CN115856390A - Monitoring and diagnosing method and system of power switch cabinet and storage medium - Google Patents
Monitoring and diagnosing method and system of power switch cabinet and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of power switch cabinets, in particular to a monitoring and diagnosing method, a monitoring and diagnosing system and a storage medium of a power switch cabinet, wherein the method comprises the following steps: collecting first monitoring data, wherein the first monitoring data comprises current data and temperature data; preprocessing the first monitoring data to obtain second monitoring data; performing static judgment on the second monitoring data to obtain third monitoring data; performing diagnosis calculation on the third monitoring data; and outputting a diagnosis result. According to the technical scheme provided by the invention, through methods such as data screening, smooth regularization processing, static condition judgment and the like, the load temperature rise diagnosis method can cover the conditions of small load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, and through various diagnosis result output processes and a maximum temperature prediction algorithm under a rated load, the diagnosis result is reliable and reasonable and is easy to understand by a user.
Description
Technical Field
The invention relates to the field of power switch cabinets, in particular to a comprehensive monitoring and diagnosing method and system of a power switch cabinet and a storage medium.
Background
With the real-time increase of on-line monitoring data of power equipment, the current sensor and the temperature sensor are gradually popularized and used, the monitoring data volume is extremely large, and the monitoring data generation speed is extremely high. Especially on load current heating type power equipment, such as power switch cabinets. In the industry, a large amount of load current and temperature data of field equipment are collected, most of the load current and temperature data are designed by a diagnostic algorithm and subjected to threshold judgment and alarm, but when the monitored data have the conditions of small load current and current data pulse fluctuation and the like, the diagnostic result is inaccurate. Based on the situation, the state monitoring of the power equipment has the defect of insufficient diagnosis and early warning functions, so that the comprehensive popularization and application of the state monitoring technology of the power equipment are greatly restricted.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a comprehensive monitoring and diagnosing method, a comprehensive monitoring and diagnosing system and a storage medium for a power switch cabinet, which can cover the conditions of small load current and current pulse fluctuation when the load temperature rise diagnosing method monitors the current temperature data of the power switch cabinet, and ensure that the diagnosis result is reliable and reasonable and is easy to understand by a user through various diagnosis result output processes and a maximum temperature predicting algorithm under a rated load.
In order to achieve the above object, a first aspect of the present invention provides a method for comprehensive monitoring and diagnosis of a power switch cabinet, the method comprising:
collecting first monitoring data, wherein the first monitoring data comprises current data and temperature data;
preprocessing the first monitoring data to obtain second monitoring data;
performing static judgment on the second monitoring data to obtain third monitoring data;
and carrying out diagnosis calculation on the third monitoring data and outputting a diagnosis result.
Further, the step of preprocessing the first monitoring data to obtain second monitoring data includes:
reasonably filtering the first monitoring data through a preset temperature data range and a preset current data range;
smoothing the first monitoring data after the rationalization filtering, wherein the smoothing comprises equating fast pulse type current data into relatively smooth and slowly-changing stable current data by adopting a historical data averaging method;
and performing data regularization processing on the first monitoring data subjected to the smoothing processing.
Further, the data regularization process includes:
when Ix is less than or equal to 10A, enabling the reduction current Io =0, namely, the equipment is considered to be in a shutdown state;
when 10A-Ix-Ie is reduced by 20%, reducing current Io =20% by;
when Ix is more than or equal to 20 percent, ie, enabling the reduction current Io = Ix to enter a normal diagnosis state;
replacing the current smooth value with the normalized value after the normalization processing, and performing subsequent diagnosis calculation;
20% of the rated current Ie is an integrated value, recorded as 20% Ie; the Ix is a smoothed value and the Io is a rounded value.
Further, the obtaining of third monitoring data by performing static judgment on the second monitoring data includes:
when the second monitoring data meet one or more system state conditions, obtaining third monitoring data; the system state conditions include:
condition 1: and (3) load static state: in the time from the current moment to one hour before, the minimum value of the current is more than 20% of the rated current, and the maximum value and the minimum value of the current are compared, and the ratio of the current is not more than 20%;
condition 2: temperature statics of the measuring points: in the time from the current moment to 0.5 hour before, the temperature of the measuring point is always greater than the ambient temperature, and the variation of the highest value and the lowest value is not more than 1K compared with the current value;
condition 3: ambient temperature: the maximum and minimum values of the ambient temperature do not differ by more than 1K from the current values over the 0.5 hour period from the current time.
