CN109184931B - Active control system and method for methane internal combustion generator set - Google Patents

Active control system and method for methane internal combustion generator set Download PDF

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Publication number
CN109184931B
CN109184931B CN201811346181.8A CN201811346181A CN109184931B CN 109184931 B CN109184931 B CN 109184931B CN 201811346181 A CN201811346181 A CN 201811346181A CN 109184931 B CN109184931 B CN 109184931B
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internal combustion
methane
data
generating set
prediction model
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CN109184931A (en
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赵峰
张广渊
王书新
潘为刚
赵兴贵
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Shandong Jiaotong University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/0025—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures
    • F02D41/0027—Controlling engines characterised by use of non-liquid fuels, pluralities of fuels, or non-fuel substances added to the combustible mixtures the fuel being gaseous
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D29/00—Controlling engines, such controlling being peculiar to the devices driven thereby, the devices being other than parts or accessories essential to engine operation, e.g. controlling of engines by signals external thereto
    • F02D29/06—Controlling engines, such controlling being peculiar to the devices driven thereby, the devices being other than parts or accessories essential to engine operation, e.g. controlling of engines by signals external thereto peculiar to engines driving electric generators
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D35/00—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for
    • F02D35/0015—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for using exhaust gas sensors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D35/00—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for
    • F02D35/02—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions
    • F02D35/025—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions by determining temperatures inside the cylinder, e.g. combustion temperatures
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D35/00—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for
    • F02D35/02—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions
    • F02D35/027—Controlling engines, dependent on conditions exterior or interior to engines, not otherwise provided for on interior conditions using knock sensors
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/22—Safety or indicating devices for abnormal conditions
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D41/00—Electrical control of supply of combustible mixture or its constituents
    • F02D41/22—Safety or indicating devices for abnormal conditions
    • F02D2041/224—Diagnosis of the fuel system
    • F02D2041/225—Leakage detection
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F02D2200/021—Engine temperature
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F02D2200/023—Temperature of lubricating oil or working fluid
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F02D2200/04—Engine intake system parameters
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
    • F02D—CONTROLLING COMBUSTION ENGINES
    • F02D2200/00—Input parameters for engine control
    • F02D2200/02—Input parameters for engine control the parameters being related to the engine
    • F02D2200/06—Fuel or fuel supply system parameters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Output Control And Ontrol Of Special Type Engine (AREA)
  • Combined Controls Of Internal Combustion Engines (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The disclosure provides an active control system and method for a biogas internal combustion generator set. Wherein, an initiative control system of marsh gas internal combustion generating set includes: the environment sensing sensor group is configured to collect environment sensing data of the unit and transmit the data to the active controller; the active controller is also connected with the ECU controller of the methane internal combustion generating set, and is configured to collect internal operation data of the ECU controller of the methane internal combustion generating set; the active controller is further configured to: driving the corresponding active safety executing mechanism to act according to the environmental perception data of the unit; inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set; and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability of the methane internal combustion generator unit.

Description

Active control system and method for methane internal combustion generator set
Technical Field
The disclosure belongs to the technical field of biogas internal combustion generating sets, and particularly relates to an active control system and method of a biogas internal combustion generating set.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The methane internal combustion generating set is a new energy comprehensive utilization system integrating environmental protection and energy saving, utilizes a large amount of methane generated by anaerobic fermentation treatment of a large amount of organic wastes in industrial, agricultural or town life, burns the methane in a combustion chamber of an internal combustion engine to generate heat energy to drive the internal combustion generating set to generate electricity, and can fully utilize the waste heat of the generating set to realize a combined cooling heating and power system, the comprehensive efficiency reaches about 70-80%, and good economic, energy-saving and environmental benefits are generated.
But the concentration difference and the fluctuation of the biogas and methane generated by different organic waste raw materials and different fermentation temperatures are large, and a great challenge is provided for the efficient, stable and safe operation of the biogas internal combustion generator set. Particularly, the biogas internal combustion generating set is installed and operated near the biogas system, and if the biogas internal combustion generating set has the operation safety problem, huge potential safety hazards are generated for the set and the biogas system.
Disclosure of Invention
According to an aspect of one or more embodiments of the present disclosure, an active control system of a biogas internal combustion generator set is provided, which can improve the stability and reliability of the operation of the biogas internal combustion generator set.
