Self-adaptive control system for distributed variable frequency pump of heating station

Abstract

The invention discloses a self-adaptive control system for a distributed variable frequency pump of a heating station. And the data acquisition module is used for monitoring operation parameters of the heat supply system of the heating station in real time, and the operation parameters comprise the water supply temperature, the water return temperature, the water supply and return pressure difference, the environment temperature and the user side flow. By adopting the distributed variable frequency pump control and the self-adaptive algorithm, the running frequency of the pump can be automatically adjusted according to the real-time heat supply load change, the throttling loss of the adjusting valve is avoided, and the system energy consumption is remarkably reduced. The system intelligently coordinates operation of a plurality of variable frequency pumps, so that accurate distribution of pipe network flow and pressure balance are achieved, and heat supply quality and comfort are improved. The self-adaptive capacity of the system enables the system to cope with pipe network characteristic changes and external condition disturbance, efficient and stable operation is kept, and the manual intervention requirement is reduced. Energy consumption statistics and remote monitoring functions provided by the system provide convenience for operation management, and further optimization of system operation strategies is facilitated.

Description

Technical Field

[0001] This application relates to the field of heating station technology, specifically to an adaptive control system for a distributed variable frequency pump in a heating station. Background Technology

[0002] Traditional heating station systems typically employ centralized fixed-speed pumps combined with regulating valves for flow and pressure control. This approach suffers from significant energy waste, as the regulating valves alter the network's characteristic curve through throttling, resulting in substantial energy consumption at the valve's throttling losses and overall system inefficiency. Furthermore, because the heating load constantly changes with ambient temperature and time, the fixed-speed pumps cannot adapt in real time, leading to unstable heating quality and the potential for localized overheating or undercooling. While the application of distributed variable frequency pump technology has improved system regulation capabilities to some extent, existing control systems often employ fixed-parameter PID control, which struggles to handle dynamic changes in the network's characteristics. The lack of coordinated optimization among pumps further hinders improvements in system operating efficiency. Summary of the Invention

[0003] Therefore, this application provides a distributed variable frequency pump adaptive control system for heating stations to solve the problems in the prior art that it is difficult to cope with changes in the dynamic characteristics of the pipeline network and that there is a lack of coordinated optimization among the pumps.

[0004] To achieve the above objectives, this application provides the following technical solution:

[0005] An adaptive control system for a distributed variable frequency pump in a heating station includes the following modules;

[0006] The data acquisition module is used to monitor the operating parameters of the heating station's heating system in real time. The operating parameters include supply water temperature, return water temperature, supply and return water pressure difference, ambient temperature, and user-side flow rate.

[0007] The data processing module, connected to the data acquisition module, is used to filter and extract features from the acquired operating parameters, and calculate the actual heat load demand and pipeline resistance characteristics of the system.

[0008] The control decision module is connected to the data processing module. It uses a fuzzy neural network algorithm to establish a system model and generates a variable frequency pump control strategy based on the actual heat load demand and pipeline resistance characteristics.

[0009] The pump control execution module is connected to the control decision module, which converts the control strategy into the frequency control signal of the frequency converter to drive the distributed frequency pump to run.

[0010] The system coordination module connects the control decision module and the pump control execution module, and is used to coordinate the operating relationship between multiple distributed variable frequency pumps.

[0011] The human-computer interaction module connects the data processing module and the control decision module, and provides a system parameter setting interface and operating status display.

[0012] The communication module connects various modules in the system, enabling data exchange and communication between modules.

[0013] Preferably, the data acquisition module includes a temperature sensor group, a pressure sensor group, and a flow sensor group. The temperature sensor group includes a supply water temperature sensor installed on the main supply water pipeline, a return water temperature sensor installed on the main return water pipeline, and an ambient temperature sensor installed outside the heating station. The pressure sensor group includes a supply water pressure sensor installed on the main supply water pipeline and a return water pressure sensor installed on the main return water pipeline. The flow sensor group includes an electromagnetic flow meter installed on the user-side main pipeline.

