Intelligent air compressor station group control energy-saving system based on multi-machine linkage

Abstract

The invention belongs to the technical field of air compressors, and provides an air compressor station intelligent group control energy-saving system based on multi-machine linkage, which comprises a data acquisition unit, a central control unit and an execution regulation and control unit, the data acquisition unit is used for acquiring operation state parameters and environment working condition parameters of all air compressors in an air compressor station and load demand parameters of a gas using end in real time. The central control unit integrates a load prediction module, an energy efficiency evaluation module, a group control scheduling module, an equipment health monitoring module and an energy efficiency optimization feedback module, realizes minimum energy consumption scheduling under cooperative operation of multiple air compressors through high-precision sensing, dynamic modeling and multivariable optimization, has equipment abnormity early warning and model self-correction capabilities, and is high in reliability and high in reliability. Therefore, the system energy consumption can be obviously reduced, and the gas supply stability and the intelligent level are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of air compressors, and specifically relates to an intelligent group control energy-saving system for an air compressor station based on multi-machine linkage. Background Art

[0002] With the continuous improvement of industrial automation and energy efficiency requirements, as an important power source equipment, the operating efficiency and energy consumption control of an air compressor station have become key issues. The intelligent group control technology based on multi-machine linkage aims to coordinate the operating states of multiple air compressors to achieve on-demand air supply, reduce energy consumption, and improve system stability. However, the existing technologies still have deficiencies in multi-machine collaborative control strategies, real-time dynamic response capabilities, and energy efficiency optimization accuracy, making it difficult to achieve the optimal energy-saving effect under complex working conditions.

[0003] After retrieval, the "Unattended Full-automatic Control Method for Air Compressor Group in Air Compression Station" with the publication number CN113323853B was disclosed on May 5, 2023. This patent automatically adjusts the load of each unit according to the operating curve through a load distribution controller, and automatically starts and stops the compressor when the gas consumption fluctuates to maintain the stability of the header pressure. However, this solution mainly relies on the preset operating curve for load distribution, lacks the accurate prediction ability for real-time gas demand changes, and does not fully consider the energy efficiency differences between different air compressors, which may cause some equipment to operate in a non-efficient range for a long time, and the overall energy-saving effect is limited.

[0004] After retrieval, the "Method, Device and Medium for Controlling Air Compressors in an Air Compression Station" with the publication number CN114635844B was disclosed on July 15, 2022. This patent proposes to adjust the air compressor combination based on the flow rate stable time period to achieve "active control" and reduce the pressure fluctuation of the pipe network. Although this method introduces flow trend analysis, its control logic is still based on historical flow data, does not integrate multi-dimensional operating parameters (such as pressure, temperature, equipment aging status, etc.) for comprehensive decision-making, and lacks a refined regulation mechanism for dynamic load balancing during multi-machine linkage, making it difficult to meet the rapid response requirements in scenarios of sudden gas consumption peaks or valleys.

[0005] The above problems indicate that there are still obvious shortcomings in the existing air compressor group control systems in terms of intelligent prediction, multi-machine collaborative optimization, and dynamic energy efficiency management. Therefore, those skilled in the art have proposed an intelligent group control energy-saving system for an air compressor station based on multi-machine linkage to solve the problems raised in the background art. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides an intelligent group control energy-saving system for an air compressor station based on multi-machine linkage to solve the problems in the existing technology.

[0007] The intelligent group control and energy-saving system for air compressor stations based on multi-machine linkage includes a data acquisition unit, a central control unit, and an execution and control unit. The data acquisition unit is used to acquire in real time the operating status parameters, environmental conditions parameters, and load demand parameters of each air compressor in the air compressor station. The central control unit includes a load forecasting module, an energy efficiency assessment module, and a group control and scheduling module. The load forecasting module is connected to the data acquisition unit and is used to generate a gas load forecasting curve for a future preset time window based on historical load data and the current load change rate, combined with a time series analysis algorithm. The energy efficiency assessment module is connected to the data acquisition unit and the load forecasting module and is used to model the unit gas production energy consumption of each air compressor in the station under different load ranges, and calculate the expected energy efficiency index of each air compressor in the next scheduling cycle based on the current operating status and the predicted load. The group control scheduling module is connected to the energy efficiency assessment module and the load prediction module. It is used to generate the optimal air compressor start-stop combination strategy and load ratio allocation scheme based on the predicted load curve and the energy efficiency index of each air compressor. The execution control unit is connected to the central control unit and is used to receive control commands output by the group control scheduling module, and convert the control commands into start / stop signals, load / unload switching signals and frequency setting values ​​for each air compressor controller.

