Operation and maintenance management system for combined heat and power generation device unit

Abstract

The invention relates to the technical field of power generation control analysis, and discloses a combined heat and power generation device unit operation and maintenance management system which is composed of an operation monitoring module, a data analysis module, a predictive maintenance module, an alarm module and a control module. The method comprises the following steps of: acquiring operation data in real time, evaluating the state of equipment by using a function S = f (V (t), N (t) and P (t)), comparing real-time monitoring data D (t) with historical data Dhistory, and judging whether the state of the equipment is normal or not by combining the current unit operation power Pcurrent, and if the specific judgment expression is Sstatus = normal, if | D (t)-Dhistory | lt, judging whether the state of the equipment is normal or not, if | D (t)-Dhistory | lt, judging whether the state of the equipment is normal or not, and if | D (t)-Dhistory | lt, judging whether the state of the equipment is normal or not. The element belongs to the set and is Pcurrentgt; and otherwise, the Pthreshold is abnormal, so that the beneficial effect of monitoring the running state of the unit and finding the fault hidden danger in advance is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power generation control and analysis, and in particular to a cogeneration unit operation and maintenance management system. Background Art

[0002] In response to the requirements of the national energy-saving and environmental protection policy, small industrial boilers are gradually being eliminated. Instead, the surrounding condensing generator sets are being transformed for heating, and centralized heating for heat-using enterprises is achieved by extracting steam from the steam system of the generator set. In order to fully improve the energy utilization efficiency of condensing units, for heat-user enterprises with steam supply pressure levels of 0.5 to 0.8 MPa, the generator set is transformed to extract steam from the connecting pipe between the intermediate pressure cylinder and the low pressure cylinder of the steam turbine. This method requires increasing the steam flow rate of the steam turbine to increase the steam supply pressure. After the steam supply pressure is increased, the power of the generator set is also increased, and the heating of the unit is restricted by the electric power. In order to achieve real-time and flexible regulation of the steam supply pressure, the technical measure of installing a butterfly valve behind the steam extraction port of the connecting pipe between the intermediate pressure cylinder and the low pressure cylinder is adopted, but this measure has the risk of causing the low steam flow rate of the low pressure cylinder to cause overheating and damage to the last stage blades.

[0003] After searching, it was found that the publication number is CN 112343669 A, which is a monitoring device for a cogeneration unit suitable for butterfly valve control of extraction steam pressure. The device includes a connecting pipe butterfly valve, a temperature measuring device, a low-pressure cylinder differential expansion device, a low-pressure cylinder water spray cooling device, a rotating shaft vibration measuring device, a condenser vacuum measuring device, a data acquisition and control device, a connecting pipe extraction steam pressure measuring device, a regulating valve, an isolation valve, and a check valve; the temperature measuring device includes a low-pressure cylinder exhaust temperature measuring device and a medium-pressure cylinder exhaust temperature measuring device. The embodiment of the present invention realizes the monitoring and control of the heating steam turbine body in multiple dimensions and in all directions, which will greatly reduce the risk of equipment damage and increase the service life of the equipment.

[0004] Although the aforementioned technical solution monitors and controls the unit in all directions to reduce its damage risk, this technical solution can only monitor the critical state of the unit when it is damaged, or direct damage caused by a fault, and cannot monitor the abnormal state of components before a fault occurs. At the same time, the aforementioned technical solution relies on temperature monitoring, but the temperature fluctuation of the unit itself is drastic, and its driving strength is proportional to the temperature. It is difficult to rely on temperature monitoring to detect fault problems. Summary of the invention

[0005] Technical issues solved:

[0006] In view of the deficiencies in the prior art, the present invention provides an operation and maintenance management system for a cogeneration unit, which has the advantages of being able to monitor the operating status of the unit to detect potential faults in advance and making the monitoring results more accurate without relying on temperature monitoring, thereby solving the problems of the above-mentioned technology.

[0007] Technical solution:

[0008] To achieve the above object, the present invention provides the following technical solution: a cogeneration unit operation and maintenance management system, the management system is composed of an operation monitoring module, a data analysis module, a predictive maintenance module, an alarm module and a control module, and the modules of the management system are connected by a local area network;

[0009] The operation monitoring module is used to monitor the operation data of the equipment, and the operation data vibration signal, noise and the air extraction pressure of the connecting pipe are analyzed to determine the status of the equipment;

[0010] The data analysis module is used to compare and analyze the collected information of the operation monitoring module with the historical data, and to determine whether the status is normal in combination with the current unit operating power;

[0011] The predictive maintenance module predicts the failure of equipment marked as abnormal based on the collected data and historical failure records, and arranges maintenance work in advance;

[0012] The alarm module is used to immediately trigger an alarm when an abnormality is detected or a potential failure is predicted;

[0013] The control module is used to automatically adjust parameters within 10 seconds after the alarm module occurs, and the parameters include the butterfly valve position and the regulating valve opening to prevent the occurrence of faults.