[0014] The data processing module uses a combination of moving average filtering and wavelet transform to process the collected data;

[0015] The control decision module includes a system identification unit, a load prediction unit, and an optimization calculation unit. The system identification unit uses the recursive least squares method to identify the pipeline network resistance characteristic parameters online. The load prediction unit predicts the heat load demand for a future period based on historical data and weather forecast information. The optimization calculation unit uses the minimum total energy consumption of the system as the objective function and, combined with the hydraulic constraints of the pipeline network, calculates the optimal operating frequency of each variable frequency pump.

[0016] The pump control execution module includes a frequency conversion unit and a pump control protection unit. The frequency conversion unit converts the frequency command output by the optimization calculation unit into an analog signal or bus communication signal that the frequency converter can receive. The pump control protection unit monitors the operating current, voltage and temperature parameters of the variable frequency pump and performs protection actions when an abnormality occurs.

[0017] Preferably, the system coordination module includes a hydraulic balance calculation unit and a pump group optimization unit. The hydraulic balance calculation unit calculates the resistance characteristics of each branch pipeline and identifies hydraulic imbalance based on the pipeline network topology and real-time monitoring data. The pump group optimization unit performs multi-objective optimization based on a genetic algorithm to coordinate the operating status of each variable frequency pump, so that the system can minimize total energy consumption while meeting heating demand.

[0018] Preferably, the pump group optimization unit adopts a distributed optimization architecture, each variable frequency pump controller has a local optimization function, and each variable frequency pump controller exchanges information through a communication network at the same time.

[0019] Preferably, the human-machine interaction module adopts a touch screen display, providing a graphical system flowchart interface, displaying temperature, pressure, flow data and variable frequency pump operating status at each measuring point in real time, and providing parameter settings, operating mode selection and alarm information query.

[0020] Preferably, the human-computer interaction module also provides an energy consumption statistics report function, which can calculate the total energy consumption of the system, the power consumption of each pump and the energy saving by day, week, month and year, and generate trend curves and comparison charts.

[0021] Preferably, the communication module adopts a communication method combining industrial Ethernet and PROFIBUS-DP bus. Industrial Ethernet is used for data communication between modules in the control layer, and PROFIBUS-DP bus is used to connect the inverter and sensor field devices.

[0022] Preferably, it also includes an energy optimization module, which is connected to the data processing module and the control decision module. The energy optimization module performs load adjustment optimization based on real-time electricity price information and system thermal inertia characteristics. During peak electricity price periods, the heating load is reduced to maintain heating demand by utilizing system thermal inertia. During off-peak electricity price periods, the heating load is increased to store heat for use during peak periods.

[0023] Preferably, the energy optimization module adopts a model predictive control algorithm with a 24-hour optimization cycle, and generates the optimal operation strategy by comprehensively considering weather forecasts, electricity price curves and user comfort requirements.

[0024] Preferably, it also includes a fault diagnosis and fault-tolerant control module, which is connected to the data acquisition module and the control decision module. By analyzing the operating data, it identifies sensor faults, pump performance degradation and pipeline leakage abnormalities, and automatically switches to fault-tolerant control mode when a fault occurs.

[0025] Compared with the prior art, this application has at least the following beneficial effects:

[0026] This invention employs distributed variable frequency pump control and adaptive algorithms to automatically adjust the pump's operating frequency according to real-time heating load changes, avoiding throttling losses from regulating valves and significantly reducing system energy consumption.

[0027] The system achieves precise distribution of pipeline flow and pressure balance by intelligently coordinating the operation of multiple variable frequency pumps, thereby improving heating quality and comfort.

[0028] The system's adaptive capabilities enable it to cope with changes in pipeline characteristics and external disturbances, maintain efficient and stable operation, and reduce the need for manual intervention.