[0008] Furthermore, the operating status parameters include the loading / unloading status, exhaust pressure, exhaust temperature, current value, running time, and fault codes of each air compressor; the environmental condition parameters include ambient temperature, humidity, and atmospheric pressure; the load demand parameters are collected jointly by a flow meter and pressure transmitter installed on the main pipeline, reflecting the instantaneous air consumption and pressure fluctuation trend of the current air system; the start-stop combination strategy satisfies the constraint of maintaining the air pressure within the preset operating range, while minimizing the overall energy consumption of the system; the load ratio allocation scheme dynamically adjusts the load rate of each operating air compressor, making the multi-machine collaborative operation point approach the optimal area of ​​overall system energy efficiency; the execution control unit establishes a bidirectional communication connection with the local controller of each air compressor through an industrial communication bus, which adopts one of the ModbusTCP, ProfibusDP, or CANopen protocols.

[0009] Furthermore, the central control unit is also equipped with an equipment health monitoring module, which is connected to the data acquisition unit. This module analyzes the degree to which the key operating parameters of each air compressor deviate from the normal range. When the operating parameters of an air compressor exceed a preset threshold for three consecutive sampling cycles, an equipment warning signal is generated and fed back to the group control and scheduling module. After receiving the equipment warning signal, the group control and scheduling module reduces the scheduling priority of the air compressor in subsequent scheduling cycles, or isolates it from the operating sequence without affecting the stability of the gas supply pressure, and starts a standby air compressor to replace its gas supply task. The central control unit is further equipped with an energy efficiency optimization feedback loop, which is connected to the data acquisition unit and the energy efficiency evaluation module. This loop compares the actual operating energy consumption data with the energy consumption prediction value before scheduling. If the relative deviation exceeds a preset tolerance range, a model correction mechanism is triggered to correct the unit gas production energy consumption model parameters in the energy efficiency evaluation module online.

[0010] Furthermore, the flow meter and pressure transmitter in the data acquisition unit are installed on the straight section of the outlet main pipe of the air compressor station, and the distance from the nearest bend or valve is not less than ten times the pipe diameter; the local sensors of each air compressor are connected to the analog input module of the data acquisition unit through shielded cables, and the signal transmission path adopts an anti-interference wiring method to avoid parallel laying with the power cable.

[0011] Furthermore, the time series analysis algorithm used by the load prediction module is an autoregressive moving average model or a long short-term memory neural network model, and its training process is completed based on the actual load data of the air compressor station in the past several operating cycles.

[0012] Furthermore, the unit gas production energy consumption model in the energy efficiency assessment module is expressed as follows: ; in, Indicates the first Taiwanese air compressor at load rate The unit gas production energy consumption is as follows Input electrical power to it, The model generates gas flow rate; it is continuously updated through an online learning mechanism.

[0013] Furthermore, when the group control scheduling module generates the loading ratio allocation scheme, it solves the following optimization problem: ; ; ; in, This represents the number of air compressors currently participating in the scheduling. To predict gas consumption and The first Minimum and maximum allowable load rates for the air compressor.

[0014] Furthermore, the key operating parameters monitored by the equipment health monitoring module include exhaust temperature, vibration acceleration, lubricating oil pressure difference, and current harmonic distortion rate.

[0015] Furthermore, the preset tolerance range in the energy efficiency optimization feedback loop is: The model correction mechanism uses recursive least squares to update local parameters.

[0016] Furthermore, the central control unit uses an embedded industrial computer as its hardware platform and runs a real-time operating system. Its internal functional modules are deployed as independent processes, and data interaction between modules is achieved through shared memory and message queues. The load prediction module, energy efficiency assessment module, and group control scheduling module are executed sequentially according to a fixed scheduling cycle, and the execution sequence of each cycle is strictly controlled by a high-precision timer.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses a data acquisition unit to collect multi-dimensional operating parameters of the air compressor station with high precision, combines a load prediction module to dynamically model future gas demand, an energy efficiency assessment module to finely model the energy consumption characteristics of each air compressor under different loads, and a group control scheduling module to solve a multivariate optimization problem based on this, generating a start-up and shutdown and loading strategy that meets the gas supply pressure constraints and has the lowest overall energy consumption.

