Method for dynamically analyzing working condition of unit by using data of monitoring system

Abstract

The invention discloses a method for dynamically analyzing unit working conditions by using monitoring system data. The method comprises the following steps: S1, determining an analysis object; the analysis object comprises an upper guide temperature, a top cover water level and a machine pit oil leakage tank oil level; s2, collecting data of an analysis object, and performing derivation processing on the data; s3, the K value obtained after derivation is directly displayed in an auxiliary picture of a monitoring system, so that screen monitoring personnel can visually know the change trend of the parameters; setting a high-low limit of the K value, and accessing the K value to a voice alarm and an alarm light word of a monitoring system; and S4, early warning and pre-control are set by using an analytic hierarchy process. By means of derivation analysis of monitoring system data, a K value obtained after analog quantity derivation is directly displayed in an auxiliary picture through an in-situ LCU, meanwhile, high and low limits of the K value are set to be connected into voice alarm and alarm light words of a monitoring system, and screen monitoring personnel can dynamically analyze the working condition of a unit conveniently.

Description

Technical Field

[0001] The invention belongs to the technical field of unit operating condition analysis, and in particular relates to a method for dynamically analyzing unit operating conditions by using monitoring system data. Background Art

[0002] The monitoring screen of auxiliary equipment of the power plant monitoring system is one of the focuses of the patrol personnel. The operating conditions of the unit can be judged by understanding the values ​​of various parameters in the auxiliary equipment screen, such as upper guide temperature, top cover water level, oil level in the pit leakage tank, etc.

[0003] Defects and shortcomings of the existing technology:

[0004] The existing monitoring system controls and alarms various data by setting high and low limits. For example, the oil pump will be started to pump the oil level to the low limit only when the oil level in the pit oil tank reaches the high limit. At the same time, the monitoring system will report the pit oil tank over-limit alarm. When the oil leakage in the unit oil system suddenly increases, the oil level in the pit oil tank rises rapidly in a short period of time, and the monitoring system alarm is delayed or even does not alarm. The monitoring personnel will not have time to react or the operation is delayed, and the unit may leak oil. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for dynamically analyzing the operating conditions of the unit using the monitoring system data. By utilizing the derivative analysis of the monitoring system data, the K value obtained after the derivative of the analog quantity is directly displayed in the auxiliary screen through the local LCU. At the same time, the high and low limits of the K value are set to connect to the voice alarm and alarm light of the monitoring system, so as to facilitate the screen monitoring personnel to dynamically analyze the operating conditions of the unit.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for dynamically analyzing unit operating conditions using monitoring system data, comprising the following steps:

[0008] S1. Determine the analysis object; the analysis object includes the upper guide temperature, the top cover water level and the oil level of the pit oil tank;

[0009] S2, collecting data of the analysis object and performing derivative processing on the data;

[0010] S3. Display the K value obtained after derivation directly on the auxiliary screen of the monitoring system so that the monitoring personnel can intuitively understand the change trend of the parameter; set the high and low limits of the K value and connect it to the voice alarm and alarm light of the monitoring system;

[0011] S4. Use the analytic hierarchy process to set up early warning and pre-control.

[0012] Preferably, in step S1, the analysis object is used to reflect the operating status of the unit. The excessively high upper guide temperature indicates that the unit is at risk of overheating; abnormal top cover water level may cause flooding of the plant; and abnormal oil level in the oil tank in the pit may cause an oil leakage accident.

[0013] Preferably, in step S2, the process of collecting data of the analysis object is:

[0014] S2.1. Determine the time interval Δt for data collection, and use the local LCU to receive analog data from the sensor. The analog data includes real-time values ​​of upper guide temperature, top cover water level, and oil level of the oil tank in the pit;

[0015] S2.2, perform derivative processing according to the formula K=ΔT / Δt, where T represents the analyzed parameter, and the analyzed parameters include upper guide temperature, top cover water level, and oil level of the oil tank in the machine pit;

[0016] For example, for the upper conduction temperature, T is the value of the upper conduction temperature; for the top cover water level, T is the height of the top cover water level.

[0017] S2.3. The parameter change rate K is obtained by calculating the parameter difference ΔT between two adjacent time points and dividing it by the time interval Δt. The change rate K is used to reflect the change trend of the parameter over time and to identify parameter fluctuations and abnormal conditions. When the change rate K increases or decreases abnormally, it indicates that the unit is operating abnormally.

[0018] Preferably, in step S3, the K value obtained after the derivation is directly displayed in the auxiliary screen of the monitoring system so that the monitoring personnel can intuitively understand the change trend of the parameter; the K value is set to a high and low limit, and connected to the voice alarm and alarm light of the monitoring system; the operation method is:

[0019] S3.1. Use digital display and chart display to make the change of K value more clearly visible;

[0020] S3.2. Set the upper and lower limits of the K value and connect it to the voice alarm and alarm light of the monitoring system; when the K value exceeds the upper limit or is lower than the lower limit, the monitoring system will sound an alarm to remind the monitor to take corresponding measures.