[0014] Preferably, the state judgment expression in the operation monitoring module is:

[0015] S = f(V(t), N(t), P(t))

[0016] Where: V(t) is the vibration signal, N(t) is the noise signal, P(t) is the vacuum pressure of the connecting pipe, and S is the equipment status.

[0017] Preferably, the comprehensive status analysis expression in the operation monitoring module is:

[0018] S=w1·V(t)+w2·N(t)+w3·P(t)

[0019] Wherein: w1, w2 and w3 are the weights of vibration signal, noise signal and exhaust pressure signal.

[0020] Preferably, the data analysis module runs the monitoring data expression as follows:

[0021]

[0022] The historical data expression is:

[0023]

[0024] Among them: historical data is D history ;

[0025] The state judgment expression is:

[0026] S = f(D(t), D history , P current )

[0027] Where: P current It is the current unit operating power.

[0028] Preferably, the expression for judging whether the equipment status is normal in the operation monitoring module is:

[0029]

[0030] Where: ∈ is the tolerance threshold, which is the power threshold for normal operation of the unit.

[0031] Preferably, the equipment status evaluation expression in the predictive maintenance module is:

[0032] S(t)=f(D(t),D history )

[0033] Where: S(t) is the current state of the device, D(t) is the current operation monitoring data, D history For historical data;

[0034] Calculate the probability of failure based on the current device status and historical failure records:

[0035] P fault (t) = g(S(t), D history )

[0036] P fault (t) is the failure probability.

[0037] Preferably, the fault prediction expression in the predictive maintenance module is:

[0038]

[0039] The maintenance plan expression in the predictive maintenance module is:

[0040] M(t)=h(P fault (t), D(t), D history )

[0041] Among them, function h generates maintenance recommendations based on failure probability, current data and historical data.

[0042] Preferably, the alarm module abnormality detection expression is:

[0043]

[0044] Where: θ anomaly is the abnormal threshold;

[0045] The alarm trigger expression is:

[0046]

[0047] Among them: Alarm(t) is the alarm triggering status.

[0048] Preferably, the control module alarm trigger time is:

[0049] Δt=tt alarm

[0050] Where: t alarm is the time when the alarm is triggered, and t is the current time.

[0051] Preferably, the control module will automatically adjust the parameters within 10 seconds after the alarm is triggered, and the time window T is defined as:

[0052]

[0053] Compared with the prior art, the present invention provides a cogeneration unit operation and maintenance management system, which has the following beneficial effects:

[0054] 1. The present invention obtains operating data in real time by monitoring the vibration signal V(t), noise signal N(t), and exhaust pressure P(t) of the connecting pipe of the equipment, and uses the function S=f(V(t), N(t), P(t)) to evaluate the equipment status. By comparing the real-time monitoring data D(t) with the historical data Dhistory and combining the current unit operating power Pcurrent, it is judged whether the equipment status is normal. The specific judgment expression is Sstatus=normal. If |D(t)-Dhistory|<∈ and Pcurrent>Pthreshold, it is abnormal, thereby achieving the beneficial effect of monitoring the unit operation status and discovering potential faults in advance.

[0055] 2. The present invention quantifies the equipment status by using the comprehensive status analysis expression S=w1·V(t)+w2·N(t)+w3·P(t), comprehensively considers the influence of multiple signals, and overcomes the limitations brought by a single signal. The system compares the real-time monitoring data D(t) with the historical data Dhistory, and combines the current unit operating power Pcurrent to determine whether the equipment status is normal. By comparing the real-time data with the historical records, it helps to identify abnormal modes and potential faults, avoids the limitations brought by relying on temperature monitoring, and achieves the beneficial effect of making the monitoring results more accurate without relying on temperature monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of the system of the present invention; DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] See also Figure 1 , a cogeneration unit operation and maintenance management system, the management system is composed of an operation monitoring module, a data analysis module, a predictive maintenance module, an alarm module and a control module, and the modules of the management system are connected by a local area network;

[0059] The operation monitoring module is used to monitor the operation data of the equipment, and the operation data vibration signal, noise and the air extraction pressure of the connecting pipe are analyzed to determine the status of the equipment;

[0060] The data analysis module is used to compare and analyze the collected information of the operation monitoring module with the historical data, and to determine whether the status is normal in combination with the current unit operating power;

[0061] The predictive maintenance module predicts the failure of equipment marked as abnormal based on the collected data and historical failure records, and arranges maintenance work in advance;

[0062] The alarm module is used to immediately trigger an alarm when an abnormality is detected or a potential failure is predicted;

[0063] The control module is used to automatically adjust parameters within 10 seconds after the alarm module occurs, and the parameters include the butterfly valve position and the regulating valve opening to prevent the occurrence of faults.