[0018] 2. The execution control unit of this invention accurately sends scheduling instructions to each air compressor controller through a standard industrial communication protocol, realizing multi-machine collaborative operation; the equipment health monitoring module continuously monitors key operating parameters and actively adjusts the scheduling strategy when abnormal trends occur in the equipment, avoiding system air supply interruption due to single machine failure; the energy efficiency optimization feedback loop continuously corrects the energy efficiency model parameters through deviation analysis between actual operating data and predicted values, improving the energy-saving accuracy of the system in long-term operation.

[0019] 3. This invention can complete a closed-loop control process from data perception, load prediction, energy efficiency modeling, optimized scheduling to command execution without human intervention. It effectively solves the technical defects of traditional air compressor stations, such as reliance on manual experience scheduling, lack of equipment health early warning, inability to dynamically adapt to load changes, and difficulty in achieving optimal energy efficiency through multi-machine collaboration. It significantly reduces the overall operating energy consumption of air compressor stations, extends equipment service life, and improves the stability and intelligence level of the air supply system. Attached Figure Description

[0020] Figure 1 This is a block diagram of the overall structure of the intelligent group control and energy-saving system for air compressor stations based on multi-machine linkage according to the present invention; Figure 2 A schematic diagram showing the internal functional modules and data interaction relationships of the central control unit; Figure 3 A logical flowchart for generating the optimal start / stop combination and load ratio allocation scheme for the group control scheduling module; Figure 4 A schematic diagram illustrating the communication connection between the control unit and the local controllers of each air compressor. Figure 5 A schematic diagram of the data flow for the collaborative operation of the energy efficiency optimization feedback loop and the equipment health monitoring module. Detailed Implementation

[0021] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0022] This invention provides an intelligent group control and energy-saving system for air compressor stations based on multi-machine linkage, such as... Figure 1 As shown, the system consists of three main parts: a data acquisition unit, a central control unit, and an execution and control unit. These three parts are physically connected and interact with each other via industrial-grade communication interfaces and signal cables. The data acquisition unit is deployed at the air compressor station site, adjacent to each air compressor, and is used to collect real-time operating status parameters, environmental condition parameters, and air demand parameters. The central control unit is usually installed in a standard 19-inch cabinet in the control room and serves as the decision-making core of the entire system. The execution and control unit is located in the power distribution area of ​​the air compressor station or near the local controller of each air compressor and is responsible for converting scheduling commands into executable electrical or communication signals.

[0023] See Figure 2The data acquisition unit connects to the local sensors of each air compressor via multiple analog input channels, including but not limited to exhaust pressure sensors, exhaust temperature sensors, current transformers, running timers, and fault diagnosis modules. These sensors are connected to the analog input modules of the data acquisition unit via shielded twisted-pair cables. The cable laying path strictly avoids power cables and maintains a vertical distance of at least 30 cm from power lines to suppress electromagnetic interference. Simultaneously, a high-precision vortex flow meter and pressure transmitter are installed on the straight section of the main outlet pipe of the air compressor station, at a location no less than ten times the pipe diameter from the nearest bend or valve. These two devices jointly collect instantaneous flow and pressure fluctuation data of the main pipeline and transmit them to the data acquisition unit via a 4-20mA standard signal. Furthermore, ambient temperature and humidity sensors and atmospheric pressure sensors are installed in well-ventilated locations within the air compressor station, and their output signals are also connected to the corresponding analog input channels of the data acquisition unit. All acquired signals are filtered, amplified, and converted from analog to digital by the signal conditioning circuitry within the data acquisition unit. The sampling period is packaged and uploaded to the central control unit.

