Intelligent monitoring and protection method for diesel generator sets

Abstract

The present invention relates to the technical field of diesel generator sets, and specifically to an intelligent monitoring and protection method for diesel generator sets, comprising the following steps: S1, diesel generator set operating status monitoring: real-time collection of diesel generator set operating data; S2, exhaust gas composition monitoring: real-time monitoring of oxygen, carbon dioxide, nitrogen oxides, and carbon monoxide composition data in exhaust gas; S3, exhaust gas composition-driven combustion optimization: evaluating current combustion efficiency and emission levels, and automatically adjusting combustion parameters; S4, data analysis: determining whether the generator set is in normal working condition; S5, intelligent protection and fault alarm: automatically triggering an alarm and initiating protective measures. The present invention ensures that the emissions of the diesel generator set meet environmental protection standards, reduces harmful gas emissions, mitigates negative impacts on the environment, and ensures efficient and low-emission operation of the unit under different loads and operating conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of diesel generator sets, and in particular to an intelligent monitoring and protection method for diesel generator sets. Background Art

[0002] Diesel generator sets are widely used in industrial, commercial and residential areas for backup power supply, especially in areas with unstable power systems or insufficient power supply. Their role is crucial. However, diesel generator sets will face problems such as low efficiency, excessive emissions and equipment aging during long-term operation. These problems will not only reduce power generation efficiency, but also increase environmental pollution and equipment maintenance costs. Therefore, how to improve the operating efficiency of diesel generator sets, reduce exhaust emissions and ensure equipment safety has become a key issue that needs to be urgently addressed in the industry.

[0003] At present, the operating status monitoring and management of diesel generator sets mainly rely on manual inspections and regular maintenance, which are subject to problems of monitoring lag and untimely response. Although some intelligent monitoring systems have been used to monitor the basic operating data of the units (such as speed, temperature, load, etc.), these systems often lack real-time monitoring of exhaust gas components, making it difficult to comprehensively evaluate the combustion efficiency and emission levels of the units. In addition, most existing monitoring systems cannot timely analyze and automatically optimize the combustion process, resulting in low combustion efficiency, excessive emissions, and even possible failures or equipment damage. Existing fault diagnosis methods are usually limited to post-analysis and cannot effectively prevent and solve problems in a timely manner, making it impossible to achieve efficient, low-emission, and safe intelligent operation.

[0004] The purpose of the present invention is to provide an intelligent monitoring and protection method for diesel generator sets, which can ensure that the unit is always in an efficient, safe and environmentally friendly operating state through data preprocessing, combustion efficiency evaluation, combustion parameter adjustment and other means, and provide an energy-saving, emission-reducing, environmentally friendly and intelligent diesel generator set management solution. Summary of the Invention

[0005] The present invention provides an intelligent monitoring and protection method for a diesel generator set.

[0006] The intelligent monitoring and protection method for diesel generator sets includes the following steps:

[0007] S1, diesel generator set operating status monitoring: real-time collection of diesel generator set operating data, including speed, load, voltage, and temperature;

[0008] S2, exhaust gas composition monitoring: Install an exhaust gas composition sensor in the exhaust unit of the diesel generator set to monitor the composition data of oxygen, carbon dioxide, nitrogen oxides, and carbon monoxide in the exhaust gas in real time;

[0009] S3, exhaust gas composition-driven combustion optimization: Based on the results of exhaust gas composition monitoring, the system evaluates the current combustion efficiency and emission levels and automatically adjusts combustion parameters, including injection quantity, injection timing, and air supply, through an adaptive control algorithm. Specifically, it includes:

[0010] S31, data preprocessing: preprocessing the monitored exhaust gas composition data, including normalization and missing value processing;

[0011] S32, combustion efficiency and emission level assessment: Based on the pre-treated exhaust gas composition data, the current combustion efficiency is assessed and whether the emission level meets the standards is determined;

[0012] S33, combustion parameter adjustment: Based on the results of the combustion efficiency and emission level assessment, a combustion optimization strategy is formulated and combustion parameters, including injection quantity, injection timing, and air supply, are adjusted through an adaptive control algorithm;

[0013] S4, Data Analysis: Comprehensively analyze the collected diesel generator set operating data and adjusted combustion parameters to determine whether the generator set is in normal working condition. Utilize the fault diagnosis model to identify abnormal conditions, including decreased combustion efficiency, excessive emissions, and equipment failure.

[0014] S5, intelligent protection and fault alarm: Based on the identified abnormal conditions, it automatically triggers an alarm and initiates protective measures, including limiting load, adjusting operating data or shutting down the unit.

[0015] Optionally, the diesel generator set operating status monitoring in S1 includes:

[0016] S11, speed collection: The speed sensor installed on the diesel generator set is used to detect and collect the speed data of the generator set in real time;

[0017] S12, load monitoring: real-time detection and collection of load data of diesel generator sets through load sensors;

[0018] S13, voltage monitoring: by installing a voltage sensor in the electrical unit of the unit, the output voltage data of the unit is detected and collected in real time;

[0019] S14, temperature collection: Real-time detection and collection of the operating temperature data of the diesel generator set through the temperature sensor.