The utility model discloses an initiative control system of marsh gas internal combustion generating set, include:
the environment sensing sensor group is configured to collect environment sensing data of the unit and transmit the data to the active controller; the active controller is also connected with the ECU controller of the methane internal combustion generating set, and is configured to collect internal operation data of the ECU controller of the methane internal combustion generating set;
the active controller is further configured to:
driving the corresponding active safety executing mechanism to act according to the environmental perception data of the unit;
inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set;
and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability of the methane internal combustion generator unit.
In one or more embodiments, the active controller employs an RS485 bus and a Modbus protocol to collect data sensed by the ambient sensing sensor group and store it in a database.
In one or more embodiments, the set of environment-aware sensor groups includes: gas leakage detector, gas concentration sensor, smoke sensor, pressure sensor, flowmeter, vibration sensor, sound sensor, temperature and humidity sensor and image acquisition ware.
In one or more embodiments, the set of environment-aware sensor groups further comprises:
the generator grounding detector is used for detecting whether the generator grounding system works normally or not;
and the mains supply outage sensor is used for detecting whether mains supply is outage or not.
In one or more embodiments, the active safety actuator includes: exhaust fan, marsh gas valve, alarm, emergency lighting system and dehumidification system.
In one or more embodiments, the active controller is further configured to:
constructing a methane internal combustion generating set fault prediction model by using a deep learning convolutional neural network;
determining input variables and output variables of a fault prediction model of the methane internal combustion generator set, and normalizing;
constructing a sample set and a test set by the normalized input variable and the normalized output variable;
and training a fault prediction model of the methane internal combustion generator set by using the sample set, testing the output result precision of the fault prediction model of the methane internal combustion generator set by using the test set, and completing the training of the fault prediction model of the methane internal combustion generator set when the output result precision reaches the preset requirement.
In one or more embodiments, the input variables of the biogas internal combustion genset fault prediction model include: biogas data, air data, smoke data, cylinder liner water data and lubricating oil data;
the output variables of the fault prediction model of the methane internal combustion generating set comprise: knock sensor data, oxygen sensor data, exhaust gas temperature, engine oil temperature, combustion cylinder temperature and cylinder liner water temperature of the operation of the methane internal combustion generator set.
According to another aspect of one or more embodiments of the present disclosure, a control method of an active control system of a biogas internal combustion generator set is provided, which can improve the stability and reliability of the operation of the biogas internal combustion generator set.
The control method of the active control system of the methane internal combustion generating set comprises the following steps:
comparing the environment sensing data of the unit with preset safety conditions, and driving the corresponding active safety executing mechanism to act when the environment sensing data of the unit does not accord with the preset safety conditions;
receiving internal real-time operation data of an ECU controller of the methane internal combustion generating set, inputting the internal real-time operation data into a trained fault prediction model of the methane internal combustion generating set, and outputting a fault prediction result of the methane internal combustion generating set;
and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability of the methane internal combustion generator unit.
In one or more embodiments, before inputting the internal real-time operation data of the ECU controller of the biogas internal combustion generator to the trained biogas internal combustion generator failure prediction model, the method further comprises:
constructing a methane internal combustion generating set fault prediction model by using a deep learning convolutional neural network;
determining input variables and output variables of a fault prediction model of the methane internal combustion generator set, and normalizing;
constructing a sample set and a test set by the normalized input variable and the normalized output variable;
and training a fault prediction model of the methane internal combustion generator set by using the sample set, testing the output result precision of the fault prediction model of the methane internal combustion generator set by using the test set, and completing the training of the fault prediction model of the methane internal combustion generator set when the output result precision reaches the preset requirement.
In one or more embodiments, a Sigmoid function-based data normalization method is used to normalize input variables and output variables of a biogas internal combustion genset fault prediction model.
The beneficial effects of the present disclosure are:
(1) The method drives the corresponding active safety executing mechanism to act according to the environmental perception data of the unit so as to achieve the purpose of directly judging and implementing active safety control; inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set; the fault prediction model based on the unit operation data is based on an internal operation mechanism model of the unit, and is used for excavating internal potential hazards and implementing active safety control; and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability and reliability of the methane internal combustion generator unit.
