CN114237128A - Hydropower station equipment real-time monitoring data monitoring system and monitoring method based on trend alarm - Google Patents
Hydropower station equipment real-time monitoring data monitoring system and monitoring method based on trend alarm Download PDFInfo
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Abstract
The invention provides a trend alarm-based hydropower station equipment real-time monitoring data monitoring system and a trend alarm-based hydropower station equipment real-time monitoring data monitoring method, and belongs to the technical field of hydropower station monitoring. The method comprises the following steps: respectively acquiring real-time monitoring data and historical data; constructing a trend alarm model, and obtaining an alarm threshold value according to the trend alarm model; inputting the real-time monitoring data into the trend alarm model to obtain a real-time output value; and judging whether the real-time output value is greater than an alarm threshold value, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value to complete the monitoring of the real-time monitoring data of the hydropower station equipment. The invention can effectively extract the early warning, evaluation and monitoring of the hydropower equipment state and decision support information from the trend, improves the accuracy and reliability of real-time monitoring of the hydropower station and saves the technical effect of labor cost.
Description
Technical Field
The invention belongs to the technical field of hydropower station monitoring, and particularly relates to a hydropower station equipment real-time monitoring data monitoring system and method based on trend alarm.
Background
Hydropower station equipment occupies an important position in modern industrial development, and the guarantee of safe and stable operation of mechanical equipment is the basis for safe production and industrial development guarantee. Sensors for monitoring the state of equipment in real time are installed in existing hydropower station equipment, so that the equipment can be monitored and checked in real time when a fault occurs. However, the method cannot predict the faults in advance, so that a method capable of predicting the faults in advance is urgently needed, and if the fault trend is predicted according to historical data, effective trend extraction can provide early warning for monitoring hydroelectric equipment, and estimation of the state of the monitoring hydroelectric equipment and decision support information.
Disclosure of Invention
In order to overcome the defects in the prior art, the hydropower station equipment real-time monitoring data monitoring system and method based on trend alarm can effectively extract the trend and provide early warning, evaluation and monitoring of hydropower equipment state and decision support information for the hydropower station equipment.
In order to achieve the above purpose, the invention adopts the technical scheme that:
this scheme provides a power station equipment real time monitoring data monitoring system based on trend is reported to police, includes:
the data acquisition module is used for respectively acquiring real-time monitoring data of the current running state of the hydropower station equipment and historical data in a preset time period;
the alarm threshold calculation module is used for preprocessing the historical data, constructing a trend alarm model by utilizing the preprocessed historical data and obtaining an alarm threshold according to the trend alarm model;
the real-time output value calculation module is used for inputting the real-time monitoring data to the trend alarm model to obtain a real-time output value;
and the judging module is used for judging whether the real-time output value is greater than an alarm threshold value or not, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value so as to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
The invention provides a hydropower station equipment real-time monitoring data monitoring method based on trend alarm, which comprises the following steps:
s1, respectively acquiring real-time monitoring data of the current running state of the hydropower station equipment and historical data in a preset time period;
s2, preprocessing the historical data, constructing a trend alarm model by using the preprocessed historical data, and obtaining an alarm threshold value according to the trend alarm model;
s3, inputting the real-time monitoring data to the trend alarm model to obtain a real-time output value;
and S4, judging whether the real-time output value is greater than an alarm threshold value, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
Further, the step S2 includes the steps of:
s201, preprocessing the historical data;
s202, cutting the preprocessed historical data at preset time intervals;
s203, extracting the characteristics of each cut historical data;
s204, determining the weight corresponding to the extracted feature by using a preset convolutional neural network model;
s205, adjusting parameters of the convolutional neural network model by using the weights, and constructing an initial trend alarm model;
s206, extracting an alarm data set from the historical data;
s207, training the initial trend alarm model by using a target error according to the alarm data set to obtain a trend alarm model;
and S208, inputting the preprocessed historical data into the trend alarm model to obtain an alarm threshold value.
Still further, the preprocessing in step S201 includes: and filtering invalid data in the historical data and denoising the historical data.
Still further, the expression of the target error in step S207 is as follows:
where L represents the target error, y represents the alarm data set, and y' represents the prediction data.
Still further, the expression of the alarm threshold in step S208 is as follows:
J＝ads(Yi-tn)
Yi＝[Y*(Ymax-Ymin)+Ymax+Ymin]/2
Xi＝(2*X-Xmax-Xmin)/(Xmax+Xmin)
wherein J represents an alarm threshold, YiRepresenting the normalized alarm data set, tnRepresenting normalized history data, Y representing normalized alarm evaluation value, YmaxRepresents the maximum value in the history, YminRepresenting the minimum value in the history, b1And b2Threshold vector, w, representing the mean trend alarm model1And w2All represent the weight, X, of the trend alarm modeliRepresenting the normalized real-time monitoring data, X representing a vector matrix of the real-time monitoring data, XmaxMaximum value, X, representing real-time monitoring dataminRepresenting the minimum value of the real-time monitoring data.