[0024] like Figure 2 As shown, the central control unit uses an embedded industrial computer as its hardware platform, running VxWorks or LinuxRT real-time operating systems. Internally, it is divided into five functional modules: load forecasting module, energy efficiency assessment module, group control and scheduling module, equipment health monitoring module, and energy efficiency optimization feedback loop. Each module is deployed as an independent process, exchanging data through a shared memory area and utilizing a message queue mechanism to achieve event-driven synchronous communication. After system startup, a high-precision timer triggers each module to execute sequentially with a fixed scheduling cycle of seconds. First, the load forecasting module acquires current and historical load data from the data acquisition unit, uses a Long Short-Term Memory (LSTM) neural network model to predict the gas load for the next 5 minutes, and generates a gas load forecast curve. The LSTM model was trained offline using actual operating data from the past six months during the system initialization phase, and the weight parameters were continuously fine-tuned through an online learning mechanism during subsequent operation. Subsequently, the energy efficiency assessment module received the current operating status of each air compressor from the data acquisition unit, including the loading rate. The system uses exhaust pressure, current, and the predicted load output from the load prediction module to call a pre-established unit gas production energy consumption model. ; Calculate the expected energy efficiency index of each air compressor under different load rates in the next scheduling cycle; in this model and The model is a nonlinear function of the loading rate L, implemented by polynomial fitting or lookup table method, and the model parameters are dynamically corrected through energy efficiency optimization feedback loop.

[0025] The group control and scheduling module receives the energy efficiency indicators of each air compressor output by the energy efficiency assessment module and the load prediction module. Then, construct and solve the following constrained optimization problem: the objective function is to minimize the total energy consumption of the system: ; The constraints include total gas production: ; and the loading rate of each air compressor j It must be within its allowed range Inside, among which Usually , for This optimization problem is solved in real time on an embedded platform using the Sequential Quadratic Programming (SQP) algorithm to generate the optimal air compressor start-up and shutdown combination strategy: that is, determining which air compressors are in operation, which are shut down, and the load distribution scheme for each operating air compressor; for example, when the predicted load is... The system is equipped with three units, each with a rated gas production capacity of [missing information]. When operating a screw air compressor, the group control scheduling module may output: Start air compressor A and air compressor B, with loading rates of respectively and Instead of simply starting two machines to run at full capacity, the instructions are given to avoid inefficient operating intervals.

[0026] The execution control unit establishes a bidirectional communication connection with the local controller of each air compressor via an industrial communication bus. This communication bus uses the Modbus TCP protocol, with an industrial Ethernet physical layer. The execution control unit has a built-in multi-port Ethernet switch, and each port is connected to the corresponding air compressor's PLC or dedicated controller via a shielded network cable. After receiving the control commands output by the group control scheduling module, the execution control unit parses them into specific start / stop commands, load / unload switching signals, and inverter frequency settings, and then uses the Modbus TCP write register function code (such as...) or The command is sent to each air compressor controller. For fixed-frequency air compressors without frequency conversion function, the execution control unit only sends start / stop and load / unload signals. For variable-frequency air compressors, the target frequency is set at the same time to accurately control its load rate. All communication processes include CRC check and timeout retransmission mechanism to ensure reliable delivery of commands.

[0027] The equipment health monitoring module continuously acquires key operating parameters of each air compressor from the data acquisition unit, including exhaust temperature, vibration acceleration, lubricating oil differential pressure, and current harmonic distortion rate. Vibration acceleration is acquired by an IEPE accelerometer mounted on the bearing housing; lubricating oil differential pressure is measured by a differential pressure transmitter across the oil filter; and current harmonic distortion rate is calculated by the power quality analysis module. This module has multiple threshold levels; for example, the normal range for exhaust temperature is... The warning threshold is The fault threshold is When any key parameter of an air compressor exceeds the warning threshold for three consecutive sampling cycles, the equipment health monitoring module generates an equipment warning signal and transmits it to the group control scheduling module via shared memory. In subsequent scheduling cycles, the group control scheduling module lowers the scheduling priority of this air compressor; for example, prioritizing other air compressors in good health while ensuring the air supply pressure is met. If the air compressor is already running and its parameters continue to deteriorate, the module will then ensure that the total air production is not less than [a certain threshold]. Under these conditions, it is isolated from the operating sequence, and a backup air compressor is automatically started to replace its air supply task, without any manual intervention.

[0028] Energy efficiency optimization feedback loop such as Figure 5 As shown, its input terminal is connected to the actual operating energy consumption data of the data acquisition unit. The actual operating energy consumption data is calculated from the cumulative power consumption and gas production collected by the electricity meter, as well as the energy consumption value predicted by the energy efficiency assessment module before scheduling; the system completes one scheduling cycle, the cycle being... The energy efficiency optimization feedback loop calculates the relative deviation between the actual unit gas production energy consumption and the predicted value. ;like Exceeding the preset tolerance range If this occurs, the model correction mechanism is triggered: the recursive least squares (RLS) method is used to locally update the unit gas production energy consumption model parameters of the corresponding air compressor in the energy efficiency assessment module; for example, if an air compressor's internal clearance increases due to long-term operation, and its actual energy consumption is higher than the model prediction, the RLS algorithm will adjust based on the newly collected data points. The coefficients of the function make the model more closely reflect the current state of the equipment; this feedback mechanism ensures that the energy efficiency model adapts to changes in equipment aging or operating conditions, maintaining the long-term effectiveness of the scheduling strategy.