[0020] Optionally, the data preprocessing in S31 includes:

[0021] S311, data normalization: normalizing the exhaust gas composition data;

[0022] S312, missing value processing: For missing exhaust gas composition data, interpolation algorithm is used to supplement them.

[0023] Optionally, the combustion efficiency and emission level evaluation in S32 includes:

[0024] S321, Combustion efficiency evaluation: Based on the pre-treated exhaust gas composition data, the efficiency η of the combustion process is evaluated. comb ;

[0025] S322, Emission level assessment: Based on the exhaust gas composition data, determine whether the emission level meets the standard. If the carbon monoxide concentration C CO Do not exceed the carbon monoxide limit CO And the carbon dioxide concentration C CO2 Do not exceed the CO2 limit CO2 And the nitrogen oxide concentration C NOx Not exceeding the nitrogen oxide limit NOx , then the emission level meets the standard;

[0026] S323, comprehensive evaluation of combustion efficiency and emission level: Comprehensively evaluate the current combustion efficiency and emission level, and output the evaluation results. comb When the combustion efficiency is not lower than the minimum standard and the emission level meets the emission standard, it is considered normal operation. comb Abnormal operation occurs when the combustion efficiency falls below the minimum standard or the emission level does not meet the emission standard.

[0027] Optionally, the combustion parameter adjustment in S33 includes:

[0028] S331, Optimization Strategy Development: Based on the results of the combustion efficiency and emission level assessment, if the assessment result indicates abnormal operation, the optimization strategy is implemented to adjust the combustion parameters;

[0029] S332, fuel injection quantity adjustment: By adjusting the fuel injection quantity, the fuel supply quantity is adjusted, thereby affecting the combustion efficiency;

[0030] S333, injection timing adjustment: Adjust the injection timing based on the combustion efficiency evaluation results. When the combustion efficiency is below the minimum standard or the emission level does not meet the standard, the injection timing is delayed to reduce NOx emissions.

[0031] S334, Air supply adjustment: By adjusting the air supply, the air-fuel mixture ratio is optimized, thereby improving combustion efficiency and reducing emissions.

[0032] Optionally, the data analysis in S4 includes:

[0033] S41, comprehensive data analysis: conduct a comprehensive analysis of the collected diesel generator set operating data and the adjusted combustion parameters. By comparing the current status with the preset standard parameters, the operating performance of the diesel generator set under different working conditions is analyzed to determine whether it is in normal working condition;

[0034] S42, abnormal state identification: When it is identified that the diesel generator set is in an abnormal state, the fault diagnosis model is used to classify the abnormal state, including decreased combustion efficiency, excessive emissions, and equipment failure.

[0035] Optionally, the comprehensive data analysis in S41 includes:

[0036] S411, setting standard values: setting standard values ​​for each operating data and combustion parameter based on the optimal operating conditions of the diesel generator set;

[0037] S412, comprehensive analysis and judgment: The operating data and the adjusted combustion parameters are comprehensively analyzed with the set standard values ​​by the weighted average method to determine whether the diesel generator set is in normal working condition.

[0038] Optionally, the fault diagnosis model in S42 adopts a k-means clustering model, and the k-means clustering model includes:

[0039] S421, data preparation: obtaining the operating data of the diesel generator set and the adjusted combustion parameters;

[0040] S422, determining the number of clusters: using the elbow rule to select the optimal k value, the elbow rule selects the most appropriate number of clusters k by calculating the sum of squared errors (SSE) under different k values;

[0041] S423, initializing cluster centers: randomly selecting k data points as initial cluster centers;

[0042] S424, clustering process: The clustering process specifically includes:

[0043] Assign data points: For each data point, calculate its Euclidean distance to each cluster center and assign it to the cluster with the closest distance;

[0044] Update cluster center: For each cluster, calculate the cluster center as the mean of all data points in the cluster;

[0045] Convergence judgment: When the cluster center no longer changes, stop the iteration, at which point the clustering is complete;

[0046] S425, abnormal state classification: When the combustion efficiency of a cluster is lower than the minimum combustion efficiency standard, it is judged as a decrease in combustion efficiency; when the exhaust gas components in a cluster exceed the emission standard, it is judged as an emission exceeding the standard; when the difference between the state of a data point and that of an adjacent cluster exceeds the fault threshold, it is judged as an equipment failure.