Further, the outputting the diagnosis result includes:
(1) Diagnosing a temperature threshold of a measuring point;
(2) Diagnosing a current threshold of a measuring point;
(3) Diagnosing the unbalance threshold of the three-phase current;
(4) Diagnosing the threshold value of the load temperature rise performance index of the equipment;
(5) Diagnosing the three-phase deviation threshold of the load temperature rise performance of the equipment;
(6) Maximum temperature prediction at rated load.
Further, the equipment load temperature rise performance indicator threshold diagnosis includes:
setting early warning diagnosis threshold and alarm diagnosis threshold through algorithm formula
Performing threshold diagnosis on load temperature rise performance index when 4 x Ki is less than k Rλ (t) when Ki is less than or equal to 9, early warning is carried out; when k is Rλ When the (t) is more than 9X Ki, alarming; k is Rλ (t) and Ki are the load temperature rise performance indexes, and the standard unit is K/A 2 ×10 5 。
Further, the maximum temperature prediction at rated load includes,
when theta is 0 (t)+D1≤Δθ(t) M ＜θ 0 (t) + D2, performing low threshold early warning;
when Δ θ (t) M ≥θ 0 When the sum of the t and the D2 is greater than the preset threshold value, performing high threshold value early warning;
theta is described 0 (t) is the temperature at the measurement point, theθ(t) M The maximum temperature of the measuring point is calculated according to the following formula: theta (t) M ＝Δθ(t) M +θ 0 (t)，Δθ(t) M ＝k Rλ (t)I e 2 The said I e Is the rated current of the power switch cabinet, I e And D1 is a low early warning value, and D2 is a high early warning value.
The invention provides a comprehensive monitoring and diagnosing system of a power switch cabinet, which comprises:
the acquisition module is used for acquiring first monitoring data, and the first monitoring data comprises current data and temperature data;
the control module is used for preprocessing the first monitoring data to obtain second monitoring data; performing static judgment on the second monitoring data to obtain third monitoring data; and carrying out diagnosis calculation on the third monitoring data and outputting a diagnosis result.
Furthermore, the acquisition module comprises a current sensor, a temperature sensor and a current temperature sensor, and the control module comprises a wireless forwarding device and a background monitoring system.
A third aspect of the present invention provides a storage medium, wherein the storage medium stores an executable program, and when the executable program is executed, the method for comprehensively monitoring and diagnosing the power switch cabinet is any one of the methods described above.
Compared with the prior art, the invention provides a comprehensive monitoring and diagnosing method, a comprehensive monitoring and diagnosing system and a storage medium for a power switch cabinet, which can effectively solve the problem that a diagnosis and calculation result is inaccurate due to small-load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like. In the comprehensive diagnosis method of the power switch cabinet, methods such as data screening, smooth regularization processing, static condition judgment and the like are designed, so that the load temperature rise diagnosis method can cover the conditions of small load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, and a maximum temperature prediction algorithm under various diagnosis result output processes and rated loads is designed, so that the diagnosis result is reliable and reasonable and is easy to understand by a user.
Drawings
Fig. 1 is a first flowchart of a comprehensive monitoring and diagnosing method for a power switch cabinet according to the present invention.
Fig. 2 is a second flowchart of the comprehensive monitoring and diagnosing method for the power switch cabinet according to the present invention.
Fig. 3 is a third flowchart of the comprehensive monitoring and diagnosing method for the power switch cabinet provided by the present invention.
Fig. 4 is a schematic structural diagram of an integrated monitoring and diagnosing system of a power switch cabinet according to the present invention.
Fig. 5 is a second schematic structural diagram of the integrated monitoring and diagnosing system of the power switch cabinet according to the present invention.
Fig. 6 is a first current data graph of the integrated monitoring and diagnosis system for a power switch cabinet according to the present invention.
Fig. 7 is a second current data graph of the integrated monitoring and diagnosis system for a power switch cabinet according to the present invention.
Fig. 8 is a third current data graph of the integrated monitoring and diagnosis system for a power switch cabinet according to the present invention.
Fig. 9 is a fourth current data graph of the integrated monitoring and diagnosis system for power switch cabinets provided in the present invention.
Fig. 10 is a fifth current data graph of the integrated monitoring and diagnosis system for power switch cabinets provided in the present invention.
Fig. 11 is a first current data graph of the integrated monitoring and diagnosis system for a power switch cabinet according to the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings and the detailed description, and it should be noted that any combination of the embodiments or technical features described below can be used to form a new embodiment without conflict.
Referring to fig. 1, fig. 1 is a flowchart of a method for comprehensive monitoring and diagnosis of a power switch cabinet according to the present invention.
A comprehensive monitoring and diagnosing method for a power switch cabinet is characterized by comprising the following steps:
collecting first monitoring data, wherein the first monitoring data comprises current data and temperature data;
specifically, current data and temperature data are collected by a current temperature sensor.