(2) The method and the system are based on the surrounding environment information of the methane internal combustion generating set and the set operation data, a dynamic mathematical model for accurately describing the operation characteristics of the methane internal combustion generating set under different methane components and multiple working conditions is built, the complex operation rule and fault prediction of the methane internal combustion generating set are obtained, the surrounding environment information of the set is actively and insights, an active safety control mechanism is built, the safety and stability performance of the set is fundamentally improved, and the operation stability and reliability of the whole methane system and the whole generating set system are fundamentally ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate and explain the exemplary embodiments of the disclosure and together with the description serve to explain the disclosure, and do not constitute an undue limitation on the disclosure.
Fig. 1 is a schematic structural diagram of an active control system of a biogas internal combustion generator set of the present disclosure.
Fig. 2 is a control method flow chart of an active control system of a biogas internal combustion generator set of the present disclosure.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments in accordance with the present disclosure. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Fig. 1 is a schematic structural diagram of an active control system of a biogas internal combustion generator set of the present disclosure.
As shown in fig. 1, an active control system of a biogas internal combustion generator set of the present disclosure includes: the system comprises an environment sensing sensor group, an active controller, a methane internal combustion generating set ECU controller and an active safety executing mechanism.
The following detailed analysis of the active control system of a biogas internal combustion generator set:
(1) Environment sensing sensor group
And the environment sensing sensor group is configured to collect environment sensing data of the unit and transmit the data to the active controller.
In particular implementations, the set of environment-aware sensor groups includes, but is not limited to: gas leakage detector, gas concentration sensor, smoke sensor, pressure sensor, flowmeter, vibration sensor, sound sensor, temperature and humidity sensor and image acquisition ware.
The environmental perception sensor group further includes:
the generator grounding detector is used for detecting whether the generator grounding system works normally or not;
and the mains supply outage sensor is used for detecting whether mains supply is outage or not.
For example:
1) Explosion-proof biogas leakage detector: the device is used for detecting whether the biogas of the biogas pipeline leaks or not;
2) Explosion-proof carbon dioxide concentration sensor: the device is used for detecting whether a flue gas pipeline in a machine room of the methane internal combustion generator set leaks or not;
3) Explosion-proof type CO concentration sensor: the device is used for detecting whether the combustion of the methane internal combustion generating set is sufficient;
4) Smoke alarm: the system is used for detecting whether the smoke alarm exists in the methane internal combustion generator set machine room;
5) Biogas pipeline pressure: the device is used for detecting whether the gas supply pressure of the biogas pipeline meets the pressure requirement;
6) Biogas flow meter: the device is used for detecting whether the biogas pipeline is blocked;
7) Vibration sensor of explosion-proof unit: the device is used for detecting whether the methane internal combustion generator set burns normally or not;
8) Noise sensor: the silencer is used for detecting whether the muffler of the methane internal combustion generating set works normally or not;
9) Cable temperature detector: the device is used for detecting whether an electric energy output cable of the generator set is overloaded;
10 Generator set grounding system: the device is used for detecting whether the generator grounding system works normally or not;
11 Flue gas duct pressure sensor: the device is used for detecting the pressure of the flue gas pipeline and judging whether the flue gas pipeline is blocked;
12 Smoke Nox sensor: for detecting the concentration of nitrides
13 Indoor temperature sensor of unit: for detecting the temperature in the machine room.
14 Indoor humidity sensor of the unit: for detecting humidity in the air, and excessive humidity may cause fire.
15 Automatic identification of the camera by the operator: whether for the automatic equipment is a crew member;
16 Mains supply outage sensor): and the device is used for detecting whether the mains supply is powered off.
The component models in the environmental sensor group are shown in table 1.
Table 1 element model in environmental awareness sensor group
(2) ECU controller of methane internal combustion generator set
The active controller of the methane internal combustion generating set provides an RS485 interface and an RJ45 interface to communicate with the ECU of the methane internal combustion generating set, and the collected internal operation data N of the ECU of the methane internal combustion generating set i Comprising the following steps:
N i ＝[H CH4 ,H Q2 ,C ZS ,C BZ ,T PY ,P JY ,T JY ,P ZQ ,J TQ ,T G ,T GT ,D ZG ]；
wherein H is CH4 Is CH in methane 4 Content (%), H Q2 Data value (%), C of oxygen sensor ZS Is the rotating speed value (rpm) and C of the methane internal combustion engine BZ Is knock sensor data value (%), T PY The smoke discharging temperature value (DEG C) and the P of the methane internal combustion generating set are JY Is the engine oil pressure value (kPa) and T of the methane internal combustion generator set JY The engine oil temperature (DEG C) and the engine oil temperature (P) of the methane internal combustion generator set ZQ Is the pressure value (kPa) and J of an inlet manifold of the methane internal combustion generator set TQ Is the ignition advance angle (degree) and T of the methane internal combustion generator set G Is the temperature (DEG C) and T in a combustion cylinder of a methane internal combustion generating set GTS Is the cylinder sleeve water temperature (DEG C) and D of the methane internal combustion generator set ZG The power generation power (kW) of the methane internal combustion generator set.