[0064] Preferably, the state judgment expression in the operation monitoring module is:

[0065] S = f(V(t), N(t), P(t))

[0066] Where: V(t) is the vibration signal, N(t) is the noise signal, P(t) is the vacuum pressure of the connecting pipe, and S is the equipment status.

[0067] Vibration signals fluctuate abnormally due to non-fault reasons. Relying on these data alone will ignore other key factors. By combining multiple signals, we can reduce misjudgments caused by single signal abnormalities. The combined use of multiple signals can more accurately identify the true status of the equipment because different types of signals can reveal different potential problems. For example, a certain vibration pattern combined with a specific noise abnormality indicates a specific type of fault.

[0068] Preferably, the comprehensive status analysis expression in the operation monitoring module is:

[0069] S=w1·V(t)+w2·N(t)+w3·P(t)

[0070] Wherein: w1, w2 and w3 are the weights of vibration signal, noise signal and exhaust pressure signal.

[0071] By assigning different weights to vibration signals, noise signals and exhaust pressure signals, the impact of these signals on the equipment status is comprehensively considered to provide a more comprehensive status evaluation. The weights w1, w2 and w3 are adjusted according to the actual situation and operation requirements of the equipment, making the status judgment more suitable for practical applications.

[0072] Preferably, the data analysis module runs the monitoring data expression as follows:

[0073]

[0074] The historical data expression is:

[0075]

[0076] Among them: historical data is D history ;

[0077] The state judgment expression is:

[0078] S = f(D(t), D history , P current )

[0079] Where: P current It is the current unit operating power.

[0080] The weighted analysis that comprehensively considers vibration, noise and exhaust pressure provides a more comprehensive equipment status assessment. Comparing real-time data with historical data helps to identify anomalies and track trend changes. Combined with the current unit operating power and tolerance threshold, the status judgment is more accurate and can adapt to different operating conditions. The clear normal and abnormal status judgment standards improve the system's usability and ease of operation.

[0081] Preferably, the expression for judging whether the equipment status is normal in the operation monitoring module is:

[0082]

[0083] Where: ∈ is the tolerance threshold, which is the power threshold for normal operation of the unit.

[0084] Through the real-time data D(t) and historical data D history The difference judgment can sensitively detect the changes in the operating status of the equipment. Slight change recognition: Setting the tolerance threshold ∈ distinguishes normal fluctuations from abnormal situations, so that the system can more accurately judge the true status of the equipment and avoid misjudgment. This expression takes into account both data changes and power levels. Only when both conditions are met can the equipment status be confirmed as "normal", which increases the reliability of status judgment and reduces the risk of misjudgment. current >P threshold The conditions ensure that the equipment operates under a reasonable load, which helps to eliminate abnormal conditions caused by insufficient or overload. The tolerance threshold ∈ is adjusted according to the characteristics of different equipment and the actual operating conditions, making the judgment standard flexible. In different operating environments or conditions, the judgment standard can be adjusted in time according to the actual situation to improve the adaptability of the system. Through real-time monitoring and accurate judgment of the equipment status, an alarm is issued before the problem becomes serious, so that maintenance measures can be taken in time to prevent equipment failure and shutdown. The setting of dual condition judgment improves the fault prediction capability and makes equipment maintenance more efficient and scientific.

[0085] Preferably, the equipment status evaluation expression in the predictive maintenance module is:

[0086] S(t)=f(D(t),D history )

[0087] Where: S(t) is the current state of the device, D(t) is the current operation monitoring data, D history For historical data;

[0088] Calculate the probability of failure based on the current device status and historical failure records:

[0089] P fault (t) = g(S(t), D history )

[0090] P fault (t) is the failure probability.

[0091] Preferably, the fault prediction expression in the predictive maintenance module is:

[0092]

[0093] The maintenance plan expression in the predictive maintenance module is:

[0094] M(t)=h(P fault (t), D(t), D history )

[0095] Among them, function h generates maintenance recommendations based on failure probability, current data and historical data.

