Intelligent power plant equipment management system

Abstract

The invention relates to the technical field of power plant equipment management, and particularly discloses an intelligent power plant equipment management system, and the system specifically comprises an equipment data collection and judgment module which collects the real-time voltage of equipment in a power plant, judges whether the voltage of the equipment is abnormal or not, and generates a voltage abnormal signal; the abnormal data calculation and processing module is used for acquiring a corresponding time unit based on the generated voltage abnormal signal, performing data processing and calculating an abnormal time characterization value; and the abnormity judgment module is used for comparing the abnormal time characterization value with an abnormal time characterization threshold value and judging the severity of the voltage abnormity in the abnormal time unit. Therefore, by monitoring the voltage of the equipment and performing abnormity judgment, abnormity of the equipment can be known and found in time, and further analysis is performed based on the abnormal time, so that the severity of a fault of the equipment can be assessed, and which level of countermeasures can be taken can be determined in an assisted manner; and the maintenance strategy can be optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant equipment management, and in particular to an intelligent power plant equipment management system. Background Art

[0002] A power plant is a power plant that converts some form of raw energy into electrical energy for fixed facilities or transportation, such as thermal, hydroelectric, steam, diesel or nuclear power plants. Power plant equipment is an indispensable and important part of the power production process. They are responsible for converting various energy sources into electrical energy and ensuring the stable transmission and distribution of electrical energy.

[0003] However, power plant equipment management mainly relies on manual inspections and regular maintenance. This model has many limitations, such as fixed inspection cycles, difficulty in timely detection of early equipment failures, lack of targeted maintenance strategies, resulting in waste of resources and low efficiency, and delayed detection of equipment anomalies, which affects the stability and reliability of power supply. At the same time, the maintenance management efficiency of abnormal equipment is low.

[0004] In view of this, we propose an intelligent power plant equipment management system. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent power plant equipment management system to solve the technical problems in the above background.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] The present invention provides an intelligent power plant equipment management system, which specifically includes: an equipment data acquisition and judgment module: collecting the real-time voltage of the equipment in the power plant, judging whether the equipment voltage is abnormal, and generating a voltage abnormality signal; an abnormal data calculation and processing module: based on the generated voltage abnormality signal, obtaining the corresponding time unit, performing data processing, and calculating the abnormal time characterization value; an abnormal judgment module: comparing the abnormal time characterization value with the abnormal time characterization threshold, judging the severity of the voltage abnormality in the abnormal time unit, and generating a high abnormality signal; an equipment abnormality frequency analysis module: based on the generated high abnormality signal, analyzing the frequency of abnormal voltage equipment that generates a high abnormality signal, and generating a frequent signal; an equipment abnormality regularity analysis module: based on the generated frequent signal, whether the equipment to be analyzed generates a high abnormality signal with regularity, and generating a re-analysis signal or a regular signal; an equipment abnormality periodicity re-analysis module: based on the generated re-analysis signal, re-analyzing the periodicity of the equipment to be analyzed.

[0008] As a further solution of the present invention: the process of generating the voltage abnormality signal is:

[0009] Obtain the real-time voltage of power plant equipment during operation;

[0010] Divide the running time into several time units, extract the maximum and minimum voltage values ​​of each time unit, and compare the maximum and minimum voltage values ​​with the voltage threshold range respectively;

[0011] If the maximum voltage is not within the voltage threshold range or the minimum voltage is not within the voltage threshold range, a voltage abnormality signal is generated;

[0012] It is marked as an abnormal voltage device based on the generated voltage abnormality signal.

[0013] As a further solution of the present invention: the process of obtaining the abnormal time characterization value is:

[0014] Obtain the abnormal time proportion, the total value of the time interval deviation ratio, and the extreme value deviation ratio of the abnormal time period;

[0015] Substitute into the formula The abnormal time characterization value SY is calculated, where CX represents the abnormal time proportion, JG represents the total value of the time interval deviation ratio, ZF represents the extreme value deviation ratio of the abnormal time period, and a1, a2, and a3 are preset proportional coefficients.

[0016] As a further solution of the present invention: the process of obtaining the abnormal time ratio value is as follows:

[0017] Obtain a time unit corresponding to a generated voltage abnormality signal, mark the time unit as an abnormal time unit, and obtain a real-time voltage corresponding to the abnormal time unit;

[0018] A two-dimensional model is established with time as the X-axis and voltage as the Y-axis. The real-time voltage is substituted into the two-dimensional model to draw a real-time voltage curve. The maximum value and the minimum value of the voltage threshold range are used as standard values ​​to draw standard lines parallel to the X-axis, which are marked as the first standard line and the second standard line respectively.