(3) Active safety actuating mechanism
Specifically, the active safety actuator includes, but is not limited to: exhaust fan, marsh gas valve, alarm, emergency lighting system and dehumidification system.
In this embodiment, the active safety actuator of the biogas internal combustion generator set includes: the specific models of the electric control exhaust fan, the electric control methane valve, the alarm lamp, the alarm, the emergency lighting system and the dehumidifying system are shown in table 2.
Table 2 model of active safety actuator
(4) Active controller
The active controller is further configured to:
driving the corresponding active safety executing mechanism to act according to the environmental perception data of the unit;
inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set;
and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability of the methane internal combustion generator unit.
Specifically, the active controller adopts an RS485 bus and a Modbus protocol to collect data perceived by the environment perception sensor group and store the data in a database.
The active controller of this embodiment runs on an X86 platform and employs a linux real-time operating system.
The active controller of this embodiment has the following functions:
collecting data of an external sensor of the methane internal combustion generator set;
internal operation data read through communication between the RS485 communication module and the ECU;
storing the operation history data in a data platform;
operating an active safety control method;
and outputting an active safety control signal to operate the active safety executing mechanism.
The system comprises an environment and safe operation data platform of a methane internal combustion generator set, wherein the data platform is connected with an active controller by adopting an RJ45 interface, and the data platform establishes an SQL Server database to store the environment and safe operation data of the methane internal combustion generator.
In a specific implementation, the active controller drives the corresponding active safety executing mechanism to act according to the environmental sensing data of the unit, as shown in fig. 2:
the active controller detects that the biogas leakage alarm detector and the smoke alarm, the active controller increases the power of the exhaust fan and starts the alarm, and the active controller communicates with the ECU of the biogas internal combustion generating set, so that the generating set reduces the generating power and is off-grid with the power grid, and the generating set is stopped after off-grid. And finally, closing the methane valve by the active controller.
If the active controller detects that any one of the carbon dioxide concentration, the CO concentration, the temperature and the flue gas NOx exceeds a set value, the active controller increases the power of the exhaust fan to improve the ventilation in the machine room, and simultaneously starts the alarm. If the concentration of carbon dioxide, the concentration of carbon monoxide or the temperature and the NOx of the flue gas are lower than the set values within 2 minutes, the alarm is turned off, and the running power of the exhaust fan is kept. Otherwise, the active controller is communicated with the ECU of the methane internal combustion generating set, so that the generating set reduces the power generation and is separated from the power grid, and after the power grid is separated from the power grid, the generating set is stopped, and a methane valve is closed.
And if the active controller detects that the humidity value exceeds the set value, the active controller starts the dehumidification system to reduce the humidity in the machine room, and simultaneously starts the alarm. If the humidity value is lower than the set value, the alarm is turned off and the dehumidification system is turned off.
If the face recognition camera of the active controller detects unauthorized staff, the active controller starts an alarm and an alarm lamp and plays a safety prompt voice;
if the active controller detects that the commercial power is disconnected and the generator is in ground connection fault, the active controller starts an alarm, an alarm lamp and an emergency lighting system and communicates with an ECU (electronic control unit) of the methane internal combustion generator set, so that the generator set and a power grid are disconnected, and after the generator set is disconnected, the generator set is stopped and a methane valve is closed;
and if the active controller detects that the biogas pressure is lower than the biogas pressure value allowed by the unit, the active controller starts an alarm and communicates with the ECU of the biogas internal combustion generating set to reduce the load of the generating set and leave the power grid, and after the generating set leaves the power grid, the generating set runs at idle speed until the biogas pressure reaches the pressure value allowed by the unit to increase the load and be connected with the power grid. If the biogas pressure can not reach the biogas pressure set value within 10 minutes, the generator set is stopped.