[0021] Preferably, the sub-steps of step S3.2 are:

[0022] S3.2.1. Perform historical data analysis: Assume an analysis parameter T (such as upper guide temperature) and collect data T1, T2, ..., T n ;

[0023] S3.2.2. Calculate statistical indicators:

[0024] Calculate the mean μ of the parameters:

[0025]

[0026] Where, μ: the average value of parameter T;

[0027] n: the number of data points;

[0028] T i : The value of the i-th data point;

[0029] Calculate the standard deviation σ of the parameter:

[0030]

[0031] S3.2.3 Determine the alarm threshold:

[0032] The lower threshold L and the upper threshold H are set according to the mean and standard deviation; μ-cσ is selected as the lower threshold and μ+cσ is selected as the upper threshold, where c is a constant; it can be adjusted according to actual conditions (for example, c=2 or c=3). The expression is:

[0033] L = μ - cσ;

[0034] H = μ + cσ;

[0035] S3.2.4, Alarm logic setting:

[0036] The parameter value T(t) is collected every time interval Δt;

[0037] T(t): parameter value collected at time t;

[0038] Δt: time interval for data collection;

[0039] Derivative processing:

[0040] Calculate the rate of change K of the parameter:

[0041]

[0042] K: rate of change of parameter T;

[0043] T(t-Δt): parameter value collected at time (t-Δt);

[0044] Alarm judgment:

[0045] Parameter value alarm: Check whether the parameter value T(t) exceeds the lower limit threshold L or the upper limit threshold H;

[0046] if T(t)<L or T(t)>H;

[0047] Change rate alarm: Check whether the parameter change rate K exceeds the set threshold Klow and K high ;

[0048] if K<K low or K>K high ;

[0049] K low : The lower threshold of the rate of change;

[0050] K high : The upper limit threshold of the rate of change;

[0051] When the parameter change rate K is lower than K low If K low Set to -0.5, which means that when the parameter change rate K is lower than -0.5, the system will trigger a low limit alarm.

[0052] To set the alarm level:

[0053] Low-level alarm: the parameter value is close to but does not exceed the threshold, or the rate of change is close to but does not exceed the threshold;

[0054] Set the low-level alarm thresholds to L+∈ and H-∈, where ∈ is a small positive number;

[0055] if L+∈<T(t)<L or H<T(t)<H-∈;

[0056] if K low +δ<K<K low or K high <K<K high -δ;

[0057] ∈: threshold increment for low-level alarm;

[0058] δ: the rate of change threshold increment for low-level alarms;

[0059] Intermediate alarm: The parameter value exceeds the lower threshold but does not exceed the upper threshold, or the rate of change exceeds the lower threshold but does not exceed the upper threshold;

[0060] if T(t)<L or T(t)>H;

[0061] if K<K low or K>K high ;

[0062] Advanced alarm: The parameter value seriously exceeds the threshold, or the rate of change seriously exceeds the threshold;

[0063] Set the advanced alarm thresholds to L-η and H+η, where η is a large positive number;

[0064] if T(t)<L-ηor T(t)>H+η;

[0065] if K<K low -γor K>K high +γ;

[0066] η: threshold increment for advanced alarms;

[0067] γ: rate of change threshold increment for advanced alarms;

[0068] The definitions of "larger" and "smaller" are: "larger" refers to the change of parameters relative to the normal range. A large positive number refers to a value that is significantly beyond the normal range. It is used to set the high-level alarm threshold, indicating that the parameter value is seriously deviated from the normal range and emergency measures need to be taken immediately. "smaller" refers to the change of parameters relative to the normal range. A small positive number refers to a value that is slightly beyond the normal range. It is used to set the low-level alarm threshold, indicating that the parameter value is close to but not seriously deviated from the normal range, and attention needs to be paid and preventive measures need to be taken.

[0069] S3.2.5, Alarm recording and tracking:

[0070] Record the time, parameter value, change rate, alarm level and processing results of each alarm;

[0071] Record(t,T(t),K,alarmlevel,action taken);

[0072] t: time when the alarm occurs

[0073] T(t): parameter value when the alarm occurs;

[0074] K: parameter change rate when the alarm occurs;

[0075] Record represents an operation or function used to record specific events, data, or status for subsequent analysis, auditing, or tracing;

[0076] alarm level: alarm level, including low, medium and high;

[0077] action taken: The measures taken.

[0078] Preferably, the sub-steps of step S4 are as follows:

[0079] S4.1. Establish a hierarchical model, including:

[0080] Target layer: early warning and pre-control of unit operating conditions;

[0081] Criteria layer: including upper guide temperature, top cover water level, and pit oil tank oil level parameters;

[0082] Solution layer: early warning and preventive control measures for different parameters; such as pouring water or pressurized water supply for the upper guide tile temperature.

[0083] S4.2, construct a judgment matrix, including:

[0084] For each parameter of the criterion layer, its relative importance is determined by pairwise comparison, and the judgment matrix A is constructed;

[0085] For example, for the comparison between the upper conduction temperature and the top cover water level, if the upper conduction temperature is considered to be relatively more important, the corresponding position in the judgment matrix is ​​assigned a value of 3, otherwise it is assigned a value of 1 / 3.