[0019] Marking the area between the first standard line and the second standard line as a normal voltage area, and marking the area above the first standard line and below the second standard line as an abnormal voltage area;

[0020] Obtain the time period corresponding to the voltage abnormal area, mark it as the abnormal time period, sum up all the time values ​​of the abnormal time period to obtain the total abnormal time value of the abnormal time period, perform ratio processing on the total abnormal time value and the total time value of the abnormal time unit to obtain the abnormal time ratio value;

[0021] As a further solution of the present invention: the process of obtaining the total value of the time interval deviation ratio and the extreme value deviation ratio of the abnormal time period is as follows:

[0022] Obtain the time interval values ​​corresponding to adjacent abnormal areas, sum and average all the time interval values, perform difference calculations on all the time interval values ​​and the time interval average, take the absolute value of the difference to obtain the time interval deviation value, perform ratio calculation on the time interval deviation value to obtain the time interval deviation ratio, sum all the time interval deviation ratios to obtain the total value of the time interval deviation ratio;

[0023] Extract the maximum and minimum values ​​of all time values ​​in abnormal time periods, calculate the difference between the maximum and minimum time values ​​in the abnormal time periods to obtain the extreme value difference of the time values ​​in the abnormal time periods, calculate the ratio of the extreme value difference of the time values ​​in the abnormal time periods to the minimum time values ​​in the abnormal time periods to obtain the extreme value deviation ratio of the abnormal time periods.

[0024] As a further solution of the present invention: the acquisition process of generating a high abnormality signal is:

[0025] Obtaining an abnormal time representation value, and comparing the abnormal time representation with an abnormal time representation threshold;

[0026] If the abnormal time representation is greater than the abnormal time representation threshold, a high abnormality signal is generated.

[0027] As a further solution of the present invention: the process of analyzing the frequency of abnormal voltage devices generating high abnormality signals is as follows:

[0028] Acquire abnormal voltage devices corresponding to signals with high abnormality, and mark these devices as devices to be analyzed;

[0029] Extract the historical period before the abnormal time unit, and obtain the numerical value of the high abnormality signal generated by the device to be analyzed in the historical period;

[0030] Obtain the number of time units in the historical period, perform ratio processing on the number of generated high abnormality signal values ​​and the number of time units in the historical period, and obtain the ratio of the number of abnormality signals;

[0031] Compare the value of the proportion of the number of abnormal degree signals with the threshold value of the proportion of the number of abnormal degree signals;

[0032] If the percentage of abnormal degree signals is greater than the percentage threshold of abnormal degree signals, a frequent signal is generated.

[0033] As a further solution of the present invention: the process of generating the reanalysis signal or the regular signal is:

[0034] Obtain the percentage of small deviations, and compare the percentage of small deviations with the threshold of the percentage of small deviations;

[0035] If the percentage of small deviations is less than or equal to the percentage threshold of small deviations, a reanalysis signal is generated;

[0036] If the proportion of small deviations is greater than the threshold of small deviation proportion, a regular signal is generated.

[0037] As a further solution of the present invention: the process of obtaining the percentage of small number of deviations is:

[0038] Based on the generated frequent signals, the time units corresponding to the signals with high abnormality generated by the device to be analyzed in the historical period are obtained and marked as the time units to be analyzed;

[0039] The time values ​​of adjacent time units to be analyzed are calculated to obtain the time unit deviation value, all the time unit deviation values ​​are summed and averaged to obtain the time unit deviation mean, all the time unit deviation values ​​are calculated to be different from the time unit deviation mean, the difference is taken as the absolute value, and the time unit deviation degree value is obtained;

[0040] comparing the time bias unit deviation degree value with the time bias unit deviation degree threshold;

[0041] If the time deviation unit deviation degree value is less than or equal to the time deviation unit deviation degree threshold, a smaller deviation signal is generated;

[0042] If the time deviation unit deviation degree value is greater than the time deviation unit deviation degree threshold, a large deviation signal is generated;

[0043] Obtain the number of signals with smaller deviations and the number of signals with larger deviations, sum the number of signals with smaller deviations and the number of signals with larger deviations to obtain the total number, calculate the ratio of the number of signals with smaller deviations to the total number, and obtain the proportion of signals with smaller deviations.

[0044] As a further solution of the present invention: the process of periodically reanalyzing the device to be analyzed is:

[0045] Obtain the time units to be analyzed corresponding to the signals with high abnormality level generated by the first-class batches and the second-class batches, and respectively obtain the percentage of small deviations corresponding to the first-class batches and the percentage of small deviations corresponding to the second-class batches;

[0046] Compare the small deviation ratio with the small deviation ratio threshold,

[0047] If the percentage of small deviations is less than or equal to the percentage threshold of small deviations, a non-periodic signal is generated;

[0048] If the proportion of small deviations is greater than the threshold of small deviation proportion, a periodic signal is generated.