If the active controller detects that the noise exceeds 10% of the set value, the active controller starts an alarm and communicates with an ECU of the methane internal combustion generating set, so that the load of the generating set is reduced, the generating set is off-grid with a power grid, and the generating set is stopped after the generating set is off-grid.
In specific implementation, the process of inputting the internal real-time operation data of the ECU controller of the methane internal combustion generating set into the trained fault prediction model of the methane internal combustion generating set comprises the following steps:
(1) Firstly, establishing a fault prediction model of a methane internal combustion generator set by using a deep learning convolutional neural network based on environmental data and operation data;
determining input variable I of fault prediction modeling of methane internal combustion generator set i And output variable O i ；
Input variable I of fault prediction model of methane internal combustion generator set i Comprising the following steps: biogas data, air data, smoke data, cylinder liner water data and lubricating oil data; determining an input variable I for modeling a biogas internal combustion generator set i (i＝1,…9):I i ＝[H CH4 ,L ZQ ,Y ZQ ,L KQ ,J TQ ,C ZS ,D YG ,D WG ,T JF ]；
Wherein L is ZQ Is the flow value (m 3 /h)；Y ZQ Is the pressure value (kPa) of the biogas; t (T) JF Is the temperature value (DEG C) in the machine room;
output variable O of fault prediction model of methane internal combustion generator set i Comprising the following steps: knock sensor data, oxygen sensor data, smoke exhaust temperature, engine oil temperature, temperature in a combustion cylinder and cylinder liner water temperature of the operation of the methane internal combustion generating set; determining output variable O of overall modeling of biogas internal combustion generating set i (i＝1,…8):O i ＝[C BZ ,H Q2 ,T JY ,T G ,T GTS ,L GTS ,T YQ ,L YQ ,T RHY ]。
(2) Input variable I is input by adopting a normalization method based on Sigmoid function i And output variable O i Normalizing the data;
the method can effectively enlarge the difference of the running data of the state acquisition system of the methane internal combustion generator set by adopting a data normalization method based on a Sigmoid function, and the normalization function is as follows:
wherein, NI i (i=1, … 9) is normalized data based on Sigmoid function, NO i (i=1, … 8) is normalized data based on the Sigmoid function.
(3) Construction of deep learning based convolutional neural network (Convolutional Neural Network, CNN) according to input variables I i Data normalized data and output variable O i The convolutional neural network is trained by the data of the methane internal combustion generating set to obtain a fault prediction model of the methane internal combustion generating set.
The convolutional neural network (Convolutional Neural Network, CNN) is a feedforward neural network, and the artificial neurons can respond to surrounding units and can perform large-scale data operation processing. The convolutional neural network mainly comprises an input layer, a convolutional layer, a pooling layer and a full-connection layer.
The modeling steps are as follows:
step 1: the input layer of the method is 1200 x 9 x 10 three-dimensional vectors, wherein 1200 is 1200 sets of operation data, 9 is 9 influencing factors, and 10 is the time dimension;
step 2: the method has 3 convolution layers, wherein the number of convolution kernels is set to be 128, and the area size of the convolution kernels is 4*4;
step 3: the Pooling layer adopts Max-Pooling (maximum pool sampling layer), and the area size of the sampling layer is 2 x 2;
step 4: the activation function adopted by the method is a hyperbolic tangent function tanh, and the mathematical expression is as follows:
step 5: the adopted optimization algorithm is a Adam (Adaptive Moment Estimation) method, and the learning rate of each parameter is dynamically adjusted by utilizing the first moment estimation and the second moment estimation of the gradient. The selected optimal learning rate lr=0.05.
Step 6: the full connectivity layer was set to 256 neurons and the Dropout parameter was set to 0.5.
Step 7: setting an output layer of a fault prediction model of the methane internal combustion generator set as a two-dimensional vector of 8 x 10, wherein 8 is a prediction vector, and 10 is a time dimension;
step 8: obtaining an optimal weight matrix W, and obtaining a fault prediction model of the methane internal combustion generator set based on the deep learning convolutional neural network.
Constructing a sample set and a test set by the normalized input variable and the normalized output variable;
and training a fault prediction model of the methane internal combustion generator set by using the sample set, testing the output result precision of the fault prediction model of the methane internal combustion generator set by using the test set, and completing the training of the fault prediction model of the methane internal combustion generator set when the output result precision reaches the preset requirement.
