conservancy reservoir environment; The real-time reservoir water level data are dam front water level data, the real-time warehouse-in flow data are upstream inflow flow data, and the current actuating mechanism opening data are reservoir gate opening data; wherein, servo actuating mechanism is hydraulic drive gate actuator or motor drive gate actuator.
9. The adaptive PID control system of a water conservancy reservoir environment of claim 7, wherein the boundary morphology evolution trend is a sediment accumulation thickness evolution trend of a reservoir bottom.

Description

Self-adaptive PID control system for water conservancy reservoir environment Technical Field The invention relates to the technical field of water conservancy automation control and edge calculation, in particular to a self-adaptive PID control system for a water conservancy reservoir environment. Background In the current water conservancy reservoir dispatching operation and maintenance environment, large and medium reservoirs, particularly valley type regulating reservoirs, can generate complex surface wave propagation and reflection phenomena during flood peak passing or rapid opening and closing of a gate, the prior art generally adopts a traditional PID control scheme, and directly carries out closed-loop regulation on single-point water level data collected by a sensor in front of a dam as feedback quantity; The method can induce frequent reciprocating actions of the gate actuating mechanism to form serious mechanical impact and control oscillation, shortens the service life of servo equipment, is difficult to cope with long-term evolution of boundary conditions caused by sediment accumulation, scouring and the like of a bottom bed in a large-inertia and long-term adjusting process, and therefore, the method can realize effective decoupling of real mass residual errors and transient fluctuation energy in a complex dynamic environment with multiple physical quantity aliasing, and realize self-adaptive optimization of control parameters according to a fluid space topology evolution rule so as to improve control stability under flood peak working conditions and the adaptive capacity of the system to the bottom bed environment evolution, and becomes a technical problem to be solved urgently. Disclosure of Invention The invention aims to provide a self-adaptive PID control system for a water conservancy reservoir environment, which solves the following technical problems: the physical separation of the real reservoir capacity change and the surface wave disturbance is realized, false fluctuation caused by waves is prevented from being misjudged as a water level error, so that ineffective oscillation and mechanical impact of a gate are reduced, the control stability under the flood peak working condition is improved, the service life of an actuating mechanism is prolonged, the long-term evolution of the reservoir bottom bed boundary can be self-adapted, and the control performance degradation caused by model aging is prevented. The aim of the invention can be achieved by the following technical scheme: A self-adaptive PID control system for a water conservancy reservoir environment comprises an edge computing gateway, a water level sensor, a flow sensor and a servo executing mechanism for driving a reservoir gate to act; the water level sensor, the flow sensor and the servo executing mechanism are respectively used for collecting real-time reservoir water level data, real-time warehouse-in flow data and current executing mechanism opening data, and sending the collected data to the edge computing gateway; The topology mapping module is used for obtaining preset fluid static geometrical parameters including bottom resistance parameters, constructing a fluid space topology model, and establishing an initial state matrix of a fluid momentum state observer according to the fluid space topology model; The momentum decoupling module is used for inputting the received real-time reservoir water level data, the real-time warehouse-in flow data and the current actuator opening data into the fluid momentum state observer, driving the initial state matrix to perform state update to obtain a current state matrix, and performing frequency domain decomposition on the current state matrix to separate a fast variable component representing surface waves and a slow variable component representing reservoir capacity change, so as to generate transient fluctuation energy characteristics and real reserve residual characteristics in a decoupling mode; the prior fitting module is used for calculating a proportional coefficient based on the transient fluctuation energy characteristic, calculating an integral coefficient based on the real reserve residual characteristic, and extracting the time change rate of the real-time warehouse-in flow data to calculate a differential coefficient; The closed-loop evolution module is used for generating a control instruction according to the proportional coefficient, the integral coefficient and the differential coefficient, sending the control instruction to the servo executing mechanism, and extracting steady-state deviation of the transient fluctuation energy characteristic so as to correct the bottom resistance parameter in the fluid static geometrical parameter; The servo executing mechanism is also used for receiving the control instruction and driving the reservoir gate to act. Further, the topology mapping module is specifically configured to, when constructing the fluid space topology