[0049] As a further solution of the present invention: the process of obtaining the first batch and the second batch is as follows:

[0050] Based on the generated reanalysis signal, obtain the abnormal time characterization value corresponding to the device to be analyzed when generating a high abnormality degree signal, extract the maximum value of the abnormal time characterization value, and mark the maximum value of the abnormal time characterization value as the first division degree standard value;

[0051] The maximum value of the abnormal time characterization value and the abnormal characterization threshold are summed and averaged, and the obtained average is marked as the second division degree standard value;

[0052] Marking the abnormal time characterization threshold as the third division level standard value;

[0053] Compare the abnormal time characterization value with the first division degree standard value, the second division degree standard value, and the third division degree standard value respectively;

[0054] If the third classification standard value is less than the abnormal time characterization value and less than or equal to the second classification degree standard value, it is marked as a class I batch;

[0055] If the second classification standard value is less than the abnormal time characterization value and is less than or equal to the second classification degree standard value, it will be marked as a Class II batch.

[0056] Beneficial effects of the present invention:

[0057] (1) The present invention first collects the real-time voltage of the equipment and makes an abnormal judgment on the real-time voltage of the equipment. When a voltage abnormality occurs, the time unit where the voltage abnormality occurs is separated, and the abnormal time ratio, the total value of the time interval deviation ratio and the extreme deviation ratio of the abnormal time period are calculated. Data processing is performed based on the calculated abnormal time ratio, the total value of the time interval deviation ratio and the extreme deviation ratio of the abnormal time period to calculate the abnormal time characterization value, and the abnormal time characterization value is compared with the abnormal time characterization threshold. When the abnormal time characterization value is less than or equal to the abnormal time characterization threshold, it means that the voltage abnormality of the corresponding time unit equipment is low. When the abnormal time characterization value is greater than the abnormal time characterization threshold, it means that the voltage abnormality of the corresponding time unit equipment is high. Therefore, the voltage of the equipment can be monitored and an abnormal judgment can be made, so as to timely understand and discover the abnormality of the equipment. Moreover, based on further analysis of the abnormal time, the severity of the equipment failure can be evaluated, and the level of countermeasures to be taken can be assisted in deciding, so as to optimize the maintenance strategy.

[0058] (2) The present invention analyzes the number of times the device to be analyzed has a high degree of abnormality in the historical period. If the number of times is large, it means that the device is a device with frequent failures and needs to be given priority attention, which helps to optimize the allocation of maintenance resources and improve maintenance efficiency;

[0059] (3) The present invention performs regularity analysis on the equipment to be analyzed that occurs frequently and has a high degree of abnormality. The time unit deviation values ​​of adjacent time units corresponding to the high degree of abnormality are obtained by summing and averaging to obtain the mean value of the time unit deviation. The time unit deviation degree value is calculated based on the time unit deviation value and the time unit deviation mean. After comparison, if the time unit deviation degree value is less than or equal to the time unit deviation degree threshold, a signal with a smaller deviation is generated, and the proportion of the number of small deviations is obtained. The proportion of the number of small deviations is then compared with the proportion of the number of small deviations to generate a re-analysis signal or a regular signal, so that the periodic interval value of the signal with a high degree of abnormality can be obtained based on the generated regular signal, and the equipment failure can be protected in advance based on the periodic interval value, thereby reducing the possibility of equipment abnormality. It can provide power plants with more comprehensive and accurate equipment maintenance management, help improve the operating efficiency and safety of power plants, and ensure the stability and reliability of power supply.

[0060] (4) When generating a reanalysis signal, the present invention divides the abnormal time characterization values ​​corresponding to the generated high abnormality signals into batches, and then determines whether the time when the high abnormality signals appear in different batches is periodic. Therefore, after the abnormal characterization values ​​are divided into batches according to the size of the values, the periodicity of the high abnormality signals can be analyzed more accurately, and then the abnormal equipment can be managed and maintained in advance more accurately. Based on the analysis results, more scientific and reasonable maintenance plans and management strategies can be formulated to improve the overall operating efficiency, enhance the operation and management capabilities of the power plant, and promote intelligent development. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The present invention will be further described below in conjunction with the accompanying drawings.