(4) Based on the trained methane internal combustion generating set fault prediction model, model input data of real-time running of the set is input into a fault running model to obtain model output data of the fault prediction model, such as knock sensor data, oxygen sensor data, exhaust gas temperature, engine oil temperature, temperature in a combustion cylinder and cylinder liner water temperature deviation is greater than 5%, an active safety controller starts an alarm and an alarm lamp, reduces load of the set, and is off-grid with a power grid, and after off-grid, a methane valve is stopped and closed, as shown in fig. 2.
The method drives the corresponding active safety executing mechanism to act according to the environmental perception data of the unit so as to achieve the purpose of directly judging and implementing active safety control; inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set; the fault prediction model based on the unit operation data is based on an internal operation mechanism model of the unit, and is used for excavating internal potential hazards and implementing active safety control; and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability and reliability of the methane internal combustion generator unit.
The control method of the active control system of the methane internal combustion generating set comprises the following steps:
comparing the environment sensing data of the unit with preset safety conditions, and driving the corresponding active safety executing mechanism to act when the environment sensing data of the unit does not accord with the preset safety conditions;
receiving internal real-time operation data of an ECU controller of the methane internal combustion generating set, inputting the internal real-time operation data into a trained fault prediction model of the methane internal combustion generating set, and outputting a fault prediction result of the methane internal combustion generating set;
and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability of the methane internal combustion generator unit.
In one or more embodiments, before inputting the internal real-time operation data of the ECU controller of the biogas internal combustion generator to the trained biogas internal combustion generator failure prediction model, the method further comprises:
constructing a methane internal combustion generating set fault prediction model by using a deep learning convolutional neural network;
determining input variables and output variables of a fault prediction model of the methane internal combustion generator set, and normalizing;
constructing a sample set and a test set by the normalized input variable and the normalized output variable;
and training a fault prediction model of the methane internal combustion generator set by using the sample set, testing the output result precision of the fault prediction model of the methane internal combustion generator set by using the test set, and completing the training of the fault prediction model of the methane internal combustion generator set when the output result precision reaches the preset requirement.
Specifically, a data normalization method based on a Sigmoid function is adopted to normalize input variables and output variables of a fault prediction model of the methane internal combustion generator set.
The method drives the corresponding active safety executing mechanism to act according to the environmental perception data of the unit so as to achieve the purpose of directly judging and implementing active safety control; inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set; the fault prediction model based on the unit operation data is based on an internal operation mechanism model of the unit, and is used for excavating internal potential hazards and implementing active safety control; and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability and reliability of the methane internal combustion generator unit.
While the specific embodiments of the present disclosure have been described above with reference to the drawings, it should be understood that the present disclosure is not limited to the embodiments, and that various modifications and changes can be made by one skilled in the art without inventive effort on the basis of the technical solutions of the present disclosure while remaining within the scope of the present disclosure.

Claims (6)

1. An active control system for a biogas internal combustion generator set, comprising:
the environment sensing sensor group is configured to collect environment sensing data of the unit and transmit the data to the active controller; the active controller is also connected with the ECU controller of the methane internal combustion generating set, and is configured to collect internal operation data of the ECU controller of the methane internal combustion generating set; the environmental perception sensor group comprises: the system comprises a methane leakage alarm detector, a gas concentration sensor, a smoke sensor, a pressure sensor, a flowmeter, a vibration sensor, a noise sensor, a temperature and humidity sensor and an image collector;
the active controller is further configured to:
driving the corresponding active safety executing mechanism to act according to the environmental perception data of the unit; the active safety actuator comprises: an exhaust fan, a methane valve, an alarm, an emergency lighting system and a dehumidification system;
inputting internal real-time operation data of an ECU controller of the methane internal combustion generating set into a failure prediction model of the methane internal combustion generating set after training is finished, and outputting a failure prediction result of the methane internal combustion generating set;
the active controller is further configured to:
constructing a methane internal combustion generating set fault prediction model by using a deep learning convolutional neural network;
determining input variables and output variables of a fault prediction model of the methane internal combustion generator set, and normalizing;