[0029] The system's energy consumption statistics and remote monitoring functions facilitate operation and management, and are conducive to further optimizing system operation strategies. Attached Figure Description

[0030] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0031] Figure 1 This is a block diagram of an adaptive control system for a distributed variable frequency pump in a heating station according to this application. Detailed Implementation

[0032] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] like Figure 1 As shown, this application discloses an adaptive control system for a distributed variable frequency pump in a heating station, which includes the following modules;

[0034] The data acquisition module is used to monitor the operating parameters of the heating station's heating system in real time. The operating parameters include supply water temperature, return water temperature, supply and return water pressure difference, ambient temperature, and user-side flow rate.

[0035] The data processing module, connected to the data acquisition module, is used to filter and extract features from the acquired operating parameters, and calculate the actual heat load demand and pipeline resistance characteristics of the system.

[0036] The control decision module is connected to the data processing module. It uses a fuzzy neural network algorithm to establish a system model and generates a variable frequency pump control strategy based on the actual heat load demand and pipeline resistance characteristics.

[0037] The pump control execution module is connected to the control decision module, which converts the control strategy into the frequency control signal of the frequency converter to drive the distributed frequency pump to run.

[0038] The system coordination module connects the control decision module and the pump control execution module, and is used to coordinate the operating relationship between multiple distributed variable frequency pumps to optimize flow distribution;

[0039] The human-computer interaction module connects the data processing module and the control decision module, and provides a system parameter setting interface and a running status display function.

[0040] The communication module connects various modules in the system, enabling data exchange and communication between modules.

[0041] In the implementation of this invention, various sensors in the data acquisition module monitor system operating parameters such as supply / return water temperature, pressure, flow rate, and ambient temperature in real time. After filtering and feature extraction by the data processing module, these data are used to calculate the actual heat load demand and pipeline resistance characteristics. The control decision module, based on a fuzzy neural network algorithm, generates an optimal frequency control strategy with the goal of minimizing total system energy consumption through system identification, load prediction, and optimization calculation units. The pump control execution module converts this strategy into inverter control signals to drive the distributed variable frequency pumps. At the same time, the system coordination module optimizes multi-pump collaboration, the human-machine interaction module provides an operating interface, and the communication module ensures data exchange between modules. Ultimately, this achieves automatic adjustment of pump frequency based on real-time operating conditions, thereby achieving the goals of high efficiency, energy saving, and stable heating.

[0042] The data acquisition module includes a temperature sensor group, a pressure sensor group, and a flow sensor group. The temperature sensor group includes a supply water temperature sensor installed on the main supply water pipeline, a return water temperature sensor installed on the main return water pipeline, and an ambient temperature sensor installed outdoors at the heating station. The pressure sensor group includes a supply water pressure sensor installed on the main supply water pipeline and a return water pressure sensor installed on the main return water pipeline. The flow sensor group includes an electromagnetic flow meter installed on the user-side main pipeline, thereby enabling more accurate data monitoring.

[0043] The data processing module uses a combination of moving average filtering and wavelet transform to process the collected data in order to eliminate measurement noise and interference signals.

[0044] The control decision module includes a system identification unit, a load forecasting unit, and an optimization calculation unit. The system identification unit uses the recursive least squares method to identify the pipeline network resistance characteristic parameters online. The load forecasting unit predicts the heat load demand for a future period based on historical data and weather forecast information. The optimization calculation unit uses the minimum total system energy consumption as the objective function and, combined with the hydraulic constraints of the pipeline network, calculates the optimal operating frequency of each variable frequency pump. The pump control execution module includes a frequency conversion unit and a pump control protection unit. The frequency conversion unit converts the frequency command output by the optimization calculation unit into an analog signal or bus communication signal acceptable to the frequency converter. The pump control protection unit monitors the operating current, voltage, and temperature parameters of the variable frequency pump and executes protection actions when abnormalities occur.

[0045] The system coordination module includes a hydraulic balance calculation unit and a pump group optimization unit. The hydraulic balance calculation unit calculates the resistance characteristics of each branch pipeline based on the pipeline network topology and real-time monitoring data, and identifies hydraulic imbalances. The pump group optimization unit performs multi-objective optimization based on a genetic algorithm to coordinate the operating status of each variable frequency pump, so that the system can minimize total energy consumption while meeting heating demand.