[0062] Figure 1 is a flowchart of Embodiment 1 of the present invention;

[0063] Figure 2 is a flowchart of Embodiment 2 and Embodiment 3 of the present invention;

[0064] Figure 3 It is a flowchart of obtaining the abnormal time characterization value in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Embodiment 1:

[0067] See also Figure 1 and Figure 3 As shown, an intelligent power plant equipment management system according to an embodiment of the present invention specifically includes:

[0068] Equipment data collection and judgment module: collects the real-time voltage of equipment in the power plant and determines whether the equipment voltage is abnormal;

[0069] In some embodiments, real-time voltage of power plant equipment during operation is obtained;

[0070] Divide the running time into several time units, extract the maximum and minimum voltage values ​​of each time unit, and compare the maximum and minimum voltage values ​​with the voltage threshold range respectively;

[0071] Among them, the voltage threshold range is summarized by the staff in this field based on historical experimental data;

[0072] If the maximum voltage is within the voltage threshold range and the minimum voltage is within the voltage threshold range, it means that all voltages in the time unit are within the voltage threshold range, and a voltage normal signal is generated;

[0073] If the maximum voltage is not within the voltage threshold range or the minimum voltage is not within the voltage threshold range, it means that the voltage of the time unit is not within the voltage threshold range, and a voltage abnormality signal is generated;

[0074] marking it as an abnormal voltage device based on the generated voltage abnormality signal;

[0075] When a voltage anomaly signal is generated, the voltage anomaly time needs to be further analyzed;

[0076] Abnormal data calculation and processing module: based on the generated voltage abnormality signal, obtain the corresponding time unit, perform data processing, and calculate the abnormal time representation value;

[0077] In some embodiments, a time unit corresponding to the generation of a voltage abnormality signal is obtained, the time unit is marked as an abnormal time unit, and a real-time voltage corresponding to the abnormal time unit is obtained;

[0078] A two-dimensional model is established with time as the X-axis and voltage as the Y-axis. The real-time voltage is substituted into the two-dimensional model to draw a real-time voltage curve. The maximum value and the minimum value of the voltage threshold range are used as standard values ​​to draw standard lines parallel to the X-axis, which are marked as the first standard line and the second standard line respectively.

[0079] Marking the area between the first standard line and the second standard line as a normal voltage area, and marking the area above the first standard line and below the second standard line as an abnormal voltage area;

[0080] Obtain the time period corresponding to the voltage abnormal area, mark it as the abnormal time period, sum up all the time values ​​of the abnormal time period to obtain the total abnormal time value of the abnormal time period, perform ratio processing on the total abnormal time value and the total time value of the abnormal time unit to obtain the abnormal time ratio value;

[0081] It should be explained that the meaning of the abnormal time ratio is: the abnormal time ratio is calculated by the abnormal time period, wherein the more abnormal time periods there are and the larger the abnormal time period value is, the larger the abnormal time ratio is, indicating that the voltage abnormality in the abnormal time unit occurs more frequently or lasts longer;

[0082] Obtain the time interval values ​​corresponding to adjacent abnormal areas, sum and average all the time interval values, perform difference calculations on all the time interval values ​​and the time interval average, take the absolute value of the difference to obtain the time interval deviation value, perform ratio calculation on the time interval deviation value to obtain the time interval deviation ratio, sum all the time interval deviation ratios to obtain the total value of the time interval deviation ratio;

[0083] It should be explained that the total value of the time interval deviation ratio reflects the following meaning: the total value of the time interval deviation ratio is calculated by the time interval deviation ratio, wherein the larger the time interval deviation ratio is, the larger the deviation between the time interval value of two adjacent abnormal time periods and the time interval mean is, that is, the time when the voltage abnormality occurs in the time unit has a large volatility, and the occurrence of voltage abnormality in the abnormal time unit has a large instability;

[0084] Extract the maximum and minimum values ​​of all abnormal time period time values, calculate the difference between the maximum and minimum values ​​of the abnormal time period time values ​​to obtain the extreme value difference of the abnormal time period time values, calculate the ratio of the extreme value difference of the abnormal time period time values ​​to the minimum value of the abnormal time period time values ​​to obtain the extreme value deviation ratio of the abnormal time period;

[0085] It should be explained that the meaning of the extreme value deviation ratio of the abnormal time period is as follows: the extreme value deviation ratio of the abnormal time period is calculated by the maximum and minimum values ​​in the abnormal time period, which indicates the volatility of the duration of the voltage anomaly. The larger the deviation between the maximum and minimum values, the greater the difference in the duration of the abnormal time period, indicating that the voltage anomaly of the abnormal time unit has a greater volatility.