constructing a sample set and a test set by the normalized input variable and the normalized output variable;
training a methane internal combustion generating set fault prediction model by using a sample set, testing the output result precision of the methane internal combustion generating set fault prediction model by using a test set, and completing the training of the methane internal combustion generating set fault prediction model when the output result precision reaches a preset requirement;
the biogas internal combustion generator set is complementarily and actively controlled by the external data judgment of the set and the prediction judgment of the internal operation mechanism model so as to improve the operation stability of the biogas internal combustion generator set;
the process of inputting the internal real-time operation data of the ECU controller of the methane internal combustion generating set to the failure prediction model of the methane internal combustion generating set after training comprises the following steps:
firstly, establishing a fault prediction model of a methane internal combustion generator set by using a deep learning convolutional neural network based on environmental data and operation data;
determining input variable I of fault prediction modeling of methane internal combustion generator set i And output variable O i ；
Input variable I of fault prediction model of methane internal combustion generator set i Comprising the following steps: biogas data, air data, smoke data, cylinder liner water data and lubricating oil data;
output variable O of fault prediction model of methane internal combustion generator set i Comprising the following steps: knock sensor data, oxygen sensor data, smoke exhaust temperature, engine oil temperature, temperature in a combustion cylinder and cylinder liner water temperature of the operation of the methane internal combustion generating set; input variable I is input by adopting a normalization method based on Sigmoid function i And output variable O i Normalizing the data;
adopting a data normalization method based on a Sigmoid function, wherein the normalization function is as follows:
wherein, NI i， i=1, …, is input variable data normalized based on Sigmoid function，NO i I=1, … 8 is output variable data normalized based on Sigmoid function;
constructing a convolutional neural network based on deep learning according to the normalized input variable I i Training a convolutional neural network by using the data and the output variable Oi data to obtain a fault prediction model of the methane internal combustion generator set;
the modeling steps are as follows:
step 1: the input layer is 1200 x 9 x 10 three-dimensional vectors, wherein 1200 is 1200 sets of operation data, 9 is 9 influencing factors, and 10 is the time dimension;
step 2: the number of the convolution layers is 3, the number of the convolution kernels is set to be 128 by the convolution layers, and the area size of the convolution kernels is 4*4;
step 3: the Pooling layer adopts Max-Pooling, and the area size of the sampling layer is 2 x 2;
step 4: the adopted activation function is a hyperbolic tangent function tanh, and the mathematical expression is as follows:
step 5: the adopted optimization algorithm is an Adam method, and the learning rate of each parameter is dynamically adjusted by utilizing the first moment estimation and the second moment estimation of the gradient;
step 6: the full connection layer is set to 256 neurons, and the Dropout parameter is set to 0.5;
step 7: setting an output layer of a fault prediction model of the methane internal combustion generator set as a two-dimensional vector of 8 x 10, wherein 8 is a prediction vector, and 10 is a time dimension;
step 8: obtaining an optimal weight matrix W, and obtaining a fault prediction model of the methane internal combustion generator set based on the deep learning convolutional neural network;
based on a trained methane internal combustion generating set fault prediction model, inputting model input data of real-time running of the set into the fault prediction model to obtain model output data, and when deviation of knock sensor data, oxygen sensor data, smoke exhaust temperature, engine oil temperature, temperature in a combustion cylinder and cylinder liner water temperature is more than 5%, starting an alarm and an alarm lamp by an active controller, reducing load of the set, and disconnecting the set from a power grid, stopping the set after disconnecting the set from the power grid, and closing a methane valve;
the process of driving the corresponding active safety executing mechanism to act by the active controller according to the environmental perception data of the unit comprises the following steps:
the active controller detects that any one of the methane leakage alarm detector and the smoke alarm alarms, the active controller increases the power of the exhaust fan and starts the alarm, the active controller communicates with the ECU of the methane internal combustion generating set, so that the generating set reduces the power generation and is off-grid with the power grid, the generating set stops after off-grid, and finally, the active controller closes the methane valve;
if the active controller detects that any one of the carbon dioxide concentration, the CO concentration, the temperature and the flue gas NOx exceeds a set value, the active controller increases the running power of the exhaust fan to improve the ventilation in the machine room, and simultaneously starts an alarm; if the concentration of carbon dioxide, the concentration of carbon monoxide or the temperature and the NOx of the flue gas are lower than the set values within 2 minutes, the alarm is turned off, and the running power of the exhaust fan is kept; otherwise, the active controller is communicated with the ECU of the methane internal combustion generating set, so that the generating set reduces the power generation and is off-grid with the power grid, and after off-grid, the generating set is stopped, and a methane valve is closed;