[0096] Preferably, the alarm module abnormality detection expression is:

[0097]

[0098] Where: θ anomaly is the abnormal threshold;

[0099] The alarm trigger expression is:

[0100]

[0101] Among them: Alarm(t) is the alarm triggering status.

[0102] The alarm trigger expression combines the abnormal detection results A(t) and other fault indications F(t). The multi-condition trigger mechanism can ensure that the triggering of the alarm does not only rely on a single factor, which improves the reliability of the alarm system. The alarm will be triggered when the equipment status is abnormal or other fault indication conditions are met, reducing the risk of false alarms caused by single detection errors and improving the reliability of the system. Through this mechanism, the alarm is triggered immediately when the equipment status exceeds the threshold or the fault indication reaches the preset conditions, thereby quickly responding to potential problems.

[0103] Preferably, the control module alarm trigger time is:

[0104] Δt=tt alarm

[0105] Where: t alarm is the time when the alarm is triggered, and t is the current time.

[0106] Preferably, the control module will automatically adjust the parameters within 10 seconds after the alarm is triggered, and the time window T is defined as:

[0107]

[0108] By calculating Δt, the system can accurately know the time that has passed since the alarm was triggered, which is very important for time-sensitive automatic adjustment operations. It helps to ensure that the adjustment operation is performed within the predetermined time window. The difference between the alarm trigger time and the current time is calculated in real time, and the system status changes are responded to immediately to ensure that potential problems are handled in a timely manner. Setting the time window T to 1 (valid) or 0 (invalid) ensures that the parameters are automatically adjusted within 10 seconds after the alarm is triggered, ensuring that the system makes adjustments quickly when an alarm occurs, and corrects equipment operation problems in a timely manner. The setting of the time window ensures that the adjustment operation does not lag behind the alarm trigger, which helps to respond quickly and take measures when the equipment is abnormal to prevent the problem from expanding. The definition of the time window makes the control logic simple and clear. As long as it is within 10 seconds after the alarm is triggered, the automatic adjustment operation will be enabled. This clear time window definition reduces complexity and simplifies system design. The implementation of the time window logic is relatively simple, which is convenient for programming and operation in the control module, reducing the difficulty of system implementation.

[0109] Automatically adjusting parameters after an alarm is triggered helps the system quickly return to normal, reducing the need for human intervention. This helps improve the stability and self-healing capabilities of the system, and the automatic adjustment mechanism reduces errors caused by manual operation, making the system's response more consistent and reliable.

[0110] Vibration signals reflect the mechanical condition of the equipment, such as imbalance, looseness, wear and tear. These problems usually produce specific vibration patterns when the equipment is running. Different types of mechanical problems will cause different vibration frequencies and amplitudes. By analyzing these vibration signals, the health status of the equipment can be identified.

[0111] Noise signals reveal abnormal sounds during equipment operation, including friction sounds, impact sounds, etc. These sounds are signs of equipment failure. Noise analysis helps identify mechanical and fluid dynamic problems and is an important supplement to detecting equipment abnormalities.

[0112] The exhaust pressure reflects the fluid dynamics performance of the equipment and can display the gas flow conditions and operating efficiency of the equipment. Abnormal changes in pressure indicate unstable gas flow, leading to performance degradation or failure of the equipment.

[0113] The system uses a set algorithm to evaluate the overall status of the equipment by combining vibration, noise and pressure signals. For example, if the vibration is too large, the noise is abnormal, and the pressure fluctuates greatly, the system will judge that there is a problem with the equipment. By combining multiple indicators, the system can more accurately reflect the actual condition of the equipment. It can provide more comprehensive information than a single indicator (including temperature).

[0114] The system assigns different weights to different monitoring signals. For example, vibration has a greater impact on the equipment status than noise, so it is given a higher weight. Weight analysis enables the system to adjust the influence of different signals on the equipment status judgment according to actual conditions and improve the accuracy of the assessment.

[0115] The system compares the currently collected real-time data with historical data. Through this comparison, the operating trends and abnormal changes of the equipment are discovered. The comparative analysis can identify data that does not conform to the normal operating mode, thereby early warning of potential problems. If the current vibration mode deviates significantly from the normal mode in the historical record, there is a problem.

[0116] Analyze the trend changes of equipment operation data. For example, a long-term upward trend in vibration signals indicates potential mechanical wear. Trend analysis can provide a long-term perspective on the health of equipment and help predict and prevent failures.