[0047] Optionally, the intelligent protection and fault alarm in S5 includes:

[0048] S51, abnormal state triggering alarm: When the diesel generator set is identified as being in an abnormal state, the alarm is automatically triggered and the relevant operators are notified through the visual interface, SMS, and email;

[0049] S52, start protection measures: After the alarm is triggered, the corresponding protection measures will be automatically started according to the type of abnormal state, including:

[0050] Load limit: If the combustion efficiency decreases or the emission exceeds the standard, the unit load will be automatically reduced;

[0051] Adjust operating parameters: If abnormal operation of the equipment is found, adjust the operating parameters (such as adjusting the injection amount, injection timing, air supply, etc.);

[0052] Shut down the unit: If the abnormal state cannot be resolved through adjustment, the emergency shutdown protection is triggered and the unit is shut down.

[0053] Beneficial effects of the present invention:

[0054] The present invention can accurately evaluate and optimize the combustion process by collecting the unit's operating data and exhaust gas composition in real time and combining it with an adaptive control algorithm. The exhaust gas composition-driven combustion optimization can automatically adjust combustion parameters such as injection quantity, injection timing and air supply according to real-time data, thereby improving combustion efficiency and reducing energy waste. At the same time, based on the monitoring and evaluation of emission levels, it can ensure that the emissions of the diesel generator set meet environmental protection standards, reduce harmful gas emissions, reduce negative impacts on the environment, and ensure efficient and low-emission operation of the unit under different loads and operating conditions.

[0055] The present invention can timely discover potential faults or abnormalities by comprehensively monitoring and analyzing the operating status of the diesel generator set. Through the intelligent protection and fault alarm mechanism, when the system identifies abnormal conditions such as decreased combustion efficiency, excessive emissions or equipment failure, it can automatically trigger an alarm and initiate corresponding protection measures. These measures include limiting the unit load, adjusting operating parameters or shutting down the unit when necessary, thereby effectively preventing equipment damage, excessive emissions or other safety hazards, ensuring the safe and reliable operation of the unit, and significantly improving the operating stability and safety of the diesel generator set through accurate fault diagnosis and timely protection measures.

[0056] The present invention, through real-time monitoring of exhaust gas components and data preprocessing, can eliminate data noise and missing values, ensure data quality and the accuracy of system response, and comprehensively evaluate the mechanism of combustion efficiency and emission levels, which helps to timely discover the risks of incomplete combustion or excessive emissions, and take effective adjustment measures before the abnormality occurs, thereby extending the life of the equipment, improving the equipment utilization efficiency, reducing maintenance costs, and optimizing resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flow chart of a monitoring and protection method according to an embodiment of the present invention;

[0059] Figure 2 Schematic diagram of combustion optimization driven by exhaust gas components according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. It is also noted that, to provide a more detailed description, the following embodiments are best and preferred embodiments, and those skilled in the art may employ alternative methods for implementing certain known technologies. Furthermore, the accompanying drawings are intended only to provide a more detailed description of the embodiments and are not intended to limit the present invention.

[0061] like Figure 1-Figure 2 As shown, the intelligent monitoring and protection method for diesel generator sets includes the following steps:

[0062] S1, diesel generator set operating status monitoring: real-time collection of diesel generator set operating data, including speed, load, voltage, and temperature;

[0063] S2, exhaust gas composition monitoring: Install an exhaust gas composition sensor in the exhaust unit of the diesel generator set to monitor the composition data of oxygen, carbon dioxide, nitrogen oxides, and carbon monoxide in the exhaust gas in real time;

[0064] S3, exhaust gas composition-driven combustion optimization: Based on the results of exhaust gas composition monitoring, the system evaluates the current combustion efficiency and emission levels. It then uses an adaptive control algorithm to automatically adjust combustion parameters, including injection quantity, injection timing, and air supply, to optimize the combustion process, ensuring optimal combustion efficiency and reducing emissions under different loads and operating conditions. Specifically, it includes:

[0065] S31, data preprocessing: preprocessing the monitored exhaust gas composition data, including normalization and missing value processing;

[0066] S32, combustion efficiency and emission level assessment: Based on the pre-treated exhaust gas composition data, the current combustion efficiency is assessed and whether the emission level meets the standards is determined;

[0067] S33, combustion parameter adjustment: Based on the results of the combustion efficiency and emission level assessment, a combustion optimization strategy is formulated and combustion parameters, including injection quantity, injection timing, and air supply, are adjusted through an adaptive control algorithm;

[0068] S4, Data Analysis: Comprehensively analyze the collected diesel generator set operating data and adjusted combustion parameters to determine whether the generator set is in normal working condition. Utilize the fault diagnosis model to identify abnormal conditions, including decreased combustion efficiency, excessive emissions, and equipment failure.

[0069] S5, intelligent protection and fault alarm: Based on the identified abnormal conditions, it automatically triggers an alarm and initiates protective measures, including limiting load, adjusting operating data, or shutting down the unit to prevent further equipment damage or environmental pollution, ensuring the safe operation of the diesel generator set;

[0070] Through the above content, the operating data and exhaust gas composition of the diesel generator set are monitored in real time, and combined with adaptive control and fault diagnosis algorithms, the combustion process is optimized, the combustion efficiency is improved, and harmful emissions are reduced. At the same time, the unit is ensured to be in the best operating state. Through data preprocessing, combustion efficiency evaluation, combustion parameter adjustment and intelligent protection mechanism, the unit operation can be automatically adjusted under different loads and working conditions to avoid equipment failures, reduce emissions, and improve the safety and environmental protection performance of the unit. It has significant advantages in energy saving, emission reduction and safety protection, ensuring the efficient, stable and environmentally friendly operation of the diesel generator set.