[0086] The general form of the judgment matrix is: A = [a], where a represents the relative importance of the i-th parameter and the j-th parameter;

[0087] S4.3, calculating the weight vector, including:

[0088] Calculate the maximum eigenvalue λ and the corresponding eigenvector W of the judgment matrix A;

[0089] After normalization, the feature vector W becomes the weight vector of each parameter;

[0090] S4.4. Consistency check, including:

[0091] Calculate the consistency index CI = (λ-n) / (n-1), where n is the order of the judgment matrix;

[0092] Find the average random consistency index RI, which is determined according to the order of the judgment matrix;

[0093] Calculate the consistency ratio CR = CI / RI. If CR < 0.1, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted.

[0094] S4.5. Early warning and pre-control decisions, including:

[0095] According to the weight vector and the K value of each parameter, the comprehensive early warning index is calculated.

[0096] For example, comprehensive early warning index = Σ(parameter weight × parameter K value).

[0097] When the comprehensive early warning indicators exceed the set threshold, the corresponding early warning and preventive control measures are initiated.

[0098] Preferably, after step S4 is completed, step S5 is performed: analyzing the efficiency of the pump;

[0099] The early warning and pre-control measures in step S4 serve as inputs to the pump efficiency analysis in step S5, forming a closed-loop feedback mechanism; the sub-steps of step 5 are as follows:

[0100] S5.1. Establish a hierarchical model, including:

[0101] Target layer: top cover pump efficiency analysis;

[0102] Criteria layer: including parameters such as K value mean, start and stop time, etc.

[0103] Solution level: different top cover pumps;

[0104] S5.2, construct judgment matrix: compare each parameter of the criterion layer in pairs, determine their relative importance, and construct judgment matrix A;

[0105] A=[a ij ] n×n ;

[0106] a ij : The relative importance of the i-th parameter and the j-th parameter;

[0107] For example, if the mean of the change rate K is considered to be more important than the start-stop time, the corresponding position in the judgment matrix can be assigned a value of 3, otherwise it can be assigned a value of 1 / 3.

[0108] S5.3. Calculate the weight vector:

[0109] Calculate the maximum eigenvalue λmax of the judgment matrix A and the corresponding eigenvector W;

[0110] After normalization, the feature vector W becomes the weight vector of each parameter;

[0111] S5.4, consistency check:

[0112] Calculate the consistency index CI:

[0113]

[0114] n: The order of the judgment matrix.

[0115] Find the average random consistency index RI: determined according to the order of the judgment matrix;

[0116] Calculate the consistency ratio CR:

[0117]

[0118] If CR<0.1, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted.

[0119] S5.5. Efficiency analysis and decision making

[0120] Calculate the comprehensive efficiency index: Calculate the comprehensive efficiency index based on the weight vector and the parameter values ​​of each pump;

[0121] Comprehensive efficiency index = ∑(parameter weight × parameter value);

[0122] For example, comprehensive efficiency index = ∑(w i ×K i ), where w i is the weight of the i-th parameter, K i is the value of the i-th parameter.

[0123] Evaluate pump performance: The higher the comprehensive efficiency index of a pump, the higher the efficiency and the better the performance.

[0124] A system for dynamically analyzing unit operating conditions using monitoring system data adopts the method for dynamically analyzing unit operating conditions using monitoring system data.

[0125] The present invention can achieve the following beneficial effects:

[0126] This method uses the derivative analysis of the monitoring system data, and directly displays the K value obtained after the analog value is derived on the auxiliary screen through the local LCU. At the same time, the K value is set to high and low limits to access the monitoring system voice alarm and alarm light, which is convenient for the monitor to dynamically analyze the unit working conditions. For example, when the oil level in the pit leaking oil tank rises rapidly within a unit time, the K value increases rapidly, and the higher the upper limit alarm, the monitor can display the dynamic increase of the K value in real time on the monitoring system screen, and react before the oil level reaches the upper limit to start the oil pump to pump the oil level of the pit leaking oil tank to the lower limit, so as to avoid the occurrence of oil leakage in the unit in time.

[0127] This method is simple and convenient to implement, with low modification cost. It only requires simple processing of the monitoring system data and access to the monitoring system screen and voice alarm, which is convenient for the monitoring personnel to observe and analyze and take preventive measures in time. It is also helpful for analyzing the efficiency of the pump. BRIEF DESCRIPTION OF THE DRAWINGS

[0128] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0129] Figure 1 This is a curve diagram of the upper guide tile temperature change after 10F is turned on in this embodiment;

[0130] Figure 2 This is the K value diagram after 10F is turned on in this embodiment;

[0131] Figure 3 This is a comparison of the K value diagrams of the three 10F top cover pumps in this embodiment. DETAILED DESCRIPTION

[0132] The preferred solution is Figures 1 to 3 As shown, a method for dynamically analyzing the unit operating conditions using monitoring system data comprises the following steps:

[0133] S1. Determine the analysis object; the analysis object includes the upper guide temperature, the top cover water level and the oil level of the pit oil tank;

[0134] The monitoring screen of auxiliary equipment in the power plant monitoring system contains many parameters, which reflect the operating status of the unit. Specific analysis objects include upper guide temperature, top cover water level, oil level of the machine pit leakage tank, etc.

[0135] S2, collecting data of the analysis object and performing derivative processing on the data;

[0136] S3. Display the K value obtained after derivation directly on the auxiliary screen of the monitoring system so that the monitoring personnel can intuitively understand the change trend of the parameter; set the high and low limits of the K value and connect it to the voice alarm and alarm light of the monitoring system;

[0137] S4. Use the analytic hierarchy process to set up early warning and pre-control.