[0086] Substitute into the formula The abnormal time characterization value SY is calculated, where CX represents the abnormal time proportion, JG represents the total value of the time interval deviation ratio, ZF represents the extreme value deviation ratio of the abnormal time period, and a1, a2, and a3 are preset proportional coefficients;

[0087] Abnormality judgment module: compares the abnormal time characterization value with the abnormal time characterization threshold to judge the severity of the voltage abnormality within the abnormal time unit;

[0088] In some embodiments, an abnormal time characterization value is obtained, and the abnormal time characterization is compared with an abnormal time characterization threshold, wherein the abnormal time characterization value is summarized and set by a person skilled in the art based on historical multiple experimental data;

[0089] If the abnormal time representation is less than or equal to the abnormal time representation threshold, it means that the frequency of the abnormal time period of voltage in the abnormal time unit is low, the duration is short and the stability is good, and a low abnormality signal is generated;

[0090] Based on the generated low abnormality level signal, early warning is given to these abnormal voltage devices;

[0091] If the abnormal time representation is greater than the abnormal time representation threshold, it means that the frequency of the abnormal time period of voltage in the abnormal time unit is high, the duration is long, and the stability is poor, and a high abnormality signal is generated;

[0092] The technical solution of the embodiment of the present invention is mainly as follows: first, the real-time voltage of the equipment is collected and the real-time voltage of the equipment is judged to be abnormal. When a voltage abnormality occurs, the time unit where the voltage abnormality occurs is separated, and the abnormal time proportion, the total value of the time interval deviation ratio and the extreme deviation ratio of the abnormal time period are calculated, and data processing is performed based on the calculated abnormal time proportion, the total value of the time interval deviation ratio and the extreme deviation ratio of the abnormal time period, and the abnormal time characterization value is calculated, and the abnormal time characterization value is compared with the abnormal time characterization threshold. When the abnormal time characterization value is less than or equal to the abnormal time characterization threshold, it means that the degree of voltage abnormality in the corresponding time unit equipment is low. When the abnormal time characterization value is greater than the abnormal time characterization threshold, it means that the degree of voltage abnormality in the corresponding time unit equipment is high. Therefore, the voltage of the equipment can be monitored and abnormal judgment can be performed, so as to timely understand and discover the abnormality of the equipment, and based on further analysis of the abnormal time, the severity of the equipment failure can be evaluated, and the level of response measures to be taken can be assisted in deciding, so as to optimize the maintenance strategy.

[0093] Embodiment 2:

[0094] Based on Example 1, please refer to Figure 2 As shown, an intelligent power plant equipment management system according to an embodiment of the present invention specifically includes:

[0095] Equipment abnormality frequency analysis module: Based on the generated high abnormality degree signal, the frequency of abnormal voltage equipment that generates high abnormality degree signals is analyzed;

[0096] In some embodiments, abnormal voltage devices corresponding to the generation of high abnormality signals are obtained, and these devices are marked as devices to be analyzed;

[0097] Extract the historical period before the abnormal time unit, and obtain the numerical value of the high abnormality signal generated by the device to be analyzed in the historical period;

[0098] Obtain the number of time units in the historical period, perform ratio processing on the number of generated high abnormality signal values ​​and the number of time units in the historical period, and obtain the ratio of the number of abnormality signals;

[0099] The abnormal degree signal number ratio value is compared with the abnormal degree signal number ratio threshold value, wherein the abnormal degree signal number ratio threshold value is a critical value used to determine whether the number of abnormal degree signals appear frequently in the historical period, and is summarized and set by technicians in this field based on historical experimental data;

[0100] If the value of the ratio of the number of abnormal degree signals is less than or equal to the threshold value of the number of abnormal degree signals, it means that the number of high abnormal degree signals in the historical period of the device to be analyzed is small, and an infrequent signal is generated;

[0101] If the value of the ratio of the number of abnormal degree signals is greater than the threshold value of the number of abnormal degree signals, it means that the device to be analyzed has a large number of high abnormal degree signals in the historical period, generating frequent signals;

[0102] Equipment abnormality regularity analysis module: Based on the frequently generated signals, it checks whether there is regularity when the equipment to be analyzed generates high abnormality signals;

[0103] In some embodiments, based on the generated frequent signals, the time units corresponding to the signals with high abnormality generated by the device to be analyzed in the historical period are obtained and marked as the time units to be analyzed;

[0104] The time values ​​of adjacent time units to be analyzed are calculated to obtain the time unit deviation value, all the time unit deviation values ​​are summed and averaged to obtain the time unit deviation mean, all the time unit deviation values ​​are calculated to be different from the time unit deviation mean, the difference is taken as the absolute value, and the time unit deviation degree value is obtained;

[0105] Compare the time bias unit deviation degree value with the time bias unit deviation degree threshold, wherein the time bias unit deviation degree threshold is summarized and set by a person skilled in the art based on historical experimental data;

[0106] If the time deviation unit deviation degree value is less than or equal to the time deviation unit deviation degree threshold, a smaller deviation signal is generated;