Preprocessing the first monitoring data to obtain second monitoring data;
specifically, the first monitoring data is subjected to rationalization filtering, then data smoothing processing is performed, and finally data normalization processing is performed.
Performing static judgment on the second monitoring data to obtain third monitoring data;
specifically, three system static conditions of load static, measuring point temperature static and environment temperature are judged on the processed second monitoring data, subsequent diagnosis calculation is carried out on the data meeting the system static condition judgment, and the data not meeting the system static condition does not participate in calculation.
Performing diagnosis calculation on the third monitoring data, and outputting a diagnosis result;
specifically, the third monitoring data is diagnosed and calculated, and output results are output through processes of point temperature threshold diagnosis, point current threshold diagnosis, three-phase current unbalance degree threshold diagnosis, equipment load temperature rise performance index threshold diagnosis, equipment load temperature rise performance three-phase deviation degree threshold diagnosis and highest temperature prediction in rated load.
The comprehensive monitoring and diagnosing method of the power switch cabinet enables the load temperature rise diagnosing method to cover the condition of small load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, effectively solves the problem of inaccurate diagnosis and calculation results caused by small load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like through various diagnosis result output processes and a highest temperature predicting algorithm under rated load, and enables the diagnosis results to be reliable and reasonable and to be easily understood by users.
Further, referring to fig. 2, as a preferred embodiment, in this embodiment, the process of preprocessing the first monitoring data to obtain the second monitoring data includes the following three steps:
s1, rationalizing and filtering first monitoring data: the first step of data preprocessing is to reasonably filter the original temperature measurement data and current data of the sensor, so that the reasonable and normal monitoring data can be used for diagnosis and calculation.
The rationalized filtration conditions were as follows:
(1) Reasonable range of preset temperature data: the temperature is between 20 ℃ below zero and 199 ℃, and if the temperature exceeds the preset temperature, judging that the sensor data are abnormal;
(2) Reasonable range of preset current data: and 0A to 2500A, and if the data exceeds the threshold value, judging that the sensor data is abnormal.
S2, smoothing first monitoring data: the second step of the data preprocessing is to smooth the first monitoring data filtered reasonably, so that the first monitoring data can deal with larger fluctuation data, convert dynamic fluctuation data into an equivalent static condition effect, and eliminate dynamic impact caused by fluctuating load current.
The smoothing method comprises the following steps: and (3) equating the fast pulse type current data to relatively smooth and slowly-changing stable current data by adopting a historical data averaging method.
The equivalence principle of the smoothing algorithm is as follows: the total thermal power is similar to the principle. The total power of the smoothed current and the original current is similar in the history process, and the current is considered to be equivalent, and the total power is equal.
The data smoothing method comprises the following steps:
and averaging the historical monitoring data (default is 1 hour of historical data) of the current for a period of time (the average value adopts a root mean square value) to replace the current monitoring data, and then calculating and diagnosing by using a diagnostic algorithm.
Setting: the root mean square value of historical current data of a period of time is Ix;
the total number of the historical current data in a period of time is n;
ik is the kth data in the n data, the value range of k is 1 to k,
then:
data processing algorithm example:
and taking the original data within one hour to calculate the root mean square value. And (3) calculating data uploaded once in 5 minutes by the sensor, wherein the total current data in one hour is 12, and the root mean square value of the current data is as follows:
theoretical description of the equivalence of the smoothing algorithm:
the algorithm is designed according to the principle that the total thermal power is close, original current data with any length of historical data time (T) is selected, the total power of the current is calculated by the current data after smoothing processing, and whether the total power is relative or approximate is compared. The specific total power comparison equation is as follows,
total power of original current:
P y ＝(Rt 1 I y1 2 +Rt 2 I y2 2 +…+Rt x I yx 2 )/T＝(t 1 I y1 2 +t 2 I y2 2 +…+t x I yx 2 )R/T
total current power after smoothing:
P p ＝(Rt 1 I p 2 +Rt 2 I p 2 +…+Rt x I p 2 )/T＝(t 1 +t 2 +…+t x )I p 2 R/T＝I p 2 R
because equal sampling intervals are used, let:
t＝t 1 ＝t 2 ＝…＝t x
the following reasons: x = T/T, so: T/T =1/x
Then, the total power of the raw current:
P y ＝(I y1 2 +I y2 2 +…+I yx 2 )Rt/T＝(I y1 2 +I y2 2 +…+I yx 2 )R/x
the algorithm for smoothing includes:
I p 2 ＝(I y1 2 +I y2 2 +…+I yx 2 )/x
therefore:
P p ＝P y
namely, the total power of the current after the smoothing processing is equal to the total power of the original current.