[0029] In actual operation, suppose a manufacturing plant's air compressor station is equipped with four screw air compressors, namely air compressor A, air compressor B, air compressor C, and air compressor D. Among them, air compressor A and air compressor B are variable frequency compressors, while air compressor C and air compressor D are fixed frequency compressors, and their rated air production capacity is [missing information]. At the start of the morning shift, the gas load rapidly increased from... Rise to The data acquisition unit detected an accelerated rate of pressure drop in the main pipeline and a continuous increase in the flow meter reading, uploading the data to the central control unit in real time. The load forecasting module, based on historical early morning load curves and the current rate of change, predicts that the load will stabilize within the next 5 minutes. The energy efficiency assessment module calls the energy efficiency models of each air compressor and finds that air compressor A and air compressor B... The optimal energy efficiency is achieved within the load rate range. Air compressors C and D have high energy efficiency at full load, but their efficiency drops sharply under partial load. Based on this, the group control scheduling module solves the optimization problem and outputs the command: start air compressors A, B, and C, with air compressor A at a load rate of [missing information]. Air compressor B loading rate Air compressor C is fully loaded, with a total air production of Slightly higher than the predicted value to allow for a margin; the control unit sends the frequency setpoint to air compressor A and air compressor B via Modbus TCP, corresponding to... and The rotational speed is adjusted, and start-up and loading commands are sent to air compressor C. During operation, the equipment health monitoring module detects that the exhaust temperature of air compressor C reaches a certain level within three consecutive cycles. ,Exceed Upon reaching the warning threshold, an alert is immediately sent to the group control and scheduling module. The module recalculates in the next cycle, deciding to disable air compressor C, start air compressor D, and adjust the loading rates of air compressors A and B. The total gas production still meets demand; meanwhile, the energy efficiency optimization feedback loop compares actual power consumption with predicted values ​​and finds a deviation of [missing information]. ,Exceed Tolerance is considered, and the RLS algorithm is then initiated to update the energy efficiency model parameters of air compressor C and air compressor D. The entire system completes closed-loop control of perception, prediction, evaluation, scheduling, execution and feedback without human intervention, achieving optimal energy efficiency and stable air supply under multi-machine collaboration. In order to enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further supplemented below with a specific application scenario.

[0030] In the air compressor station of an automotive parts manufacturing plant, four screw air compressors are deployed, labeled Air Compressor A, Air Compressor B, Air Compressor C, and Air Compressor D. Air Compressors A and B are variable frequency models, while Air Compressors C and D are fixed frequency models. The rated air production capacity of each unit is [missing information]. The system follows Figure 1The installation follows the architecture shown. Data acquisition units are located at the air compressor station and connected to the exhaust pressure sensor, exhaust temperature sensor, current transformer, and running timer of each air compressor via shielded twisted-pair cables. High-precision vortex flow meters and pressure transmitters are installed on the straight sections of the main pipeline. The central control unit is installed in a standard cabinet in the control room, integrating a load forecasting module, energy efficiency assessment module, group control and scheduling module, equipment health monitoring module, and energy efficiency optimization feedback loop. Figure 2 As shown; the control unit is located in the power distribution area and establishes communication with the local controllers of each air compressor via an industrial Ethernet network. Communication connection, such as Figure 4 As shown.

[0031] When the early shift production starts, gas demand surges, causing the main pipeline pressure to drop rapidly, and the data acquisition unit... The sampling period acquires the instantaneous flow rate of the main pipeline, the exhaust temperature, current, and operating status of each air compressor in real time, and uploads the data to the central control unit. The load forecasting module, based on the current flow rate trend and historical load data from the same period, uses a trained LSTM neural network model to predict the gas load for the next 5 minutes and outputs the predicted value. = The forecast results serve as input for subsequent scheduling, ensuring that the gas supply capacity has a reasonable margin.