[0117] The system calculates the probability of equipment failure based on current status and historical data. If the probability of failure is high, the system will issue an alarm and recommend maintenance. Predictive maintenance allows intervention before the failure actually occurs, thereby reducing equipment downtime and repair costs.

[0118] Based on the calculated failure probability, the system generates a specific maintenance plan. For example, if the failure probability of a certain component exceeds a threshold, an inspection and maintenance for that component is scheduled. This approach is more effective than traditional scheduled maintenance because it is based on actual data and failure probability rather than fixed time intervals.

[0119] The system sets abnormal thresholds, and when it detects that the device status exceeds these thresholds, an alarm is triggered immediately.

[0120] Providing real-time alarms helps operators respond quickly to potential problems and prevent them from getting worse. After the alarm is triggered, the system can automatically adjust equipment parameters, including butterfly valve position and regulating valve opening, in a short time to prevent the problem from expanding.

[0121] Automatic adjustment intervenes when a problem just occurs, reducing delays in manual operations and improving the system's response speed.

[0122] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations may be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. The operation and maintenance management system of the combined heat and power plant is characterized by: The management system is composed of an operation monitoring module, a data analysis module, a predictive maintenance module, an alarm module and a control module, and the modules of the management system are connected by a local area network; The operation monitoring module is used to monitor the operation data of the equipment, and the operation data vibration signal, noise and the air extraction pressure of the connecting pipe are analyzed to determine the status of the equipment; The data analysis module is used to compare and analyze the collected information of the operation monitoring module with the historical data, and to determine whether the status is normal in combination with the current unit operating power; The predictive maintenance module predicts the failure of equipment marked as abnormal based on the collected data and historical failure records, and arranges maintenance work in advance; The alarm module is used to immediately trigger an alarm when an abnormality is detected or a potential failure is predicted; The control module is used to automatically adjust parameters within 10 seconds after the alarm module occurs, and the parameters include the butterfly valve position and the regulating valve opening to prevent the occurrence of faults.

2. The operation and maintenance management system for a combined heat and power plant according to claim 1, characterized in that: The state judgment expression in the operation monitoring module is: S=f(V(t),N(t),P(t)) Where: V(t) is the vibration signal, N(t) is the noise signal, P(t) is the vacuum pressure of the connecting pipe, and S is the equipment status.

3. The operation and maintenance management system for a combined heat and power plant according to claim 2, characterized in that: The comprehensive state analysis expression in the operation monitoring module is: S=w1·V(t)·+w2·N(t)+w3·P(t) Wherein: w1, w2 and w3 are the weights of vibration signal, noise signal and exhaust pressure signal.

4. The operation and maintenance management system for a combined heat and power plant according to claim 3, characterized in that: The data analysis module runs the monitoring data expression as follows: The historical data expression is: Among them: historical data is D history ; The state judgment expression is: S=f(D(t),D history ,P current ) Where: P current It is the current unit operating power.

5. The operation and maintenance management system for a combined heat and power plant according to claim 4, characterized in that: The expression for judging whether the equipment status is normal in the operation monitoring module is: Where: Φ is the tolerance threshold, which is the power threshold for normal operation of the unit.

6. The operation and maintenance management system for a cogeneration unit according to claim 1, characterized in that: The equipment status evaluation expression in the predictive maintenance module is: S(t)=f(D(t),D history ) Where: S(t) is the current state of the device, D(t) is the current operation monitoring data, D history For historical data; Calculate the probability of failure based on the current device status and historical failure records: P fault (t)=g(S(t),D history ) P fault (t) is the failure probability.

7. The operation and maintenance management system for a combined heat and power plant according to claim 6, characterized in that: The fault prediction expression in the predictive maintenance module is: The maintenance plan expression in the predictive maintenance module is: M(t)=h(P fault (t),D(t),D history ) Among them, function h generates maintenance recommendations based on failure probability, current data and historical data.

8. The operation and maintenance management system for a combined heat and power plant according to claim 1, characterized in that: The abnormal detection expression of the alarm module is: Where: θ anomaly is the abnormal threshold; The alarm trigger expression is: Among them: Alarm(t) is the alarm triggering status.

9. The operation and maintenance management system for a combined heat and power plant according to claim 1, characterized in that: The control module alarm trigger time is: Δt=t-t alarm Where: t alarm is the time when the alarm is triggered, and t is the current time.

10. The operation and maintenance management system for a combined heat and power plant according to claim 1, characterized in that: The control module will automatically adjust the parameters within 10 seconds after the alarm is triggered, and the time window T is defined as:

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