[0138] Furthermore, in step S1, the analysis object is used to reflect the operating status of the unit. The excessively high upper guide temperature indicates that the unit is at risk of overheating; abnormal top cover water level may cause flooding of the plant; and abnormal oil level in the pit oil tank may cause an oil leakage accident.

[0139] Furthermore, in step S2, the process of collecting data of the analysis object is:

[0140] S2.1. Determine the time interval Δt for data collection, and use the local LCU to receive analog data from the sensor. The analog data includes real-time values ​​of upper guide temperature, top cover water level, and oil level of the oil tank in the pit;

[0141] S2.2, perform derivative processing according to the formula K=ΔT / Δt, where T represents the analyzed parameter, and the analyzed parameters include upper guide temperature, top cover water level, and oil level of the oil tank in the machine pit;

[0142] For example, for the upper conduction temperature, T is the value of the upper conduction temperature; for the top cover water level, T is the height of the top cover water level.

[0143] S2.3. The parameter change rate K is obtained by calculating the parameter difference ΔT between two adjacent time points and dividing it by the time interval Δt. The change rate K is used to reflect the change trend of the parameter over time and to identify parameter fluctuations and abnormal conditions. When the change rate K increases or decreases abnormally, it indicates that the unit is operating abnormally.

[0144] Further, in step S3, the K value obtained after the derivation is directly displayed in the auxiliary screen of the monitoring system so that the monitoring personnel can intuitively understand the change trend of the parameter; the K value is set to a high and low limit, and connected to the voice alarm and alarm light of the monitoring system; the operation method is:

[0145] S3.1. Use digital display and chart display to make the change of K value more clearly visible;

[0146] S3.2. Set the upper and lower limits of the K value and connect it to the voice alarm and alarm light of the monitoring system; when the K value exceeds the upper limit or is lower than the lower limit, the monitoring system will sound an alarm to remind the monitor to take corresponding measures.

[0147] Furthermore, the sub-steps of step S3.2 are:

[0148] S3.2.1. Perform historical data analysis: Assume an analysis parameter T (such as upper guide temperature) and collect data T1, T2, ..., T n ;

[0149] For example: collect parameter data over a period of time (such as a month or a quarter), including upper guide temperature, top cover water level, pit oil tank oil level, etc. For each parameter, record its value changes in different time periods.

[0150] Take the oil level of the oil tank in the pit as an example, and count the maximum, minimum, and average values ​​of the oil level in the pit during this period. Assume that the collected data shows that the oil level in the pit in the past month ranges from 100 liters to 500 liters, with an average value of 300 liters.

[0151] S3.2.2. Calculate statistical indicators:

[0152] Calculate the mean μ of the parameters:

[0153]

[0154] Where, μ: the average value of parameter T;

[0155] n: the number of data points;

[0156] T i : The value of the i-th data point;

[0157] Calculate the standard deviation σ of the parameter:

[0158]

[0159] For example: according to the derivative formula K=ΔT / Δt, the K value of each parameter in the historical data time period is calculated.

[0160] For the oil level of the leaking oil tank in the pit, assume that the change of the oil level in the past month is recorded as follows: in a certain period of time, the oil level rises from 200 liters to 300 liters, and the time interval is 1 hour. Then the K value at this time is (300-200) / 1=100 liters / hour. By calculating the K value of the oil level change in all time periods, the distribution of the K value of the oil level of the leaking oil tank in the pit is obtained.

[0161] According to the distribution of K values ​​in historical data, the normal range of K values ​​is determined. For example, through statistical analysis, it is found that the K value of the oil level of the oil tank in the machine pit is usually between 50 liters / hour and 150 liters / hour, so this range can be preliminarily determined as the normal range of the K value of the oil level of the oil tank in the machine pit.

[0162] S3.2.3 Determine the alarm threshold:

[0163] The lower threshold L and the upper threshold H are set according to the mean and standard deviation; μ-cσ is selected as the lower threshold and μ+cσ is selected as the upper threshold, where c is a constant; it can be adjusted according to actual conditions (for example, c=2 or c=3). The expression is:

[0164] L = μ - cσ;

[0165] H = μ + cσ;

[0166] S3.2.4, Alarm logic setting:

[0167] The parameter value T(t) is collected every time interval Δt;

[0168] T(t): parameter value collected at time t;

[0169] Δt: time interval for data collection;

[0170] Derivative processing:

[0171] Calculate the rate of change K of the parameter:

[0172]

[0173] K: rate of change of parameter T;

[0174] T(t-Δt): parameter value collected at time (t-Δt);

[0175] Alarm judgment:

[0176] Parameter value alarm: Check whether the parameter value T(t) exceeds the lower limit threshold L or the upper limit threshold H;

[0177] if T(t)<L or T(t)>H;

[0178] Change rate alarm: Check whether the parameter change rate K exceeds the set threshold K low and K high ;

[0179] if K<K low or K>K high ;

[0180] K low : The lower threshold of the rate of change;

[0181] K high : The upper limit threshold of the rate of change;

[0182] To set the alarm level:

[0183] Low-level alarm: the parameter value is close to but does not exceed the threshold, or the rate of change is close to but does not exceed the threshold;