[0032] The energy efficiency assessment module synchronously receives the current load rate of each air compressor. The system retrieves exhaust pressure and current data and calls up a pre-stored unit gas production energy consumption model. ,in and The module uses a loading rate correlation function obtained through polynomial fitting to calculate the expected energy efficiency of each air compressor under different loading rates, identifying air compressors A and B. The loading range has the lowest unit energy consumption, while air compressors C and D are only energy efficient at full load. At partial load, their efficiency drops significantly due to frequent loading / unloading.

[0033] The group control and scheduling module is based on the above energy efficiency assessment results and load forecast values. Construct a constrained optimization problem with the objective of minimizing the total energy consumption of the system, which is mathematically expressed as: ; satisfy and This module uses the Sequential Quadratic Programming (SQP) algorithm on an embedded platform to solve the problem in real time, generating the optimal start-stop combination and load ratio allocation scheme. In this scenario, the module outputs: start air compressors A, B, and C, and the load ratio of air compressor A is... Air compressor B loading rate The dispatch command for full load of air compressor C corresponds to a total air production of This ensures that gas supply needs are met and avoids inefficient operating zones.

[0034] After receiving the instruction, the control unit parses it into specific control signals: for air compressor A and air compressor B, it writes the corresponding frequency setpoints to their local controllers via the Modbus TCP protocol: respectively corresponding to and Rated speed; for air compressor C, a start command and loading signal are sent; all commands are reliably transmitted through CRC check and timeout retransmission mechanism, such as... Figure 4 The communication topology shown ensures the synchronization and accuracy of multi-machine commands.

[0035] During operation, the equipment health monitoring module continuously monitors the key parameters of each air compressor; when it detects that the exhaust temperature of air compressor C is below the threshold for three consecutive sampling cycles, i.e. Internally reached Exceeding the preset warning threshold Upon receiving the warning signal, the module immediately generates an early warning signal and transmits it to the group control and scheduling module via shared memory. In the next scheduling cycle, the group control and scheduling module re-solves the optimization problem, excluding air compressor C from participation, and instead starts the standby air compressor D, while adjusting the load rates of air compressors A and B to [a specific value]. To ensure that the total gas production remains no less than This also prevents high-temperature equipment from continuing to operate, achieving proactive isolation before a fault occurs.

[0036] At the same time, the energy efficiency optimization feedback loop compares the actual unit gas production energy consumption after each 10-second scheduling cycle. Compared with the predicted value before scheduling Actual unit gas production energy consumption The result is calculated from the cumulative power consumption of the electricity meter and the gas production of the flow meter; when the relative deviation is calculated... ,Exceed When the tolerance threshold is reached, the model correction mechanism is triggered; this loop uses recursive least squares (RLS) to evaluate the energy efficiency of air compressor C and the actual unit gas production energy consumption in the energy efficiency assessment module. D The function coefficients are updated online to compensate for model inaccuracies caused by equipment aging or operating condition drift, such as... Figure 5 The data flow path shown ensures the dynamic adaptability of the energy efficiency model.

[0037] Through the above process, the system, without human intervention, relies on the collaborative work of the data acquisition unit, the central control unit, and the execution and control unit to complete a complete closed loop from load perception, energy efficiency assessment, optimized scheduling, command execution to health warning and model self-correction, achieving stable gas supply and optimal energy consumption under multi-machine linkage.

[0038] All contents not described in detail in the specification are existing technologies known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited; conventional equipment can be used. Electrical control components not mentioned in this technical solution are existing technologies and are therefore not shown in the figures, nor will they be described here.

[0039] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made by those skilled in the art to the above embodiments within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-computer linkage-based air compressor station intelligent group control energy-saving system, characterized in that: The air compressor station comprises a data acquisition unit, a central control unit and an execution control unit. The data acquisition unit is configured to acquire in real time the running state parameters of each air compressor in the air compressor station, the environmental condition parameters and the load demand parameters of the gas using end. The central control unit comprises a load prediction module, an energy efficiency evaluation module and a group control scheduling module. The group control scheduling module is connected to the energy efficiency evaluation module and the load prediction module, and is configured to generate an optimal air compressor start-stop combination strategy and a loading ratio distribution scheme according to the predicted load curve and the energy efficiency indicators of each air compressor. The execution control unit is connected to the central control unit, and is configured to receive the control instructions output by the group control scheduling module and convert the control instructions into start-stop signals, loading / unloading switching signals and frequency setting values of the frequency converters of the air compressor controllers.