The invention has the beneficial effects that:
(1) according to the invention, the neural network model is constructed through the historical operation data of the hydropower station, and the real-time monitoring data of the hydropower station operation is early warned by using the neural network model, so that the early warning of the monitoring hydropower equipment, the state evaluation of the monitoring hydropower equipment and the decision support information can be effectively extracted from the trend, the accuracy and the reliability of the real-time monitoring of the hydropower station are improved, and the technical effect of saving the labor cost is achieved.
(2) The invention constructs a trend alarm model by combining historical data of the hydropower station and trains the model by utilizing the alarm data set, thereby effectively improving the precision of the model and further improving the monitoring accuracy of the hydropower station.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
Example 1
As shown in fig. 1, the present invention provides a trend alarm based hydropower station equipment real-time monitoring data monitoring system, which includes:
the data acquisition module is used for respectively acquiring real-time monitoring data of the current running state of the hydropower station equipment and historical data in a preset time period;
the alarm threshold calculation module is used for preprocessing the historical data, constructing a trend alarm model by utilizing the preprocessed historical data and obtaining an alarm threshold according to the trend alarm model;
the real-time output value calculation module is used for inputting the real-time monitoring data to the trend alarm model to obtain a real-time output value;
and the judging module is used for judging whether the real-time output value is greater than an alarm threshold value or not, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value so as to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
The working principle of the invention is as follows: firstly, acquiring real-time monitoring data and historical data of the current running state of hydropower station equipment, preprocessing the historical data, cutting the preprocessed historical data at preset time intervals, and extracting the characteristics of each cut historical data; determining the weight corresponding to the extracted features by using a preset convolutional neural network model; adjusting parameters of the convolutional neural network model by using the weights to construct an initial trend alarm model; extracting an alarm data set from historical data; training the initial trend alarm model by using a target error according to the alarm data set to obtain a trend alarm model; inputting the preprocessed historical data into the trend alarm model to obtain an alarm threshold value; inputting the real-time monitoring data into the trend alarm model to obtain a real-time output value; and judging whether the real-time output value is greater than an alarm threshold value, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
In the embodiment of the invention, the functional units can be divided according to the trend alarm-based hydropower station equipment real-time monitoring data monitoring system, for example, each function can be divided into each functional unit, and two or more functions can be integrated into one processing unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software functional unit. It should be noted that the division of the cells in the present invention is schematic, and is only a logical division, and there may be another division manner in actual implementation.
In the embodiment of the invention, the trend alarm-based hydropower station equipment real-time monitoring data monitoring system comprises a hardware structure and/or a software module which are corresponding to each function. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware and/or combinations of hardware and computer software, where a function is performed in a hardware or computer software-driven manner, and that the function described may be implemented in any suitable manner for each particular application depending upon the particular application and design constraints imposed on the technology, but such implementation is not to be considered as beyond the scope of the present application.
The invention has the beneficial effects that: according to the invention, the neural network model is constructed through the historical operation data of the hydropower station, and the real-time monitoring data of the hydropower station operation is early warned by using the neural network model, so that the early warning of the monitoring hydropower equipment, the state evaluation of the monitoring hydropower equipment and the decision support information can be effectively extracted from the trend, the accuracy and the reliability of the real-time monitoring of the hydropower station are improved, and the technical effect of saving the labor cost is achieved.
Example 2
As shown in fig. 2, the invention provides a hydropower station equipment real-time monitoring data monitoring method based on trend alarm, which is implemented as follows:
s1, respectively acquiring real-time monitoring data of the current running state of the hydropower station equipment and historical data in a preset time period;
s2, preprocessing the historical data, constructing a trend alarm model by using the preprocessed historical data, and obtaining an alarm threshold value according to the trend alarm model, wherein the method comprises the following steps:
s201, preprocessing the historical data;
in the embodiment of the invention, the pretreatment comprises the following steps: and filtering invalid data in the historical data and denoising the historical data.
In the embodiment of the invention, invalid points, abnormal points and illegal points in historical data and/or real-time parameters are filtered according to the preset filtering rule, and the adjustable range of the period can be freely adjusted. The filtering rules include invalid point filtering rules, abnormal point filtering rules and illegal point filtering rules.
S202, cutting the preprocessed historical data at preset time intervals;
s203, extracting the characteristics of each cut historical data;
s204, determining the weight corresponding to the extracted feature by using a preset convolutional neural network model;
in the embodiment of the invention, the weight corresponding to each cut historical data represents the trend corresponding to each cut historical data.
S205, adjusting parameters of the convolutional neural network model by using the weights, and constructing an initial trend alarm model;
s206, extracting an alarm data set from the historical data;
s207, training the initial trend alarm model by using a target error according to the alarm data set to obtain a trend alarm model;
in the embodiment of the present invention, the parameters of the trend alarm model, such as the number of channels and the number of convolution kernels, are not limited in this embodiment.
For example, the trend alarm model may include an input layer, and the input layer may be configured to receive historical data, and may include a feature extraction layer configured to perform feature extraction on the historical data transmitted by the input layer.