[0184] Set the low-level alarm thresholds to L+∈ and H-∈, where ∈ is a small positive number;

[0185] if L+∈<T(t)<L or H<T(t)<H-∈;

[0186] if K low +δ<K<K low or K high <K<K high -δ;

[0187] ∈: threshold increment for low-level alarm;

[0188] δ: the rate of change threshold increment for low-level alarms;

[0189] Intermediate alarm: The parameter value exceeds the lower threshold but does not exceed the upper threshold, or the rate of change exceeds the lower threshold but does not exceed the upper threshold;

[0190] if T(t)<L or T(t)>H;

[0191] if K<K low or K>K high ;

[0192] Advanced alarm: The parameter value seriously exceeds the threshold, or the rate of change seriously exceeds the threshold;

[0193] Set the advanced alarm thresholds to L-η and H+η, where η is a large positive number;

[0194] if T(t)<L-ηor T(t)>H+η;

[0195] if K<K low -γor K>K high+γ;

[0196] η: threshold increment for advanced alarms;

[0197] γ: rate of change threshold increment for advanced alarms;

[0198] For example: based on the determined normal range of K value, set the upper and lower limits. Generally, the upper limit of the normal range can be increased by a certain percentage as the high limit, and the lower limit of the normal range can be decreased by a certain percentage as the low limit.

[0199] For the K value of the oil level in the pit leakage tank, if the normal range is 50 liters / hour to 150 liters / hour, the upper limit can be set to 120% of the upper limit of the normal range, that is, 150×1.2=180 liters / hour; the lower limit can be set to 80% of the lower limit of the normal range, that is, 50×0.8=40 liters / hour.

[0200] S3.2.5, Alarm recording and tracking:

[0201] Record the time, parameter value, change rate, alarm level and processing results of each alarm;

[0202] Record(t,T(t),K,alarmlevel,action taken);

[0203] t: time when the alarm occurs

[0204] T(t): parameter value when the alarm occurs;

[0205] K: parameter change rate when the alarm occurs;

[0206] alarm level: alarm level, including low, medium and high;

[0207] action taken: The measures taken.

[0208] For example: In the actual operation process, the operator provides feedback on the rationality of the alarm value based on experience. For example, if the operator finds that the K value of the oil level in the pit oil tank reaches 170 liters / hour under a certain situation, but there is actually no abnormal situation, this may indicate that the currently set high limit is too sensitive and the high limit can be adjusted appropriately. When an abnormal situation occurs, analyze whether the alarm value plays a role in timely warning. If in a certain oil spill accident, the K value of the oil level in the pit oil tank rises rapidly, but the alarm is not triggered until it exceeds the high limit for a period of time, this may indicate that the high limit is set too high and the high limit needs to be lowered to improve the timeliness of the warning. Evaluate and adjust the alarm value regularly (for example, quarterly or semi-annually). Based on the operation situation and actual experience over a period of time, combined with new historical data, re-determine the normal range of the K value and adjust the high and low limits accordingly. For example, if the K value of the oil level in the pit oil tank is found to be high overall in the past few months, it means that there may be some new situations that affect the change of the oil level. It is necessary to re-analyze the historical data and adjust the alarm value to adapt to the new working conditions.

[0209] Furthermore, the sub-steps of step S4 are as follows:

[0210] S4.1. Establish a hierarchical model, including:

[0211] Target layer: early warning and pre-control of unit operating conditions;

[0212] Criteria layer: including upper guide temperature, top cover water level, and pit oil tank oil level parameters;

[0213] Solution layer: early warning and preventive control measures for different parameters; such as pouring water or pressurized water supply for the upper guide tile temperature.

[0214] S4.2, construct a judgment matrix, including:

[0215] For each parameter of the criterion layer, its relative importance is determined by pairwise comparison, and the judgment matrix A is constructed;

[0216] For example, for the comparison between the upper conduction temperature and the top cover water level, if the upper conduction temperature is considered to be relatively more important, the corresponding position in the judgment matrix is ​​assigned a value of 3, otherwise it is assigned a value of 1 / 3.

[0217] The general form of the judgment matrix is: A = [a], where a represents the relative importance of the i-th parameter and the j-th parameter;

[0218] S4.3, calculating the weight vector, including:

[0219] Calculate the maximum eigenvalue λ and the corresponding eigenvector W of the judgment matrix A;

[0220] After normalization, the feature vector W becomes the weight vector of each parameter;

[0221] S4.4. Consistency check, including:

[0222] Calculate the consistency index CI = (λ-n) / (n-1), where n is the order of the judgment matrix;

[0223] Find the average random consistency index RI, which is determined according to the order of the judgment matrix;

[0224] Calculate the consistency ratio CR = CI / RI. If CR < 0.1, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted.

[0225] S4.5. Early warning and pre-control decisions, including:

[0226] According to the weight vector and the K value of each parameter, the comprehensive early warning index is calculated.

[0227] For example, comprehensive early warning index = Σ(parameter weight × parameter K value).

[0228] When the comprehensive early warning indicators exceed the set threshold, the corresponding early warning and preventive control measures are initiated.