[0046] The hydraulic balance calculation unit analyzes the resistance of each branch based on the pipeline network topology and real-time data to identify hydraulic imbalances. The pump group optimization unit uses a genetic algorithm for multi-objective optimization, coordinating the operation of multiple pumps to minimize total energy consumption while ensuring heating supply. For example, when the pressure difference at the end of the pipeline network is insufficient, this unit can adjust the pressure of nearby pumps to supplement the pressure, avoiding over-operation of pumps when a single pump is in use.

[0047] The pump group optimization unit adopts a distributed optimization architecture. Each variable frequency pump controller has local optimization capabilities and exchanges information through a communication network to achieve global optimization goals. Each pump controller has local optimization capabilities and shares data (such as flow rate and pressure) through the communication network to achieve global coordination. For example, when a pump detects an abnormal flow rate, it can exchange information with other pump controllers and jointly adjust the frequency to maintain the hydraulic balance of the system.

[0048] The human-machine interface module uses a touch screen display to provide a graphical system flowchart interface, which displays the temperature, pressure, and flow data of each measuring point and the operating status of the variable frequency pump in real time. It also provides parameter setting, operating mode selection and alarm information query functions to facilitate users to view the operating conditions.

[0049] The human-computer interaction module also provides an energy consumption statistics report function, which can calculate the total energy consumption of the system, the power consumption of each pump, and the energy saving by day, week, month, and year, and generate trend curves and comparison charts to help managers analyze the energy-saving effect.

[0050] The communication module adopts a communication method that combines industrial Ethernet and PROFIBUS-DP bus. Industrial Ethernet is used for data communication between modules in the control layer, and PROFIBUS-DP bus is used to connect field devices such as frequency converters and sensors.

[0051] Industrial Ethernet is used for high-speed data exchange at the control layer (such as decision and coordination modules), while PROFIBUS-DP connects field devices (such as frequency converters and sensors) to ensure the real-time performance and reliability of data transmission.

[0052] The communication module also supports wireless communication, and can connect to a remote monitoring center via GPRS or 4G network to realize remote monitoring and fault diagnosis of the system.

[0053] It also includes an energy optimization module, which connects the data processing module and the control decision module. Based on real-time electricity price information and system thermal inertia characteristics, it performs load adjustment optimization, appropriately reduces the heating load during peak electricity price periods, utilizes system thermal inertia to maintain basic heating demand, and increases the heating load during off-peak electricity price periods to store heat for use during peak periods, thereby reducing operating costs.

[0054] The energy optimization module employs a model predictive control algorithm with a 24-hour optimization cycle, taking into account weather forecasts, electricity price curves, and user comfort requirements to generate the optimal operating strategy.

[0055] It also includes a fault diagnosis and fault-tolerant control module, which connects the data acquisition module and the control decision module. By analyzing the operating data, it identifies sensor failures, pump performance degradation and pipeline leakage anomalies, and automatically switches to fault-tolerant control mode when a failure occurs to ensure the basic operation of the system. For example, when a pressure sensor fails, the system uses other sensors to estimate the pressure, thereby maintaining the basic operation of the system until it is repaired.

[0056] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.

Claims

1. An adaptive control system for a distributed variable frequency pump in a heating station, characterized in that, Includes the following modules; The data acquisition module is used to monitor the operating parameters of the heating station's heating system in real time. The operating parameters include supply water temperature, return water temperature, supply and return water pressure difference, ambient temperature, and user-side flow rate. The data processing module, connected to the data acquisition module, is used to filter and extract features from the acquired operating parameters, and calculate the actual heat load demand and pipeline resistance characteristics of the system. The control decision module is connected to the data processing module. It uses a fuzzy neural network algorithm to establish a system model and generates a variable frequency pump control strategy based on the actual heat load demand and pipeline resistance characteristics. The pump control execution module is connected to the control decision module, which converts the control strategy into the frequency control signal of the frequency converter to drive the distributed frequency pump to run. The system coordination module connects the control decision module and the pump control execution module, and is used to coordinate the operating relationship between multiple distributed variable frequency pumps. The human-computer interaction module connects the data processing module and the control decision module, and provides a system parameter setting interface and operating status display. The communication module connects various modules in the system, enabling data exchange and communication between modules.

2. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 1, characterized in that, The data acquisition module includes a temperature sensor group, a pressure sensor group, and a flow sensor group. The temperature sensor group includes a supply water temperature sensor installed on the main supply water pipeline, a return water temperature sensor installed on the main return water pipeline, and an ambient temperature sensor installed outdoors at the heating station. The pressure sensor group includes a supply water pressure sensor installed on the main supply water pipeline and a return water pressure sensor installed on the main return water pipeline. The flow sensor group includes an electromagnetic flow meter installed on the user-side main pipeline. The data processing module uses a combination of moving average filtering and wavelet transform to process the collected data; The control decision module includes a system identification unit, a load prediction unit, and an optimization calculation unit. The system identification unit uses the recursive least squares method to identify the pipeline network resistance characteristic parameters online. The load prediction unit predicts the heat load demand for a future period based on historical data and weather forecast information. The optimization calculation unit uses the minimum total energy consumption of the system as the objective function and, combined with the hydraulic constraints of the pipeline network, calculates the optimal operating frequency of each variable frequency pump. The pump control execution module includes a frequency conversion unit and a pump control protection unit. The frequency conversion unit converts the frequency command output by the optimization calculation unit into an analog signal or bus communication signal that the frequency converter can receive. The pump control protection unit monitors the operating current, voltage and temperature parameters of the variable frequency pump and performs protection actions when an abnormality occurs.

3. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 1, characterized in that, The system coordination module includes a hydraulic balance calculation unit and a pump group optimization unit. The hydraulic balance calculation unit calculates the resistance characteristics of each branch pipeline based on the pipeline network topology and real-time monitoring data, and identifies hydraulic imbalances. The pump group optimization unit performs multi-objective optimization based on a genetic algorithm to coordinate the operating status of each variable frequency pump, so that the system can minimize total energy consumption while meeting heating demand.

4. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 3, characterized in that, The pump group optimization unit adopts a distributed optimization architecture. Each variable frequency pump controller has a local optimization function, and each variable frequency pump controller exchanges information through a communication network.

5. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 3, characterized in that, The human-machine interaction module uses a touch screen display to provide a graphical system flowchart interface, which displays the temperature, pressure, and flow data of each measuring point and the operating status of the variable frequency pump in real time. It also provides parameter settings, operating mode selection, and alarm information query.

6. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 5, characterized in that, The human-computer interaction module also provides an energy consumption statistics report function, which can calculate the total energy consumption of the system, the power consumption of each pump, and the energy saving by day, week, month, and year, and generate trend curves and comparison charts.

7. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 3, characterized in that, The communication module adopts a communication method that combines industrial Ethernet and PROFIBUS-DP bus. Industrial Ethernet is used for data communication between modules in the control layer, and PROFIBUS-DP bus is used to connect the inverter and sensor field devices.

8. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 1, characterized in that, It also includes an energy optimization module, which connects the data processing module and the control decision module. The energy optimization module performs load adjustment optimization based on real-time electricity price information and system thermal inertia characteristics. It reduces the heating load during peak electricity price periods and uses system thermal inertia to maintain heating demand. It increases the heating load during off-peak electricity price periods and stores heat for use during peak periods.

9. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 8, characterized in that, The energy optimization module employs a model predictive control algorithm with a 24-hour optimization cycle, taking into account weather forecasts, electricity price curves, and user comfort requirements to generate the optimal operating strategy.

10. The adaptive control system for a distributed variable frequency pump in a heating station according to claim 1, characterized in that, It also includes a fault diagnosis and fault-tolerant control module, which connects the data acquisition module and the control decision module. By analyzing the operating data, it identifies sensor faults, pump performance degradation and pipeline leakage anomalies, and automatically switches to fault-tolerant control mode when a fault occurs.

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