[0107] If the time deviation unit deviation degree value is greater than the time deviation unit deviation degree threshold, a large deviation signal is generated;

[0108] Obtain the number of generated signals with smaller deviations and the number of generated signals with larger deviations, sum the number of generated signals with smaller deviations and the number of generated signals with larger deviations to obtain the total number, calculate the ratio of the number of generated signals with smaller deviations to the total number, and obtain the ratio of the number of generated signals with smaller deviations;

[0109] Compare the small deviation number ratio with the small deviation number ratio threshold, wherein the small deviation number ratio threshold is set by a technician in this field based on historical experimental data;

[0110] If the proportion of small deviations is less than or equal to the threshold of small deviation proportion, it means that the proportion of small deviations is small, and a reanalysis signal is generated;

[0111] If the proportion of small deviations is greater than the threshold of small deviation proportion, it means that the proportion of small deviations is large, and a regular signal is generated;

[0112] Based on the generated regular signal, the minimum value of the time unit deviation value is extracted as the predicted regular value, and the equipment is maintained in advance when the predicted regular value is passed after the high abnormality signal is generated;

[0113] The technical solution of the embodiment of the present invention is mainly as follows: firstly, by analyzing the number of times the device to be analyzed has a high degree of abnormality in a historical period, if the number of times is large, it means that the device is a device with frequent failures and needs to be given priority attention, which helps to optimize the allocation of maintenance resources and improve maintenance efficiency;

[0114] At the same time, a regularity analysis of high abnormality levels is performed on the equipment to be analyzed that occurs more frequently. The time unit deviation values ​​of adjacent time units corresponding to the high abnormality levels are summed and averaged to obtain the time unit deviation mean. The time deviation unit deviation degree value is calculated based on the time unit deviation value and the time unit deviation mean. After comparison, if the time deviation unit deviation degree value is less than or equal to the time deviation unit deviation degree threshold, a signal with a smaller deviation is generated, and the proportion of small deviations is obtained. The proportion of small deviations is compared with the proportion of small deviations to generate a re-analysis signal or a regular signal. Therefore, the periodic interval value of the signal with a high abnormality level can be obtained based on the generated regular signal, and proactive measures can be taken to prevent equipment failures based on the periodic interval value, thereby reducing the possibility of equipment abnormalities. This can provide power plants with more comprehensive and accurate equipment maintenance management, help improve the operating efficiency and safety of power plants, and ensure the stability and reliability of power supply.

[0115] Embodiment 3:

[0116] Based on Example 1 and Example 2, please refer to Figure 2 As shown, an intelligent power plant equipment management system according to an embodiment of the present invention specifically includes:

[0117] Equipment abnormality periodicity re-analysis module: based on the generated re-analysis signal, re-analyze the periodicity of the equipment to be analyzed;

[0118] In some embodiments, based on the generated reanalysis signal, an abnormal time characterization value corresponding to the device to be analyzed when generating a high abnormality degree signal is obtained, a maximum value among the abnormal time characterization values ​​is extracted, and the maximum value of the abnormal time characterization value is marked as a first division degree standard value;

[0119] The maximum value of the abnormal time characterization value and the abnormal characterization threshold are summed and averaged, and the obtained average is marked as the second division degree standard value;

[0120] Marking the abnormal time characterization threshold as the third division level standard value;

[0121] Compare the abnormal time characterization value with the first division degree standard value, the second division degree standard value, and the third division degree standard value respectively;

[0122] If the third classification standard value is less than the abnormal time characterization value and less than or equal to the second classification degree standard value, it is marked as a class I batch;

[0123] If the second classification standard value is less than the abnormal time characterization value and less than or equal to the second classification degree standard value, it is marked as a second-class batch;

[0124] Obtain the time units to be analyzed corresponding to the generation of high abnormality signals in the first batch and the second batch, and then determine whether the time when the high abnormality signals are generated in the first batch and the second batch is periodic;

[0125] Get the percentage of small deviations corresponding to the first batch and the percentage of small deviations corresponding to the second batch respectively;

[0126] It should be noted that the calculation method of the small deviation ratio is the same as the calculation method of the small deviation ratio in the above embodiment 2, which will not be repeated here;

[0127] Compare the small deviation ratio with the small deviation ratio threshold,

[0128] If the proportion of small deviations is less than or equal to the threshold of small deviation proportion, it means that the proportion of small deviations is small, and a non-periodic signal is generated;

[0129] Based on the generated non-periodic signal, the number of inspections of the faulty equipment is increased;

[0130] If the proportion of small deviations is greater than the threshold of small deviation proportion, it means that the proportion of small deviations is large, and a periodic signal is generated;