In the embodiment of the invention, the expression of the target error is as follows:
wherein L represents a target error, y represents an alarm data set, and y' represents predicted data;
and S208, inputting the preprocessed historical data into the trend alarm model to obtain an alarm threshold value.
In the embodiment of the present invention, J ═ ads (Y)i-tn)
Yi＝[Y*(Ymax-Ymin)+Ymax+Ymin]/2
Xi＝(2*X-Xmax-Xmin)/(Xmax+Xmin)
Wherein J represents an alarm threshold, YiRepresenting the normalized alarm data set, tnRepresenting normalized history data, Y representing normalized alarm evaluation value, YmaxRepresents the maximum value in the history, YminRepresenting the minimum value in the history, b1And b2Threshold vector, w, representing the mean trend alarm model1And w2All represent the weight, X, of the trend alarm modeliRepresenting the normalized real-time monitoring data, X representing a vector matrix of the real-time monitoring data, XmaxMaximum value, X, representing real-time monitoring dataminRepresenting the minimum value of the real-time monitoring data.
S3, inputting the real-time monitoring data to the trend alarm model to obtain a real-time output value;
and S4, judging whether the real-time output value is greater than an alarm threshold value, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
The invention has the beneficial effects that: according to the invention, the neural network model is constructed through the historical operation data of the hydropower station, and the real-time monitoring data of the hydropower station operation is early warned by using the neural network model, so that the early warning of the monitoring hydropower equipment, the state evaluation of the monitoring hydropower equipment and the decision support information can be effectively extracted from the trend, the accuracy and the reliability of the real-time monitoring of the hydropower station are improved, and the technical effect of saving the labor cost is achieved.
Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the invention being indicated by the following claims. It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (6)
1. A hydropower station equipment real-time monitoring data monitoring system based on trend alarming is characterized by comprising:
the data acquisition module is used for respectively acquiring real-time monitoring data of the current running state of the hydropower station equipment and historical data in a preset time period;
the alarm threshold calculation module is used for preprocessing the historical data, constructing a trend alarm model by utilizing the preprocessed historical data and obtaining an alarm threshold according to the trend alarm model;
the real-time output value calculation module is used for inputting the real-time monitoring data to the trend alarm model to obtain a real-time output value;
and the judging module is used for judging whether the real-time output value is greater than an alarm threshold value or not, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value so as to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
2. A hydropower station equipment real-time monitoring data monitoring method based on trend alarming is characterized by comprising the following steps:
s1, respectively acquiring real-time monitoring data of the current running state of the hydropower station equipment and historical data in a preset time period;
s2, preprocessing the historical data, constructing a trend alarm model by using the preprocessed historical data, and obtaining an alarm threshold value according to the trend alarm model;
s3, inputting the real-time monitoring data to the trend alarm model to obtain a real-time output value;
and S4, judging whether the real-time output value is greater than an alarm threshold value, and outputting an alarm signal when the real-time output value is greater than the alarm threshold value to complete the monitoring of the real-time monitoring data of the hydropower station equipment.
3. The trend alarm based hydropower station device real-time monitoring data monitoring method according to claim 2, wherein the step S2 comprises the steps of:
s201, preprocessing the historical data;
s202, cutting the preprocessed historical data at preset time intervals;
s203, extracting the characteristics of each cut historical data;
s204, determining the weight corresponding to the extracted feature by using a preset convolutional neural network model;
s205, adjusting parameters of the convolutional neural network model by using the weights, and constructing an initial trend alarm model;
s206, extracting an alarm data set from the historical data;
s207, training the initial trend alarm model by using a target error according to the alarm data set to obtain a trend alarm model;
and S208, inputting the preprocessed historical data into the trend alarm model to obtain an alarm threshold value.
4. The trend alarm based hydropower station device real-time monitoring data monitoring method according to claim 3, wherein the preprocessing in the step S201 comprises: and filtering invalid data in the historical data and denoising the historical data.
5. The method for monitoring the real-time monitoring data of the hydropower station equipment with the basic trend alarm as claimed in claim 3, wherein the expression of the target error in the step S207 is as follows:
where L represents the target error, y represents the alarm data set, and y' represents the prediction data.
6. The method for monitoring the real-time monitoring data of the hydropower station equipment with the basic trend alarm as claimed in claim 3, wherein the expression of the alarm threshold value in the step S208 is as follows:
J＝ads(Yi-tn)
Yi＝[Y*(Ymax-Ymin)+Ymax+Ymin]/2
Xi＝(2*X-Xmax-Xmin)/(Xmax+Xmin)
wherein J represents an alarm threshold, YiRepresenting the normalized alarm data set, tnRepresenting normalized history data, Y representing normalized alarm evaluation value, YmaxRepresents the maximum value in the history, YminRepresenting the minimum value in the history, b1And b2Threshold vector, w, representing the mean trend alarm model1And w2All represent the weight, X, of the trend alarm modeliRepresenting the normalized real-time monitoring data, X representing a vector matrix of the real-time monitoring data, XmaxMaximum value, X, representing real-time monitoring dataminRepresenting the minimum value of the real-time monitoring data.
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Application publication date: 20220325 |