[0229] Further, after step S4 is completed, step S5 is performed: analyzing the efficiency of the pump;

[0230] The early warning and pre-control measures in step S4 serve as inputs to the pump efficiency analysis in step S5, forming a closed-loop feedback mechanism; the sub-steps of step 5 are as follows:

[0231] S5.1. Establish a hierarchical model, including:

[0232] Target layer: top cover pump efficiency analysis;

[0233] Criteria layer: including parameters such as K value mean, start and stop time, etc.

[0234] Solution level: different top cover pumps;

[0235] S5.2, construct judgment matrix: compare each parameter of the criterion layer in pairs, determine their relative importance, and construct judgment matrix A;

[0236] A=[a ij ] n×n ;

[0237] a ij : The relative importance of the i-th parameter and the j-th parameter;

[0238] For example, if the mean of the change rate K is considered to be more important than the start-stop time, the corresponding position in the judgment matrix can be assigned a value of 3, otherwise it can be assigned a value of 1 / 3.

[0239] S5.3. Calculate the weight vector:

[0240] Calculate the maximum eigenvalue λmax of the judgment matrix A and the corresponding eigenvector W;

[0241] After normalization, the feature vector W becomes the weight vector of each parameter;

[0242] S5.4, consistency check:

[0243] Calculate the consistency index CI:

[0244]

[0245] n: The order of the judgment matrix.

[0246] Find the average random consistency index RI: determined according to the order of the judgment matrix;

[0247] Calculate the consistency ratio CR:

[0248]

[0249] If CR<0.1, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted.

[0250] S5.5. Efficiency analysis and decision making

[0251] Calculate the comprehensive efficiency index: Calculate the comprehensive efficiency index based on the weight vector and the parameter values ​​of each pump;

[0252] Comprehensive efficiency index = ∑(parameter weight × parameter value);

[0253] For example, comprehensive efficiency index = ∑(w i ×K i ), where w i is the weight of the i-th parameter, K i is the value of the i-th parameter.

[0254] Evaluate pump performance: The higher the comprehensive efficiency index of a pump, the higher the efficiency and the better the performance.

[0255] In this embodiment, both step 4 and step 5 rely on the same real-time data source to ensure the consistency and accuracy of the data. The early warning and pre-control measures in step 4 provide a stable operating environment for the pump efficiency analysis in step 5. By analyzing the efficiency of the pump, the pump with lower efficiency can be found and measures can be taken to improve its efficiency, thereby reducing energy consumption and reducing operating costs. An efficient pump has a more stable operating state, which helps to extend the service life of the pump and reduce the frequency of maintenance and replacement. By monitoring the changes in the efficiency of the pump, potential faults such as wear and blockage can be discovered in a timely manner, and measures can be taken in advance to avoid the expansion of faults.

[0256] A system for dynamically analyzing unit operating conditions using monitoring system data adopts the method for dynamically analyzing unit operating conditions using monitoring system data.

[0257] Embodiment 1:

[0258] In this embodiment, taking the upper conductor temperature warning of a power plant as an example, the analog quantity received by the local LCU is simply programmed, that is, it is derivation processed and then uploaded to the monitoring system and voice alarm. Taking the upper conductor temperature in the auxiliary screen of the 10F monitoring system as an example, a brief description is given.

[0259] (1) Function 1 of this method: Early warning and preventive measures

[0260] like Figure 1 As shown, Figure 1 The change of the upper guide tile temperature after 10F is turned on. The change of the upper guide tile temperature per unit time is set to K, that is, K = ΔT / Δt, the upper limit of K is set to 2, and the lower limit is set to 0. The upper guide tile temperature after turning on can be divided into three stages:

[0261] The first stage: before starting up, the upper guide bearing temperature is constant;

[0262] Stage 2: The temperature of the upper guide tile rises rapidly. Figure 1 and Figure 2 From the K value diagram after the 10F power-on, it can be seen that the upper guide tile temperature rises rapidly to a maximum value of 46.1°C within 1-6 hours, of which the K value increases rapidly to a high limit of 2.88 within 1-3 hours, and gradually decreases to 0 within 3-6 hours. At this time, the screen monitoring personnel immediately pour water or pressurize water supply;

[0263] The third stage: The upper guide tile temperature gradually decreases and reaches 40℃ during normal operation of 10F. Figure 1 and Figure 2 It can be seen that after pouring water or pressurized water supply, the temperature of the upper guide tile gradually decreases within 6-9 hours, the absolute value of K increases from 0 to 1.07, and then gradually approaches 0.

[0264] From the analysis of these three stages, we can see that according to the traditional method, after the screen monitoring personnel observe the temperature rise of the upper guide tile after the 10F is turned on, they need to call up the historical curve to observe whether the upper guide tile temperature exceeds the historical normal operating temperature by about 5°C. Figure 1 It can be seen that it takes at least 5 hours after startup to determine whether manual water supply or pressurized water supply operations are needed, which makes the preventive control measures have a certain lag.

[0265] By using the monitoring system data to dynamically analyze the unit operating conditions, the monitoring personnel can analyze the state of the upper bearing temperature by combining the 10F upper bearing temperature and K value in the auxiliary screen of the monitoring system. When K=2.88, T=35℃, the higher the upper limit of the K value reported by the monitoring system, the faster the upper bearing temperature rises. At this time, the monitoring personnel should pay attention and prepare for technical water supply or pressurized water supply in advance.