[0071] The diesel generator set operating status monitoring in S1 includes:

[0072] S11, speed collection: The speed sensor installed on the diesel generator set is used to detect and collect the speed data of the generator set in real time;

[0073] S12, load monitoring: real-time detection and collection of load data of diesel generator sets through load sensors;

[0074] S13, voltage monitoring: by installing a voltage sensor in the electrical unit of the unit, the output voltage data of the unit is detected and collected in real time;

[0075] S14, temperature collection: real-time detection and collection of the operating temperature data of the diesel generator set through the temperature sensor;

[0076] Through the above content, the working status of the unit can be fully and accurately reflected. By deploying sensors at various key locations and timely detecting and transmitting data, it can not only help quickly discover potential operating anomalies, but also achieve accurate monitoring and optimized management of the unit's operating status.

[0077] Data preprocessing in S31 includes:

[0078] S311, data normalization: normalize the exhaust gas composition data to ensure that all data have the same dimension, expressed as:

[0079]

[0080] Among them, X is the original value of a certain exhaust gas component, X min and X max are the minimum and maximum values ​​of the exhaust gas components, respectively, X norm is the normalized value;

[0081] S312, missing value processing: For missing exhaust gas composition data, interpolation algorithm is used to supplement it, which is expressed as:

[0082]

[0083] Among them, X i and X i+1 is a known data point, t i and t i+1 is the corresponding timestamp, t missing is the time point where the missing value is located, X missing is the missing value after interpolation;

[0084] Through the above content, normalization unifies the dimension of the data, allowing data collected by different sensors to be compared and processed under the same standard. Missing value processing ensures the integrity of the data and avoids analytical errors caused by missing values. It provides high-quality input data for subsequent combustion efficiency evaluation and intelligent protection decision-making, thereby improving the accuracy and response speed of the system, helping to optimize the combustion process, reduce emissions and effectively protect equipment, ensuring the safe and efficient operation of diesel generator sets.

[0085] The combustion efficiency and emission level assessment in S32 includes:

[0086] S321, Combustion efficiency evaluation: Based on the pre-treated exhaust gas composition data, the efficiency η of the combustion process is evaluated. comb , expressed as:

[0087]

[0088] Among them, Qinput is the input fuel heat, Q exhaust is the heat of the exhaust gas;

[0089] Q exhaust =C CO2 ·V exhaust Calorific value CO2 +C CO ·V exhaust Calorific value CO ;

[0090] Among them, C CO2 and C CO are the concentrations of carbon dioxide and carbon monoxide, V exhaust is the flow rate of exhaust gas, calorific value CO2 and calorific value CO are the calorific values ​​of carbon dioxide and carbon monoxide, respectively;

[0091] S322, Emission level assessment: Based on the exhaust gas composition data, determine whether the emission level meets the standard. If the carbon monoxide concentration C CO Do not exceed the carbon monoxide limit CO And the carbon dioxide concentration C CO2 Do not exceed the CO2 limit CO2 And the nitrogen oxide concentration C NOx Not exceeding the nitrogen oxide limit NOx , then the emission level meets the standard;

[0092] Carbon monoxide limit CO , carbon dioxide limits CO2 , nitrogen oxide limits NOx It is the limit value of emission substances, which is set according to environmental protection standards;

[0093] S323, comprehensive evaluation of combustion efficiency and emission level: Comprehensively evaluate the current combustion efficiency and emission level, and output the evaluation results. comb When the combustion efficiency is not lower than the minimum standard and the emission level meets the emission standard, it is considered normal operation. comb When the combustion efficiency falls below the minimum standard or the emission level does not meet the emission standard, it indicates abnormal operation;

[0094] Through the above content, the operating status of the unit can be monitored and optimized in real time. The evaluation of combustion efficiency can help determine the efficiency of energy utilization, ensure that fuel is effectively utilized, and reduce unnecessary energy waste. The emission level evaluation ensures that exhaust emissions meet environmental protection standards, reduces negative impacts on the environment, and avoids environmental penalties due to excessive emissions. In addition, this comprehensive evaluation can identify potential abnormal conditions in advance, such as decreased combustion efficiency or excessive emissions, trigger alarms and make adjustments in time, effectively prevent equipment damage or environmental pollution, and improve the safety, reliability and economy of diesel generator sets. Through this evaluation method, users can optimize the operating parameters of the unit, keep the equipment in the best working condition, extend its service life, and comply with environmental protection requirements.