2. The multi-computer linkage-based air compressor station intelligent group control energy-saving system according to claim 1, characterized in that: The running state parameters comprise the loading / unloading state, the exhaust pressure, the exhaust temperature, the current value, the running time length and the fault code of each air compressor.

3. The multi-computer linkage-based air compressor station intelligent group control energy-saving system according to claim 1, characterized in that: The environmental condition parameters comprise the environmental temperature, the humidity and the atmospheric pressure. The load demand parameters are acquired by the flow meter and the pressure transmitter arranged on the main pipeline, and reflect the instantaneous gas consumption and the pressure fluctuation trend of the current gas using system. The start-stop combination strategy satisfies the constraint condition that the gas pressure is maintained within a preset working interval, and minimizes the overall energy consumption of the system. The loading ratio distribution scheme makes the multi-machine collaborative operation point approach the optimal region of the comprehensive energy efficiency of the system by dynamically adjusting the loading rate of each running air compressor. The execution control unit establishes a bidirectional communication connection with the local controllers of the air compressors through an industrial communication bus. The industrial communication bus adopts one of ModbusTCP, ProfibusDP and CANopen protocols. The central control unit is further provided with a device health monitoring module connected to the data acquisition unit. When it is detected that the running parameters of an air compressor continuously exceed the preset threshold value for three sampling periods, a device warning signal is generated and fed back to the group control scheduling module. The group control scheduling module reduces the scheduling priority of the air compressor in the subsequent scheduling period after receiving the equipment warning signal, or isolates it from the operation sequence without affecting the stability of the air supply pressure, and starts the standby air compressor to replace its air supply task; the central control unit is further provided with an energy efficiency optimization feedback loop connected to the data acquisition unit and the energy efficiency evaluation module, for comparing the actual operation energy consumption data with the energy consumption prediction value before scheduling, and if the relative deviation exceeds the preset tolerance range, triggering the model correction mechanism to online correct the unit gas production energy consumption model parameters in the energy efficiency evaluation module.

4. The multi-computer linkage-based air compressor station intelligent group control energy-saving system of claim 2, wherein: The flow meter and pressure transmitter in the data acquisition unit are installed on the straight pipe section of the air compressor station outlet header, and the distance from the nearest elbow or valve is not less than ten times the pipe diameter; the local sensors of each air compressor are connected to the analog input module of the data acquisition unit through shielded cables, and the signal transmission path adopts anti-interference wiring method to avoid parallel laying with power cables.

5. The multi-computer linkage-based air compressor station intelligent group control energy-saving system according to claim 1, characterized in that: The time series analysis algorithm adopted by the load prediction module is autoregressive moving average model or long short-term memory neural network model, and its training process is based on the actual load data in the past several operation cycles of the air compressor station.

6. The multi-computer linkage-based air compressor station intelligent group control energy-saving system according to claim 1, characterized in that: The unit gas production energy consumption model in the energy efficiency evaluation module is expressed as: ； wherein, represents the first air compressor unit energy consumption per unit of gas production under the load rate , is the input electric power thereof, is the gas production flow thereof; the model is continuously updated by an online learning mechanism.

7. The multi-computer linkage-based air compressor station intelligent group control energy-saving system according to claim 1, characterized in that: The group control scheduling module generates a loading proportion allocation scheme, and solves the following optimization problem: ; ； ； wherein, is the number of air compressors currently participating in the dispatch, is the predicted air usage and are the minimum and maximum loading rates allowed for the air compressors, respectively.

8. The multi-computer linkage-based air compressor station intelligent group control energy-saving system of claim 3, wherein: The key operation parameters monitored by the equipment health monitoring module include exhaust temperature, vibration acceleration, lubricating oil pressure difference and current harmonic distortion rate.

9. The multi-computer linkage-based air compressor station intelligent group control energy-saving system of claim 3, wherein: The preset tolerance range in the energy efficiency optimization feedback loop is , and the model correction mechanism adopts recursive least squares method for local parameter update.

10. The multi-compressor linkage based intelligent group control energy saving system for air compressor stations of claim 1, wherein: The central control unit takes an embedded industrial computer as the hardware platform, runs a real-time operating system, and deploys each functional module in the form of an independent process to realize data interaction between modules through shared memory and message queues; the load prediction module, energy efficiency evaluation module and group control scheduling module are executed in turn according to the fixed scheduling period, and the execution time sequence of each period is strictly controlled by a high-precision timer.

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