Wherein, P y For total power of the original current, P p For smoothing the total current power, R is the total resistance in the current line of the monitored installation, I y Is the original current value, I p For the smoothed current value, T is the acquisition time interval and T is the total time of sampling.
S3, data normalization processing: the third step of the data preprocessing is to perform regularization processing on the smoothed first monitoring data.
Load current data of a plurality of switch cabinets in an electric enterprise fluctuate within a small current range (for example, less than 100A), for rated current of the switch cabinet (mostly, rated current 1250A), temperature rise change caused by small current load change is far smaller than measurement deviation of a system (including measurement current deviation and temperature measurement deviation), and because of the existence of basic temperature rise in the system and the influence of non-current heating factors, including voltage (electric field) effect heating, ferromagnetic loss, various monitoring instruments, dehumidification, illumination, air exhaust and other basic equipment, and the like, when small current load is caused, the corresponding relation of current heating power and temperature rise can seriously deviate from a theoretical value. The method can deal with the working state of the equipment power consumption, simultaneously meets the requirement of a user on data correlation in equipment state diagnosis, adopts a current data processing and normalizing mode, and equates the basic temperature rise of the system to heating caused by a normalized current value, thereby being beneficial to actual diagnosis calculation, leading the diagnosis result data to be reasonably displayed and easily understood by the user.
After the data smoothing processing, adopting data normalization processing, wherein the method comprises the following steps:
setting 20% of rated current Ie as an integral value, recorded as 20% Ie, by taking the rated current Ie =1250A switch cabinet as an example; setting the first monitoring data after the smoothing processing as a smooth value Ix, setting the first monitoring data after the normalization processing as a normalization value IO,
when Ix is less than or equal to 10A, enabling the normalized current IO =0, namely, the equipment is considered to be in a shutdown state;
when 10A-Ix-Ie is reduced by 20%, reducing current Io =20% by;
entering a normal diagnostic state when Ix is greater than or equal to 20%;
and replacing the current smooth value with the normalized value after the normalization processing to perform subsequent diagnosis calculation.
The comprehensive monitoring and diagnosing method of the power switch cabinet enables the load temperature rise diagnosing method to cover the condition of small load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, effectively solves the problem of inaccurate diagnosis and calculation results caused by small load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like through various diagnosis result output processes and a highest temperature predicting algorithm under rated load, and enables the diagnosis results to be reliable and reasonable and to be easily understood by users.
Further, as a preferred scheme, in this embodiment, the performing static judgment on the second monitoring data to obtain third monitoring data includes performing system static condition judgment on the second monitoring data obtained after data preprocessing, and obtaining the third monitoring data when the second monitoring data meets one or more system state conditions, where the system state conditions include:
condition 1: and (3) load static state: in the time from the current moment to one hour before, the minimum value of the current is more than 20% of the rated current, and the maximum value and the minimum value of the current are compared, and the ratio of the current is not more than 20%;
condition 2: temperature statics at the measuring point: in the time from the current moment to 0.5 hour before, the temperature of the measuring point is always greater than the ambient temperature, and the variation of the highest value and the lowest value is not more than 1K compared with the current value;
Δθ(t)＝θ 2 (t)-θ 0 (t)>2Kandθ 2 (t) max -θ 2 (t) min <1K
condition 3: ambient temperature: the maximum and minimum values of the ambient temperature do not differ by more than 1K from the current values over the 0.5 hour period from the current time.
θ 0 (t) max -θ 0 (t) min <1K
The comprehensive monitoring and diagnosing method of the power switch cabinet enables the load temperature rise diagnosing method to cover the condition of small load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, effectively solves the problem of inaccurate diagnosis and calculation results caused by small load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like through various diagnosis result output processes and a highest temperature predicting algorithm under rated load, and enables the diagnosis results to be reliable and reasonable and to be easily understood by users.
Further, referring to fig. 2, as a preferred embodiment, in this embodiment, performing a diagnostic calculation on the third monitoring data, and outputting a diagnostic result includes: and carrying out diagnosis calculation on the third monitoring data after the third monitoring data is obtained, and outputting a diagnosis result after the diagnosis calculation is carried out.
The specific content of the diagnosis method is as follows, and the main algorithm formula adopted by the diagnosis method is as follows:
Δθ(t)＝k Rλ (t)I(t) 2 ，
Wherein, R is the total resistance of the loop of the measured section, and lambda is the comprehensive heat conductivity coefficient between the temperature measuring point and the environmental reference point of the measured section of the power equipment.