if the active controller detects that the humidity value exceeds the set value, the active controller starts the dehumidification system to reduce the humidity in the machine room, and simultaneously starts the alarm; if the humidity value is lower than the set value, closing the alarm and closing the dehumidification system;
if the face recognition camera of the active controller detects unauthorized staff, the active controller starts an alarm and an alarm lamp and plays a safety prompt voice;
if the active controller detects that the commercial power is disconnected and the generator is in ground connection fault, the active controller starts an alarm, an alarm lamp and an emergency lighting system and communicates with an ECU (electronic control unit) of the methane internal combustion generator set, so that the generator set and a power grid are disconnected, and after the generator set is disconnected, the generator set is stopped and a methane valve is closed;
if the active controller detects that the biogas pressure is lower than the biogas pressure value allowed by the unit, the active controller starts an alarm and communicates with an ECU of the biogas internal combustion generating unit to reduce the load of the generating unit and leave the power grid, and after the generating unit leaves the power grid, the generating unit runs at idle speed until the biogas pressure reaches the pressure value allowed by the unit to increase the load and be connected with the power grid; if the biogas pressure can not reach the biogas pressure set value within 10 minutes, stopping the generator set;
if the active controller detects that the noise exceeds 10% of the set value, the active controller starts an alarm and communicates with an ECU (electronic control unit) of the methane internal combustion generating set, so that the load of the generating set is reduced, the generating set is off-grid with a power grid, and the generating set is stopped after the generating set is off-grid;
the active controller sequentially controls whether the biogas leaks, the smoke alarm alarms and the CO 2 Judging whether the deviation between the output of a fault prediction model and operation data is greater than 5% after the concentration, the CO concentration, the temperature and the flue gas NOx exceed a set value, the humidity exceeds the set value, a camera detects unauthorized personnel, the power supply is cut off or a grounding system fails, the methane pressure is lower than the set value and the noise exceeds 10% of the set value is judged to be free of faults, if yes, an active controller starts an alarm, a generator set reduces the load and is off the grid, the generator set is stopped after the generator set is off the grid, and a methane valve is closed; if not, judging whether the external data of the unit fails or not again in sequence.
2. The active control system of a biogas internal combustion generator set according to claim 1, wherein the active controller uses an RS485 bus and a Modbus protocol to collect data sensed by an environmental sensing sensor group and store the data in a database.
3. An active control system for a biogas internal combustion generator set according to claim 1, wherein the set of environmental sensor sensors further comprises:
the generator grounding detector is used for detecting whether the generator grounding system works normally or not;
and the mains supply outage sensor is used for detecting whether mains supply is outage or not.
4. A control method of an active control system of a biogas internal combustion generator set according to any one of claims 1-3, characterized by comprising:
comparing the environment sensing data of the unit with preset safety conditions, and driving the corresponding active safety executing mechanism to act when the environment sensing data of the unit does not accord with the preset safety conditions;
receiving internal real-time operation data of an ECU controller of the methane internal combustion generating set, inputting the internal real-time operation data into a trained fault prediction model of the methane internal combustion generating set, and outputting a fault prediction result of the methane internal combustion generating set;
and through the external data judgment and the internal operation mechanism model prediction judgment of the unit, the two are complementary to actively control the methane internal combustion generator unit so as to improve the operation stability of the methane internal combustion generator unit.
5. The control method of the active control system of the biogas internal combustion generator set according to claim 4, wherein before inputting the internal real-time operation data of the ECU controller of the biogas internal combustion generator set to the trained failure prediction model of the biogas internal combustion generator set, the control method further comprises:
constructing a methane internal combustion generating set fault prediction model by using a deep learning convolutional neural network;
determining input variables and output variables of a fault prediction model of the methane internal combustion generator set, and normalizing;
constructing a sample set and a test set by the normalized input variable and the normalized output variable;
and training a fault prediction model of the methane internal combustion generator set by using the sample set, testing the output result precision of the fault prediction model of the methane internal combustion generator set by using the test set, and completing the training of the fault prediction model of the methane internal combustion generator set when the output result precision reaches the preset requirement.
6. The control method of the active control system of the biogas internal combustion generator set according to claim 4, wherein the input variable and the output variable of the biogas internal combustion generator set fault prediction model are normalized by adopting a data normalization method based on a Sigmoid function.
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