[0131] Based on the generated periodic signal, the minimum value of the time unit deviation value is extracted as the predicted period value, and the equipment is maintained in advance when the predicted period value is exceeded after the generation of a high abnormality signal;

[0132] The technical solution of the embodiment of the present invention is mainly as follows: when generating a re-analysis signal, the abnormal time characterization value corresponding to the generated high abnormality signal is divided into batches, and then it is judged whether the time of occurrence of the high abnormality signal in different batches is periodic. Therefore, after the abnormal characterization value is divided into batches according to the size of the value, the periodicity of the occurrence of the high abnormality signal can be analyzed more accurately, and then the abnormal equipment can be more accurately managed and maintained in advance, and more scientific and reasonable maintenance plans and management strategies can be formulated based on the analysis results, thereby improving the overall operational efficiency, enhancing the operational management capabilities of the power plant, and promoting intelligent development.

[0133] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent power plant equipment management system, characterized in that: Specifically include: Equipment data collection and judgment module: collects the real-time voltage of equipment in the power plant, determines whether the equipment voltage is abnormal, and generates a voltage abnormality signal; Abnormal data calculation and processing module: based on the generated voltage abnormality signal, obtain the corresponding time unit, perform data processing, and calculate the abnormal time representation value; Abnormality judgment module: compares the abnormal time characterization value with the abnormal time characterization threshold, judges the severity of the voltage abnormality in the abnormal time unit, and generates a high abnormality signal; Equipment abnormality frequency analysis module: Based on the generated high abnormality signal, the frequency of abnormal voltage equipment that generates the high abnormality signal is analyzed to generate a frequent signal; Equipment abnormality regularity analysis module: Based on the generated frequent signals, it determines whether there is regularity when the equipment to be analyzed generates high abnormality signals, and generates re-analysis signals or regular signals; Equipment abnormality periodicity re-analysis module: Based on the generated re-analysis signal, the periodicity of the equipment to be analyzed is re-analyzed.

2. The intelligent power plant equipment management system according to claim 1, characterized in that: The process of generating the voltage abnormality signal is as follows: Obtain the real-time voltage of power plant equipment during operation; Divide the running time into several time units, extract the maximum and minimum voltage values ​​of each time unit, and compare the maximum and minimum voltage values ​​with the voltage threshold range respectively; If the maximum voltage is not within the voltage threshold range or the minimum voltage is not within the voltage threshold range, a voltage abnormality signal is generated; It is marked as an abnormal voltage device based on the generated voltage abnormality signal.

3. The intelligent power plant equipment management system according to claim 1, characterized in that: The process of obtaining the abnormal time characterization value is as follows: Obtain the abnormal time proportion, the total value of the time interval deviation ratio, and the extreme value deviation ratio of the abnormal time period; Substitute into the formula The abnormal time characterization value SY is calculated, where CX represents the abnormal time proportion, JG represents the total value of the time interval deviation ratio, ZF represents the extreme value deviation ratio of the abnormal time period, and a1, a2, and a3 are preset proportional coefficients.

4. The intelligent power plant equipment management system according to claim 3, characterized in that: The process of obtaining the abnormal time ratio is as follows: Obtain a time unit corresponding to a generated voltage abnormality signal, mark the time unit as an abnormal time unit, and obtain a real-time voltage corresponding to the abnormal time unit; A two-dimensional model is established with time as the X-axis and voltage as the Y-axis. The real-time voltage is substituted into the two-dimensional model to draw a real-time voltage curve. The maximum value and the minimum value of the voltage threshold range are used as standard values ​​to draw standard lines parallel to the X-axis, which are marked as the first standard line and the second standard line respectively. Marking the area between the first standard line and the second standard line as a normal voltage area, and marking the area above the first standard line and below the second standard line as an abnormal voltage area; Obtain the time period corresponding to the voltage abnormal area, mark it as the abnormal time period, sum up all the time values ​​of the abnormal time period to obtain the total abnormal time value of the abnormal time period, perform ratio processing on the total abnormal time value and the total time value of the abnormal time unit to obtain the abnormal time ratio value; 5. The intelligent power plant equipment management system according to claim 4, characterized in that: The process of obtaining the total value of the time interval deviation ratio and the extreme value deviation ratio of the abnormal time period is as follows: Obtain the time interval values ​​corresponding to adjacent abnormal areas, sum and average all the time interval values, perform difference calculations on all the time interval values ​​and the time interval average, take the absolute value of the difference to obtain the time interval deviation value, perform ratio calculation on the time interval deviation value to obtain the time interval deviation ratio, sum all the time interval deviation ratios to obtain the total value of the time interval deviation ratio; Extract the maximum and minimum values ​​of all time values ​​in abnormal time periods, calculate the difference between the maximum and minimum time values ​​in the abnormal time periods to obtain the extreme value difference of the time values ​​in the abnormal time periods, calculate the ratio of the extreme value difference of the time values ​​in the abnormal time periods to the minimum time values ​​in the abnormal time periods to obtain the extreme value deviation ratio of the abnormal time periods.