Q (t) is the thermal power of the heat source of the section to be measured of the electrical equipment at the time t, delta theta (t) is the temperature rise of the section to be measured of the electrical equipment at the time t, I (t) 2 The square value of the measured current of the power equipment at the time t.
θ 2 (t) is the temperature of the temperature measuring point of the measured section of the power equipment at the time t, theta 0 And (t) is the environmental reference point temperature of the measured section of the power equipment at the time t. k is a radical of Rλ And (t) is the index of the load temperature rise performance of the equipment.
Wherein, the process of outputting the diagnosis result is as follows:
taking the monitored power switch cabinet as a KYN centrally installed switchgear (rated current is 1250A) as an example, the specific content of the diagnostic result output process is as follows:
(1) And a first diagnostic result output process, namely, the temperature threshold diagnosis of the measuring point.
Two thresholds of overheating early warning and overheating warning are set,
when the temperature of the measuring point reaches 65 ℃, carrying out overheating early warning,
and when the temperature of the measuring point reaches 75 ℃, carrying out overheating alarm.
(2) And a second diagnostic result output process, namely, the current threshold value diagnosis of the measuring point.
Setting two threshold values of overcurrent early warning and overcurrent alarm, wherein the rated current value of the equipment is I e ，
When the point current reaches 65% e When the system is in use, the over-current early warning is carried out,
when the temperature of the measuring point reaches 75% e And carrying out overcurrent alarm.
(3) And thirdly, outputting a diagnosis result, and diagnosing the unbalanced degree threshold of the three-phase current.
The algorithm with three-phase current imbalance is as follows:
I max (t)＝max(I a (t)，I b (t)，I c (t))
I min (t)＝min(I a (t)，I b (t)，I c (t))
wherein epsilon 1 Is three-phase unbalance, I max (t) is the maximum value of the current in the three-phase current measuring point, I min (t) is the minimum value of three-phase current measuring points, I a (t) phase current A, I b (t) phase B current, I c And (t) represents the C-phase current.
And setting the allowable range of the unbalance degree of the three-phase current and making a state diagnosis. Two diagnostic thresholds are set, early warning and alarm, respectively, wherein,
when 60% < ε 1 When the concentration is less than 85 percent, early warning is carried out,
when epsilon 1 And when the concentration is more than 85 percent, alarming.
(4) Fourth, the diagnostic result is output, and the equipment load temperature rise performance index k Rλ (t) threshold diagnosis.
Having an algorithmic formula
The standard unit is K/A 2 ×10 5 Setting two diagnostic thresholds, namely early warning and alarming respectively,
when 4 x Ki < k Rλ When (t) is less than or equal to 9 x Ki, early warning is carried out;
when k is Rλ And when the (t) > 9 × Ki, alarming.
The calculation method of Ki is as follows: during the debugging of the equipment, the maximum value in Ki historical data is taken under the stable running state of the equipment.
(5) And fifthly, outputting a diagnosis result, and diagnosing the three-phase deviation degree epsilon (t) threshold of the equipment load temperature rise performance.
The algorithm formula is as follows:
k Rλ (t)max＝max(k Rλ a(t)，k Rλ b(t)，k Rλ c(t))
k Rλ (t)ave＝average(k Rλ a(t)，k Rλ b(t)，k Rλ c(t))
wherein epsilon (t) is three-phase deviation degree of load temperature rise performance of equipment, and k Rλ (t) max is the maximum value of the three-phase load temperature rise performance index, k Rλ (t) ave is the average value of the three-phase load temperature rise performance indexes, k Rλ a (t) is A phase load temperature rise performance index, k Rλ B (t) is B phase load temperature rise performance index, k Rλ C (t) is the index of C phase load temperature rise performance.
Two diagnostic thresholds are set, early warning and alarm, respectively, wherein,
when the epsilon (t) is more than 60% and less than or equal to 85%, early warning is carried out;
and when epsilon (t) is more than 85%, alarming.
(6) And a sixth diagnostic result output process, namely highest temperature prediction in rated load.
Rated current is set as I e In I e The highest temperature of the measuring point is theta (t) M 。I e According to the actual specification setting of the power equipment, no default value exists, and the predicted highest temperature of the measuring point is as follows:
θ(t) M ＝Δθ(t) M +θ 0 (t)，Δθ(t) M ＝k Rλ (t)I e 2
calculate theta (t) M According to data provided by a power switch cabinet manufacturer, for example, a low early warning value of the rated current reference temperature rise of a dangerous heating point of the cabinet with the rated current of 1250A is D1=65K, and a high early warning value of the rated current reference temperature rise is D2=71.5K.