[0095] The combustion parameter adjustment in S33 includes:

[0096] S331, Optimization Strategy Development: Based on the results of the combustion efficiency and emission level assessment, if the assessment result indicates abnormal operation, the optimization strategy is implemented to adjust the combustion parameters;

[0097] S332, fuel injection quantity adjustment: By adjusting the fuel injection quantity, the fuel supply quantity is adjusted, thereby affecting the combustion efficiency, which is expressed as:

[0098] Q adjusted =Q current ×(1+k1×(η comb -η target ));

[0099] Among them, Q adjusted is the adjusted injection quantity, Q current is the current injection amount, η comb is the current combustion efficiency, η target is the set target combustion efficiency, k1 is the adjustment coefficient;

[0100] S333, injection timing adjustment: Based on the combustion efficiency evaluation results, the injection timing is adjusted. When the combustion efficiency is lower than the minimum standard or the emission level does not meet the standard, the injection timing is delayed to reduce NOx emissions. It is expressed as:

[0101] θ adjusted =θ current +k2×(η target -η comb );

[0102] Among them, θ adjusted is the injection timing after adjustment, θ current is the current injection timing, k2 is the timing adjustment coefficient, η target is the target combustion efficiency;

[0103] S334, air supply adjustment: By adjusting the air supply, the air-fuel mixture ratio is optimized, thereby improving combustion efficiency and reducing emissions, expressed as:

[0104] A adjusted =A current ×(1+k3×(η target -η comb ));

[0105] Among them, A adjusted is the regulated air supply, A current is the current air supply, k3 is the air supply adjustment coefficient;

[0106] Through the above content, the combustion process can be optimized in real time to ensure that the diesel generator set can maintain the best combustion efficiency and effectively reduce harmful emissions under different loads and working conditions. By precisely adjusting the combustion parameters, not only the economy and energy utilization efficiency of the combustion process are improved, but also the environmental pollution caused by excessive emissions is reduced, and the stability and safety of the equipment are enhanced. At the same time, intelligent adjustments can be made according to real-time monitoring data to reduce manual intervention and improve the automation level and response speed of the system.

[0107] The data analysis in S4 includes:

[0108] S41, comprehensive data analysis: conduct a comprehensive analysis of the collected diesel generator set operating data and the adjusted combustion parameters. By comparing the current status with the preset standard parameters, the operating performance of the diesel generator set under different working conditions is analyzed to determine whether it is in normal working condition;

[0109] S42, abnormal state identification: When the diesel generator set is identified as being in an abnormal state, the fault diagnosis model is used to classify the abnormal state, including combustion efficiency reduction, emission exceeding the standard, and equipment failure;

[0110] Through the above content, the unit status can be monitored in real time, potential anomalies can be accurately identified, and fault reports can be generated in a timely manner, effectively improving the working efficiency and safety of the generator set, reducing downtime or environmental pollution caused by equipment failure or excessive emissions, and providing accurate fault prediction and optimization suggestions, thereby reducing maintenance costs, improving equipment operating reliability and extending service life.

[0111] The comprehensive data analysis in S41 includes:

[0112] S411, set standard values: set the standard values ​​of each operating data and combustion parameter based on the optimal operating conditions of the diesel generator set, including:

[0113] S4111, collect data on optimal operating conditions: Determine the optimal operating conditions (e.g., maximum load, optimal combustion efficiency, minimum emission level, etc.) based on the design specifications and historical operating data of the diesel generator set. Collect key parameters from actual operating data and theoretical models, including speed, load, voltage, temperature, and exhaust gas composition (CO, NOx, CO2). Through statistical analysis of historical operating data, derive the performance of the diesel generator set under different operating conditions, thereby forming a standard set of optimal operating conditions that includes various operating data and combustion parameters. For example, assuming the optimal operating conditions of the diesel generator set are 1800 RPM, 90% load, 85°C, and 300 ppm NOx concentration in the exhaust, these data will be used as standard values.

[0114] S4112, determine the standard value range for each operating data and combustion parameter: In combination with the design objectives and performance requirements of the diesel generator set, set a reasonable standard value range for each key parameter. The speed is based on the rated speed of the unit, and a reasonable speed range is set (such as 1800RPM±5%). The load is set according to the standard value range of the maximum load of the unit (such as the load should be maintained at 90%-95%). The temperature is set according to the maximum operating temperature range of the engine (such as the temperature range of 85℃ to 95℃). The exhaust gas composition sets the maximum allowable concentration of various exhaust gases (such as NOx, CO, CO2), and takes into account relevant environmental protection standards (such as NOx concentration shall not exceed 300ppm, CO concentration shall not exceed 150ppm, etc.). For example, when the load of the diesel generator set is 90%-95%, the standard value of NOx in the exhaust gas is set to 300ppm and CO is set to 150ppm. The temperature standard value is set to 85℃;

[0115] S412, comprehensive analysis and judgment: The operating data and the adjusted combustion parameters are comprehensively analyzed with the set standard values ​​by the weighted average method to determine whether the diesel generator set is in normal working condition, which is expressed as:

[0116]

[0117] Among them, w i is the weight of the i-th parameter, X i is the currently collected operating data or the combustion parameters after adjustment, X std is the corresponding standard value, n is the total number of parameters, S status is the work status score after comprehensive analysis, if S status Below the set state threshold S th , it means that the diesel generator set is not in normal working condition;

[0118] Status thresholds are set based on historical data and optimal operating conditions, including:

[0119] Collect historical data and optimal operating condition data: Collect operating data and combustion parameters of diesel generator sets under different operating conditions, including speed, load, combustion efficiency, and exhaust gas composition;

[0120] Determine the scoring range for normal working conditions: Based on historical data and optimal operating condition data, determine the comprehensive scoring range for the diesel generator set under normal working conditions. The comprehensive score of the unit under optimal operating conditions should be close to 1 (i.e., optimal condition), while under poor conditions, the comprehensive score will be significantly lower than 1. The score under normal working conditions is set between 0.85 and 1, and the score under abnormal working conditions is set to less than 0.85;

[0121] Set the threshold of comprehensive score: According to the scoring range of historical data and optimal working condition data, set the comprehensive score threshold S th , expressed as:

[0122]

[0123] Among them, S status,avg It is the average comprehensive score of all normal operating conditions in historical data. 0.85 is the minimum threshold set for normal operating conditions, indicating that if the comprehensive score is lower than 0.85, the unit may be in an abnormal state.

[0124] Through the above content, it is possible to effectively identify whether the unit is in normal working condition. This process uses historical data and preset standards to automatically detect potential abnormal conditions, and discover problems such as decreased combustion efficiency, excessive emissions or equipment failures through dynamic analysis. This greatly improves the safety and reliability of unit operation, and can provide real-time feedback on the health status of the unit, issue early warnings and take repair measures, reduce the occurrence of failures, reduce maintenance costs, optimize energy utilization, and ensure environmental compliance.

[0125] The fault diagnosis model in S42 adopts the k-means clustering model, which includes:

[0126] S421, data preparation: obtaining the operating data of the diesel generator set and the adjusted combustion parameters;

[0127] S422, determine the number of clusters: use the elbow rule to select the best k value. The elbow rule selects the most appropriate number of clusters k by calculating the sum of squared errors (SSE) under different k values, which is expressed as:

[0128]

[0129] Among them, x i is a data point, μ k is the cluster center, n is the number of data points;

[0130] S423, initializing cluster centers: randomly selecting k data points as initial cluster centers;

[0131] S424, clustering process: The clustering process specifically includes:

[0132] Assigning data points: For each data point, calculate its Euclidean distance to each cluster center and assign it to the cluster with the closest distance, expressed as:

[0133]

[0134] Among them, d(x i ,μ k ) is the data point x i To cluster center μ k The Euclidean distance, x ij is the data point x i The jth feature in μ kj is the cluster center μ k The jth feature in , m is the dimension of the feature;

[0135] Update cluster center: For each cluster, calculate the cluster center as the mean of all data points in the cluster, expressed as:

[0136]

[0137] Among them, |C k | is the number of data points in cluster k, C k is the set of all data points in cluster k;

[0138] Convergence judgment: When the cluster center no longer changes, stop the iteration, at which point the clustering is complete;

[0139] S425, abnormal status classification: When the combustion efficiency of a cluster is lower than the minimum combustion efficiency standard, it is determined to be a combustion efficiency decline; when the exhaust gas components in a cluster exceed the emission standard, it is determined to be an emission excess; when the difference between the status of a data point and that of an adjacent cluster exceeds the fault threshold, it is determined to be a device fault;

[0140] Fault thresholds are set based on data clustering results, historical data, and performance under normal operating conditions, including:

[0141] Collect historical data and normal operating parameters: Collect historical data of diesel generator sets under normal operating conditions, including speed, load, voltage, temperature, combustion efficiency and exhaust gas composition;

[0142] Calculate the standard deviation of normal operating parameters based on historical data: Calculate the standard deviation of each parameter based on historical data under normal operating conditions, expressed as:

[0143]

[0144] Among them, x i is the value of the parameter of item i in the historical data, μ is the mean value of the parameter under normal working conditions, σ x is the standard deviation of the parameter, and N is the number of historical data points;

[0145] Define the fault threshold: Set the fault threshold based on the standard deviation of historical data to measure whether the difference between the data point and the cluster exceeds the normal range, expressed as:

[0146] Fault threshold = μ + k·σ x ;

[0147] Among them, μ is the mean of historical data, k is a constant (set to 2 or 3), σ x is the standard deviation of historical data;

[0148] Through the above content, the normal operating mode and potential abnormal state of the unit under different operating conditions can be effectively identified. By comparing the newly collected data with the clustering results, it is possible to accurately determine whether abnormal conditions such as decreased combustion efficiency, excessive emissions or equipment failure have occurred. In addition, the k-means clustering model has strong adaptive capabilities and can dynamically adjust the cluster division according to changes in data to adapt to different working environments and load conditions, thereby improving the accuracy and real-time performance of fault diagnosis. At the same time, by setting appropriate fault thresholds, it can sensitively identify subtle changes in unit operation and provide early warning of potential faults, avoiding the lag and misjudgment rate of traditional methods. This not only improves the efficiency and accuracy of fault diagnosis, but also reduces maintenance costs and extends the service life of equipment.