[0266] The same method can also be used for the top cover water level or the pit oil leakage tank oil level. When the K value of the top cover water level or the pit oil leakage tank oil level rises rapidly, it means that the amount of water leakage or oil leakage has increased. The operating personnel can respond according to the real-time water level or oil level and the K value, and start the top cover pump or the pit oil leakage pump in time to avoid several pumps failing to pump enough water, resulting in flooding of the factory or oil leakage accidents.

[0267] (2) Function 2 of this method: providing a basis for analyzing pump efficiency.

[0268] The traditional method of analyzing the efficiency of the top cover pump is to observe the time it takes for the top cover pump to pump the top cover water level from the upper limit to the lower limit. The method in this article is used to analyze the K value of each top cover pump to determine the pumping efficiency of the top cover pump, that is, the more water the top cover pump pumps per unit time, the higher the efficiency. Figure 3 This is the K value diagram of the three top cover pumps of 10F pumping the top cover water level from the upper limit to the lower limit within a certain period of time. Figure 4 is the location of the K value data on the monitoring system screen.

[0269] observe Figure 3 It can be seen that due to the instability of the K value within a few minutes of starting and stopping the top cover pump, the oscillation range at both ends is not considered, so the smooth area in the middle is taken for analysis. It can be concluded that the No. 1 top cover pump has the highest average K value, followed by the No. 2 top cover pump, and the No. 3 top cover pump has the lowest average K value. Combined with the start and stop time of the pump, it is not difficult to see that the No. 1 top cover pump has the highest efficiency, followed by the No. 2 top cover pump, and the No. 3 top cover pump has the lowest efficiency. The method in this article provides a basis for analyzing the efficiency of the pump, and directly calls out the K value historical curve for analysis, which is more efficient and convenient than the traditional method.

[0270] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for dynamically analyzing unit operating conditions using monitoring system data, characterized in that The following steps are involved: S1. Determine the analysis object; the analysis object includes the upper guide temperature, the top cover water level and the oil level of the pit oil tank; S2, collecting data of the analysis object and performing derivative processing on the data; S3. Display the K value obtained after derivation directly on the auxiliary screen of the monitoring system so that the monitoring personnel can intuitively understand the change trend of the parameter; set the high and low limits of the K value and connect it to the voice alarm and alarm light of the monitoring system; S4. Use the analytic hierarchy process to set up early warning and pre-control.

2. A method for dynamically analyzing unit operating conditions using monitoring system data according to claim 1, characterized in that: In step S1, the analysis object is used to reflect the operating status of the unit. The excessively high upper guide temperature indicates that the unit is at risk of overheating; abnormal top cover water level may cause flooding of the plant; and abnormal oil level in the pit oil tank may cause an oil leakage accident.

3. The method for dynamically analyzing unit operating conditions using monitoring system data according to claim 1, characterized in that: In step S2, the process of collecting data of the analysis object is as follows: S2.

1. Determine the time interval Δt for data collection, and use the local LCU to receive analog data from the sensor. The analog data includes real-time values ​​of upper guide temperature, top cover water level, and oil level of the oil tank in the pit; S2.2, perform derivative processing according to the formula K=ΔT / Δt, where T represents the analyzed parameter, and the analyzed parameters include the upper guide temperature, the top cover water level, and the oil level of the oil tank in the machine pit; S2.

3. The parameter change rate K is obtained by calculating the parameter difference ΔT between two adjacent time points and dividing it by the time interval Δt. The change rate K is used to reflect the change trend of the parameter over time and to identify parameter fluctuations and abnormal conditions. When the change rate K increases or decreases abnormally, it indicates that the unit is operating abnormally.

4. The method for dynamically analyzing unit operating conditions using monitoring system data according to claim 3, characterized in that: In step S3, the K value obtained after the derivation is directly displayed in the auxiliary screen of the monitoring system so that the monitoring personnel can intuitively understand the change trend of the parameter; the K value is set to a high and low limit, and connected to the voice alarm and alarm light of the monitoring system; the operation method is: S3.

1. Use digital display and chart display to make the change of K value more clearly visible; S3.

2. Set the upper and lower limits of the K value and connect it to the voice alarm and alarm light of the monitoring system; when the K value exceeds the upper limit or is lower than the lower limit, the monitoring system will sound an alarm to remind the monitor to take corresponding measures.

5. A method for dynamically analyzing unit operating conditions using monitoring system data according to claim 4, characterized in that: The sub-steps of step S3.2 are: S3.2.

1. Perform historical data analysis: Assume an analysis parameter T and collect data T1, T2, …, T over a period of time. n ; S3.2.