When theta is 0 (t)+D1≤Δθ(t) M ＜θ 0 (t) + D2, byEarly warning of a low threshold value;
when Δ θ (t) M ≥θ 0 And (t) + D2, performing high-threshold early warning.
Description of the conditions: for system setup I e Should be greater than or equal to all I (t), i.e. I e Is more than or equal to I (t). Otherwise, the system reports an error and does not perform calculation.
The comprehensive monitoring and diagnosing method of the power switch cabinet enables the load temperature rise diagnosing method to cover the condition of small-load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, effectively solves the problem of inaccurate diagnosis and calculation results caused by small-load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like through various diagnostic result output processes and a maximum temperature predicting algorithm under rated load, enables the diagnostic results to be reliable and reasonable, and is easy to understand by users.
As shown in fig. 4 and 5, the present invention further provides a comprehensive monitoring and diagnosing system for a power switch cabinet, including:
and the control module is used for preprocessing the first monitoring data to obtain second monitoring data, performing static judgment on the second monitoring data to obtain third monitoring data, and performing diagnostic calculation on the third monitoring data to output a diagnostic result.
The acquisition module comprises a current sensor, a temperature sensor and a current temperature sensor, the acquisition module is installed in the position of a plum blossom contact inside a power switch cabinet to monitor current and temperature data, the sensor is directly installed on the plum blossom contact of the switch cabinet, the temperature and the current of a contact finger are measured closely, data are uploaded to a background system in real time, and the current temperature sensor is used as a composite sensor and can monitor the temperature and the current simultaneously. The energy is collected by the magnetic field for the sensor, and the starting current is 5A at minimum. The temperature of a plurality of contact fingers of the tulip contact is measured through the infrared temperature measuring sensor, the load current passing through the current contact arm is measured in a non-contact mode, and 2.4GHz wireless transmission can be achieved.
The control module comprises a wireless forwarding device and a background monitoring system, the current temperature sensor is in wireless communication connection with the wireless forwarding device, and monitoring data are wirelessly uploaded to the background monitoring system through the wireless forwarding device; the background monitoring system has the functions of displaying real-time original monitoring data, calculating a diagnosis algorithm, outputting a diagnosis result, checking historical data and the like.
According to the comprehensive monitoring and diagnosing system of the power switch cabinet, the temperature and the current of the contact finger are measured in a close range through various sensors, and the data are uploaded to the background system in real time, so that the condition of small-load current and current pulse fluctuation can be covered when the load temperature rise diagnosing method monitors the current and temperature data of the power switch cabinet, the problem that the diagnosing and calculating result is inaccurate due to small-load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like is effectively solved through various diagnosing result output processes and a maximum temperature predicting algorithm under a rated load, the diagnosing and calculating result is reliable and reasonable, and the diagnosing result is easy to understand by a user.
Further, referring to fig. 6 to 10, in this embodiment, by using the comprehensive monitoring and diagnosing method and system, for a switch rectifier cabinet with small load current fluctuation in an actual engineering project, current and temperature historical data of a day of the switch rectifier cabinet is taken to perform an example application of the comprehensive diagnosing method, and data verification is performed to select three-phase current and temperature data of an upper contact of the switch rectifier cabinet. The model of the monitored switch cabinet is KYN28A, the rated current of a main bus and the rated current of a breaker are 1250A, the rated current of a main loop is 500A, and the rated voltage is 12KV.
The method comprises the following steps: the current temperature sensor is used for collecting current temperature data of the KYN28A switch cabinet plum blossom contact, and first monitoring data are obtained.
Step two: and reasonably filtering original data of the engineering data.
The original temperature measurement data and the current data of the sensor are reasonably judged and screened before entering the next data processing step, so that the normal and credible monitoring data can be diagnosed. The raw current data of the rectifier cabinet is subjected to data screening.
The three-phase filtered current data curve diagram of the contacts on the rectifier cabinet is shown in fig. 6, and the result describes that: and (4) screening a threshold range of the rectified original data, filtering illegal data to obtain reasonable monitoring data, and then performing subsequent data processing.
Step three: and (5) processing engineering data by using a smoothing algorithm.
And (3) carrying out smoothing algorithm processing on the filtered data in the fourth step, wherein the current data after the three phases of the contacts on the rectifier cabinet are smoothed is as shown in figure 7, and the result is described as follows: after the original current data are subjected to smoothing processing, the fluctuation of the current data is reduced, and the current data are stable.
Step four: and (5) processing the engineering data by a normalization algorithm.
The smoothed data is processed by a normalization algorithm, and the following graph is shown in fig. 8 after the current data of the rectifier cabinet is processed, and the result is described as follows: the normalization algorithm normalizes the light load current data to a uniform value. After the small load current data are smoothed, the data are normalized to 0A and 20 Ie.