[0149] The intelligent protection and fault alarm in S5 include:

[0150] S51, abnormal state triggering alarm: When the diesel generator set is identified as being in an abnormal state, the alarm is automatically triggered and the relevant operators are notified through the visual interface, SMS, and email;

[0151] S52, start protection measures: After the alarm is triggered, the corresponding protection measures will be automatically started according to the type of abnormal state, including:

[0152] Load Limitation: If combustion efficiency decreases or emissions exceed standards, the unit load will be automatically reduced to prevent further damage or excessive emissions;

[0153] Adjust operating parameters: If abnormal operation of the equipment is found, adjust the operating parameters (such as injection amount, injection timing, air supply, etc.) to optimize combustion or improve the operating status of the equipment;

[0154] Shut down the unit: If the abnormal state cannot be resolved through adjustment, the emergency shutdown protection is triggered and the unit is shut down to prevent further damage to the equipment or safety hazards;

[0155] Through the above content, the safety and reliability of diesel generator sets are effectively improved. When the unit encounters abnormal conditions such as decreased combustion efficiency, excessive emissions or equipment failure, the system can automatically initiate protection measures according to the specific situation, such as limiting load, adjusting operating parameters or emergency shutdown, thereby preventing further damage to the equipment or safety hazards. It not only reduces human intervention and improves fault response speed, but also can take effective measures at the early stage of the fault, reduce maintenance costs, extend equipment service life, ensure that the generator set operates in a safe and stable environment, and improve overall operating efficiency.

[0156] The present invention encompasses any alternatives, modifications, equivalents, and solutions that fall within the spirit and scope of the present invention. To provide a thorough understanding of the present invention, specific details are described in detail below in connection with the preferred embodiments of the present invention, but those skilled in the art will be able to fully understand the present invention without these detailed descriptions. Furthermore, to avoid unnecessary confusion regarding the essence of the present invention, well-known methods, processes, procedures, components, and circuits have not been described in detail.

[0157] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. Intelligent monitoring and protection method for diesel generator sets, characterized in that: The following steps are involved: S1, diesel generator set operating status monitoring: real-time collection of diesel generator set operating data, including speed, load, voltage, and temperature; S2, exhaust gas composition monitoring: Install an exhaust gas composition sensor in the exhaust unit of the diesel generator set to monitor the composition data of oxygen, carbon dioxide, nitrogen oxides, and carbon monoxide in the exhaust gas in real time; S3, exhaust gas composition-driven combustion optimization: Based on the results of exhaust gas composition monitoring, the system evaluates the current combustion efficiency and emission levels and automatically adjusts combustion parameters, including injection quantity, injection timing, and air supply, through an adaptive control algorithm. Specifically, it includes: S31, data preprocessing: preprocessing the monitored exhaust gas composition data, including normalization and missing value processing; S32, combustion efficiency and emission level assessment: Based on the pre-treated exhaust gas composition data, the current combustion efficiency is assessed and whether the emission level meets the standards is determined; the combustion efficiency and emission level assessment includes: S321, Combustion efficiency evaluation: Based on the pre-treated exhaust gas composition data, the efficiency η of the combustion process is evaluated. comb ; S322, Emission level assessment: Based on the exhaust gas composition data, determine whether the emission level meets the standard. If the carbon monoxide concentration C CO Do not exceed the carbon monoxide limit CO And the carbon dioxide concentration C CO2 Do not exceed the CO2 limit CO2 And the nitrogen oxide concentration C NOx Not exceeding the nitrogen oxide limit NOx , then the emission level meets the standard; S323, comprehensive evaluation of combustion efficiency and emission level: Comprehensively evaluate the current combustion efficiency and emission level, and output the evaluation results. comb When the combustion efficiency is not lower than the minimum standard and the emission level meets the emission standard, it is considered normal operation. comb When the combustion efficiency falls below the minimum standard or the emission level does not meet the emission standard, it indicates abnormal operation; S33, combustion parameter adjustment: Based on the results of the combustion efficiency and emission level assessment, a combustion optimization strategy is formulated and combustion parameters, including injection quantity, injection timing, and air supply, are adjusted through an adaptive control algorithm; S4, Data Analysis: Comprehensively analyze the collected diesel generator set operating data and adjusted combustion parameters to determine whether the generator set is in normal working condition. Utilize the fault diagnosis model to identify abnormal conditions, including decreased combustion efficiency, excessive emissions, and equipment failure. S5, intelligent protection and fault alarm: Based on the identified abnormal conditions, it automatically triggers an alarm and initiates protective measures, including limiting load, adjusting operating data or shutting down the unit.