2. Calculate statistical indicators: Calculate the mean μ of the parameters: Where, μ: the average value of parameter T; n: the number of data points; T i : The value of the i-th data point; Calculate the standard deviation σ of the parameter: S3.2.3 Determine the alarm threshold: The lower threshold L and the upper threshold H are set according to the mean and standard deviation; μ-cσ is selected as the lower threshold and μ+cσ is selected as the upper threshold, where c is a constant; the expression is: L = μ - cσ; H = μ + cσ; S3.2.4, Alarm logic setting: The parameter value T(t) is collected every time interval Δt; T(t): parameter value collected at time t; Δt: time interval for data collection; Derivative processing: Calculate the rate of change K of the parameter: K: rate of change of parameter T; T(t-Δt): parameter value collected at time (t-Δt); Alarm judgment: Parameter value alarm: Check whether the parameter value T(t) exceeds the lower limit threshold L or the upper limit threshold H; if T(t)<L or T(t)>H; Change rate alarm: Check whether the parameter change rate K exceeds the set threshold K low and K high ; if K<K low or K>K high ; K low : The lower threshold of the rate of change; K high : The upper limit threshold of the rate of change; To set the alarm level: Low-level alarm: the parameter value is close to but does not exceed the threshold, or the rate of change is close to but does not exceed the threshold; Set the low-level alarm thresholds to L+∈ and H-∈, where ∈ is a small positive number; if L+∈<T(t)<L or H<T(t)<H-∈; if K low +δ<K<K low or K high <K<K high -δ; ∈: threshold increment for low-level alarm; δ: the rate of change threshold increment for low-level alarms; Intermediate alarm: The parameter value exceeds the lower threshold but does not exceed the upper threshold, or the rate of change exceeds the lower threshold but does not exceed the upper threshold; if T(t)<L or T(t)>H; if K<K low or K>K high ; Advanced alarm: The parameter value seriously exceeds the threshold, or the rate of change seriously exceeds the threshold; Set the advanced alarm thresholds to L-η and H+η, where η is a large positive number; if T(t)<L-ηor T(t)>H+η; if K<K low -γor K>K high +γ; η: threshold increment for advanced alarms; γ: rate of change threshold increment for advanced alarms; S3.2.5, Alarm recording and tracking: Record the time, parameter value, change rate, alarm level and processing results of each alarm; Record(t,T(t),K,alarm level,action taken); t: time when the alarm occurs T(t): parameter value when the alarm occurs; K: parameter change rate when the alarm occurs; Record represents an operation or function used to record specific events, data, or status for subsequent analysis, auditing, or tracing; alarm level: alarm level, including low, medium and high; action taken: The measures taken.

6. The method for dynamically analyzing unit operating conditions using monitoring system data according to claim 1, characterized in that: The sub-steps of step S4 are as follows: S4.

1. Establish a hierarchical model, including: Target layer: early warning and pre-control of unit operating conditions; Criteria layer: including upper guide temperature, top cover water level, and pit oil tank oil level parameters; Solution level: early warning and preventive control measures for different parameters; S4.2, construct a judgment matrix, including: For each parameter of the criterion layer, its relative importance is determined by pairwise comparison, and the judgment matrix A is constructed; The general form of the judgment matrix is: A = [a], where a represents the relative importance of the i-th parameter and the j-th parameter; S4.3, calculating the weight vector, including: Calculate the maximum eigenvalue λ and the corresponding eigenvector W of the judgment matrix A; After normalization, the feature vector W becomes the weight vector of each parameter; S4.

4. Consistency check, including: Calculate the consistency index CI = (λ-n) / (n-1), where n is the order of the judgment matrix; Find the average random consistency index RI, which is determined according to the order of the judgment matrix; Calculate the consistency ratio CR = CI / RI. If CR < 0.1, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted. S4.

5. Early warning and pre-control decisions, including: According to the weight vector and the K value of each parameter, the comprehensive early warning index is calculated; When the comprehensive early warning indicators exceed the set threshold, the corresponding early warning and preventive control measures are initiated.

7. The method for dynamically analyzing unit operating conditions using monitoring system data according to claim 6, characterized in that: After step S4 is completed, step S5 is performed: analyzing the efficiency of the pump; The early warning and pre-control measures in step S4 serve as inputs to the pump efficiency analysis in step S5, forming a closed-loop feedback mechanism; the sub-steps of step 5 are as follows: S5.

1. Establish a hierarchical model, including: Target layer: top cover pump efficiency analysis; Criteria layer: including K value mean and start and stop time parameters; Solution level: different top cover pumps; S5.2, construct judgment matrix: compare each parameter of the criterion layer in pairs, determine their relative importance, and construct judgment matrix A; A=[a ij ] n×n ; a ij : The relative importance of the i-th parameter and the j-th parameter; S5.

3. Calculate the weight vector: Calculate the maximum eigenvalue λmax of the judgment matrix A and the corresponding eigenvector W; After normalization, the feature vector W becomes the weight vector of each parameter; S5.4, consistency check: Calculate the consistency index CI: n: the order of the judgment matrix; Find the average random consistency index RI: determined according to the order of the judgment matrix; Calculate the consistency ratio CR: If CR<0.1, the judgment matrix has satisfactory consistency, otherwise the judgment matrix needs to be readjusted; S5.5, efficiency analysis and decision making; Calculate the comprehensive efficiency index: Calculate the comprehensive efficiency index based on the weight vector and the parameter values ​​of each pump; Comprehensive efficiency index = ∑(parameter weight × parameter value); Evaluate pump performance: The higher the comprehensive efficiency index of a pump, the higher the efficiency and the better the performance.

8. A system for dynamically analyzing unit operating conditions using monitoring system data, characterized in that: A method for dynamically analyzing unit operating conditions using monitoring system data according to any one of claims 1 to 7 is adopted.

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