Step five: and judging the static condition of the engineering data system.
The relation curve graph of the three-phase normalized current of the contact on the rectifier cabinet and the static point is shown in fig. 9, wherein the value of 1 on the right ordinate represents that the static condition is achieved, and the value of 0 represents that the static condition is not achieved. The results describe: and judging the static condition of the system on the current data after the smooth normalization processing, judging that the dynamic data with small current fluctuation of the previous part reaches the static condition, and judging that the condition with large current fluctuation of the previous part is a non-static condition. The method can be used for diagnosing the dynamic data with small load.
Step six: and (5) engineering data diagnosis and calculation.
The rectifier cabinet uses the current data after the smooth regularization processing to perform static judgment, and then performs diagnostic calculation of the load temperature rise performance index Ki and the deviation degree thereof, and the result is shown in fig. 10. The results describe: after the system static determination is performed, the data that does not reach the static condition is not calculated, and k is calculated as shown in fig. 10 Rλ And (t) and epsilon (t), judging a threshold value, wherein the diagnostic value does not reach the early warning value, and the diagnostic result is that the equipment is in a normal state.
Further, referring to fig. 11, in the present embodiment, an equivalent description of the current data smoothing algorithm is as follows: and selecting original current data in a period of time (taking 6 hours as an example) in the engineering data and the current data after smoothing treatment to calculate the total power of the current, and comparing the difference of the total power. The calculation results are as follows:
the deviation result of the total current Pp after the smoothing process from the original total current Pp is shown in fig. 11, and the result describes: the sum of the total power of the smoothed current and the total power of the original current is almost consistent, which can be regarded as power equivalence, and the result proves that the current data processed by using the smoothing algorithm can replace the original data to be calculated.
According to the comprehensive monitoring and diagnosing method and system for the power switch cabinet, the temperature and the current of the contact finger are measured in a short distance through various sensors, and the data are uploaded to a background system in real time, so that the situation of small load current and current pulse fluctuation can be covered when the load temperature rise diagnosing method monitors the current and temperature data of the power switch cabinet, the problem that the diagnosing and calculating result is inaccurate due to small load current and current pulse fluctuation in the power switch cabinet in application scenes of industrial and mining enterprises, power companies and the like is effectively solved through various diagnosing result output processes and a highest temperature predicting algorithm under rated load, and the diagnosing and calculating result is reliable and reasonable and is easy to understand by a user.
In addition, the invention also provides a storage medium, wherein the storage medium stores a computer program, and the computer program realizes the comprehensive monitoring and diagnosis method of the power switch cabinet when being executed by the computer.
Compared with the prior art, the comprehensive monitoring and diagnosing method, the comprehensive monitoring and diagnosing system and the storage medium for the power switch cabinet can effectively solve the problem that the diagnosis and calculation result is inaccurate due to small load current and current pulse fluctuation in the power switch cabinet in the application scenes of industrial and mining enterprises, power companies and the like. In the comprehensive diagnosis method, methods such as data screening, smooth regularization processing, static condition judgment and the like are designed, so that the load temperature rise diagnosis method can cover the conditions of small load current and current pulse fluctuation when monitoring the current temperature data of the power switch cabinet, and the diagnosis result is reliable and reasonable and is easy to understand by a user through various diagnosis result output processes and a maximum temperature prediction algorithm under a rated load.
The above embodiments are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, and any insubstantial changes and substitutions made by those skilled in the art based on the present invention are within the protection scope of the present invention.
Claims (10)
1. A monitoring and diagnosing method for a power switch cabinet is characterized by comprising the following steps:
collecting first monitoring data, wherein the first monitoring data comprises current data and temperature data;
preprocessing the first monitoring data to obtain second monitoring data;
performing static judgment on the second monitoring data to obtain third monitoring data;
and carrying out diagnosis calculation on the third monitoring data and outputting a diagnosis result.
2. The method for monitoring and diagnosing the power switch cabinet as recited in claim 1, wherein the step of preprocessing the first monitoring data to obtain second monitoring data comprises the steps of:
reasonably filtering the first monitoring data through a preset temperature data range and a preset current data range;
smoothing the first monitoring data after the rationalization filtering, wherein the smoothing comprises equating fast pulse type current data into relatively smooth and slowly-changing stable current data by adopting a historical data averaging method;
and performing data regularization processing on the first monitoring data subjected to the smoothing processing.