2. The intelligent monitoring and protection method for diesel generator sets according to claim 1 is characterized in that: The diesel generator set operating status monitoring in S1 includes: S11, speed collection: The speed sensor installed on the diesel generator set is used to detect and collect the speed data of the generator set in real time; S12, load monitoring: real-time detection and collection of load data of diesel generator sets through load sensors; S13, voltage monitoring: by installing a voltage sensor in the electrical unit of the unit, the output voltage data of the unit is detected and collected in real time; S14, temperature collection: Real-time detection and collection of the operating temperature data of the diesel generator set through the temperature sensor.

3. The intelligent monitoring and protection method for diesel generator sets according to claim 1 is characterized in that: The data preprocessing in S31 includes: S311, data normalization: normalizing the exhaust gas composition data; S312, missing value processing: For missing exhaust gas composition data, interpolation algorithm is used to supplement them.

4. The intelligent monitoring and protection method for diesel generator sets according to claim 1 is characterized in that: The combustion parameter adjustment in S33 includes: S331, Optimization Strategy Development: Based on the results of the combustion efficiency and emission level assessment, if the assessment result indicates abnormal operation, the optimization strategy is implemented to adjust the combustion parameters; S332, fuel injection quantity adjustment: By adjusting the fuel injection quantity, the fuel supply quantity is adjusted, thereby affecting the combustion efficiency; S333, injection timing adjustment: Adjust the injection timing based on the combustion efficiency evaluation results. When the combustion efficiency is below the minimum standard or the emission level does not meet the standard, the injection timing is delayed to reduce NOx emissions. S334, Air supply adjustment: By adjusting the air supply, the air-fuel mixture ratio is optimized, thereby improving combustion efficiency and reducing emissions.

5. The intelligent monitoring and protection method for diesel generator sets according to claim 4 is characterized in that: The data analysis in S4 includes: S41, comprehensive data analysis: conduct a comprehensive analysis of the collected diesel generator set operating data and the adjusted combustion parameters. By comparing the current status with the preset standard parameters, the operating performance of the diesel generator set under different working conditions is analyzed to determine whether it is in normal working condition; S42, abnormal state identification: When it is identified that the diesel generator set is in an abnormal state, the fault diagnosis model is used to classify the abnormal state, including decreased combustion efficiency, excessive emissions, and equipment failure.

6. The intelligent monitoring and protection method for diesel generator sets according to claim 5, characterized in that: The comprehensive data analysis in S41 includes: S411, setting standard values: setting standard values ​​for each operating data and combustion parameter based on the optimal operating conditions of the diesel generator set; S412, comprehensive analysis and judgment: The operating data and the adjusted combustion parameters are comprehensively analyzed with the set standard values ​​by the weighted average method to determine whether the diesel generator set is in normal working condition.

7. The intelligent monitoring and protection method for diesel generator sets according to claim 6, characterized in that: The fault diagnosis model in S42 adopts a k-means clustering model, and the k-means clustering model includes: S421, data preparation: obtaining the operating data of the diesel generator set and the adjusted combustion parameters; S422, determining the number of clusters: using the elbow rule to select the optimal k value. The elbow rule selects the most appropriate number of clusters k by calculating the sum of squared errors under different k values; S423, initializing cluster centers: randomly selecting k data points as initial cluster centers; S424, clustering process: The clustering process specifically includes: Assign data points: For each data point, calculate its Euclidean distance to each cluster center and assign it to the cluster with the closest distance; Update cluster center: For each cluster, calculate the cluster center as the mean of all data points in the cluster; Convergence judgment: When the cluster center no longer changes, stop the iteration, at which point the clustering is complete; S425, abnormal state classification: When the combustion efficiency of a cluster is lower than the minimum combustion efficiency standard, it is judged as a decrease in combustion efficiency; when the exhaust gas components in a cluster exceed the emission standard, it is judged as an emission exceeding the standard; when the difference between the state of a data point and that of an adjacent cluster exceeds the fault threshold, it is judged as an equipment failure.

8. The intelligent monitoring and protection method for diesel generator sets according to claim 7, characterized in that: The intelligent protection and fault alarm in S5 include: S51, abnormal state triggering alarm: When the diesel generator set is identified as being in an abnormal state, the alarm is automatically triggered and the relevant operators are notified through the visual interface, SMS, and email; S52, start protection measures: After the alarm is triggered, the corresponding protection measures will be automatically started according to the type of abnormal state, including: Load limit: If the combustion efficiency decreases or the emission exceeds the standard, the unit load will be automatically reduced; Adjust operating parameters: If abnormal operation of the equipment is found, adjust the operating parameters; Shut down the unit: If the abnormal state cannot be resolved through adjustment, the emergency shutdown protection is triggered and the unit is shut down.

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