6. The intelligent power plant equipment management system according to claim 1, characterized in that: The acquisition process of generating a high abnormality signal is as follows: Obtaining an abnormal time representation value, and comparing the abnormal time representation with an abnormal time representation threshold; If the abnormal time representation is greater than the abnormal time representation threshold, a high abnormality signal is generated.

7. The intelligent power plant equipment management system according to claim 1, characterized in that: The process of analyzing the frequency of abnormal voltage devices generating high abnormality signals is as follows: Acquire abnormal voltage devices corresponding to signals with high abnormality, and mark these devices as devices to be analyzed; Extract the historical period before the abnormal time unit, and obtain the numerical value of the high abnormality signal generated by the device to be analyzed in the historical period; Obtain the number of time units in the historical period, perform ratio processing on the number of generated high abnormality signal values ​​and the number of time units in the historical period, and obtain the ratio of the number of abnormality signals; Compare the value of the proportion of the number of abnormal degree signals with the threshold value of the proportion of the number of abnormal degree signals; If the percentage of abnormal degree signals is greater than the percentage threshold of abnormal degree signals, a frequent signal is generated.

8. The intelligent power plant equipment management system according to claim 1, characterized in that: The process of generating the reanalysis signal or the regular signal is: Obtain the percentage of small deviations, and compare the percentage of small deviations with the threshold of the percentage of small deviations; If the percentage of small deviations is less than or equal to the percentage threshold of small deviations, a reanalysis signal is generated; If the proportion of small deviations is greater than the threshold of small deviation proportion, a regular signal is generated.

9. The intelligent power plant equipment management system according to claim 7, characterized in that: The process of obtaining the percentage of small number of deviations is as follows: Based on the generated frequent signals, the time units corresponding to the signals with high abnormality generated by the device to be analyzed in the historical period are obtained and marked as the time units to be analyzed; The time values ​​of adjacent time units to be analyzed are calculated to obtain the time unit deviation value, all the time unit deviation values ​​are summed and averaged to obtain the time unit deviation mean, all the time unit deviation values ​​are calculated to be different from the time unit deviation mean, the difference is taken as the absolute value, and the time unit deviation degree value is obtained; comparing the time bias unit deviation degree value with the time bias unit deviation degree threshold; If the time deviation unit deviation degree value is less than or equal to the time deviation unit deviation degree threshold, a smaller deviation signal is generated; If the time deviation unit deviation degree value is greater than the time deviation unit deviation degree threshold, a large deviation signal is generated; Obtain the number of signals with smaller deviations and the number of signals with larger deviations, sum the number of signals with smaller deviations and the number of signals with larger deviations to obtain the total number, calculate the ratio of the number of signals with smaller deviations to the total number, and obtain the proportion of signals with smaller deviations.

10. The intelligent power plant equipment management system according to claim 8, characterized in that: The process of periodically reanalyzing the equipment to be analyzed is as follows: Obtain the time units to be analyzed corresponding to the signals with high abnormality level generated by the first-class batches and the second-class batches, and respectively obtain the percentage of small deviations corresponding to the first-class batches and the percentage of small deviations corresponding to the second-class batches; Compare the small deviation ratio with the small deviation ratio threshold, If the percentage of small deviations is less than or equal to the percentage threshold of small deviations, a non-periodic signal is generated; If the proportion of small deviations is greater than the threshold of small deviation proportion, a periodic signal is generated.

11. The intelligent power plant equipment management system according to claim 9, characterized in that: The acquisition process of the first batch and the second batch is as follows: Based on the generated reanalysis signal, obtain the abnormal time characterization value corresponding to the device to be analyzed when generating a high abnormality degree signal, extract the maximum value of the abnormal time characterization value, and mark the maximum value of the abnormal time characterization value as the first division degree standard value; The maximum value of the abnormal time characterization value and the abnormal characterization threshold are summed and averaged, and the obtained average is marked as the second division degree standard value; Marking the abnormal time characterization threshold as the third division level standard value; Compare the abnormal time characterization value with the first division degree standard value, the second division degree standard value, and the third division degree standard value respectively; If the third classification standard value is less than the abnormal time characterization value and less than or equal to the second classification degree standard value, it is marked as a class I batch; If the second classification standard value is less than the abnormal time characterization value and is less than or equal to the second classification degree standard value, it will be marked as a Class II batch.

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