3. The method for monitoring and diagnosing the power switch cabinet as claimed in claim 2, wherein the data normalization process comprises:
when Ix is less than or equal to 10A, enabling the reduction current Io =0, namely, the equipment is considered to be in a shutdown state;
when 10A-Ix-Ie is reduced by 20%, reducing current Io =20% by;
when Ix is more than or equal to 20 percent, ie, enabling the reduction current Io = Ix to enter a normal diagnosis state;
replacing the current smooth value with the normalized value to perform subsequent diagnosis calculation;
20% of the rated current Ie is an integrated value, recorded as 20% Ie; the Ix is a smoothed value and the Io is a rounded value.
4. The method for monitoring and diagnosing the power switch cabinet as claimed in claim 1, wherein the step of performing the static judgment on the second monitoring data to obtain third monitoring data comprises the steps of:
when the second monitoring data meet one or more system state conditions, obtaining third monitoring data; the system state conditions include:
condition 1: and (3) load static state: in the time from the current moment to one hour before, the minimum value of the current is more than 20% of the rated current, and the maximum value of the current is compared with the minimum value, and the conversion ratio is not more than 20%;
condition 2: temperature statics of the measuring points: in the time from the current moment to 0.5 hour before, the temperature of the measuring point is always greater than the ambient temperature, and the variation of the highest value and the lowest value is not more than 1K compared with the current value;
condition 3: ambient temperature: the maximum and minimum values of the ambient temperature do not differ by more than 1K from the current values over the 0.5 hour period from the current time.
5. The method for monitoring and diagnosing the power switch cabinet as claimed in claim 1, wherein the step of outputting the diagnosis result comprises the steps of:
(1) Diagnosing a temperature threshold of a measuring point;
(2) Diagnosing a measuring point current threshold;
(3) Diagnosing a three-phase current unbalance threshold;
(4) Diagnosing the load temperature rise performance index threshold of the equipment;
(5) Diagnosing the three-phase deviation threshold of the load temperature rise performance of the equipment;
(6) And predicting the highest temperature at rated load.
6. The method for monitoring and diagnosing the power switch cabinet as claimed in claim 5, wherein the diagnosing the equipment load temperature rise performance index threshold includes:
setting early warning diagnosis threshold and alarm diagnosis threshold through algorithm formula
Performing threshold diagnosis on load temperature rise performance index when 4 x Ki is less than k Rλ (t) when Ki is less than or equal to 9, early warning is carried out; when k is Rλ When the (t) is more than 9X Ki, alarming; k is Rλ (t) and Ki are the load temperature rise performance indexes, and the standard unit is K/A 2 ×10 5 (ii) a Delta theta (t) is the temperature rise of the section to be measured of the power equipment, I (t) 2 The square value of the measured current of the power equipment.
7. The method for monitoring and diagnosing a power switchgear according to claim 5, wherein the prediction of the highest temperature at rated load includes,
when theta is measured 0 (t)+D1≤Δθ(t) M ＜θ 0 (t) + D2, performing low threshold early warning;
when Δ θ (t) M ≥θ 0 (t) + D2, performing high threshold early warning;
theta is described 0 (t) is the temperature at the measurement point, θ (t) M The maximum temperature of the measuring point is calculated according to the following formula: theta (t) M ＝Δθ(t) M +θ 0 (t)，Δθ(t) M ＝k Rλ (t)I e 2 Said I is e Is the rated current of the power switch cabinet, I e And D1 is a low early warning value, and D2 is a high early warning value.
8. A monitoring and diagnostic system for a power switch cabinet, comprising:
the acquisition module is used for acquiring first monitoring data, and the first monitoring data comprises current data and temperature data;
the control module is used for preprocessing the first monitoring data to obtain second monitoring data; performing static judgment on the second monitoring data to obtain third monitoring data; and carrying out diagnosis calculation on the third monitoring data and outputting a diagnosis result.
9. The monitoring and diagnosis system for the power switch cabinet according to claim 8, wherein the acquisition module comprises a current sensor, a temperature sensor and a current temperature sensor, and the control module comprises a wireless forwarding device and a background monitoring system.
10. A storage medium storing a computer-executable program which, when executed by a processor, implements a method of integrated monitoring and diagnosis of a power switchgear as claimed in any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116989431A (en) * | 2023-09-25 | 2023-11-03 | 深圳市华图测控系统有限公司 | Power consumption reduction method, device and system based on dehumidifier special for museum |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116989431A (en) * | 2023-09-25 | 2023-11-03 | 深圳市华图测控系统有限公司 | Power consumption reduction method, device and system based on dehumidifier special for museum |
| CN116989431B (en) * | 2023-09-25 | 2023-12-15 | 深圳市华图测控系统有限公司 | Power consumption reduction method, device and system based on dehumidifier special for museum |
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