CN113554526A - Fault early warning method and device for power equipment, storage medium and processor - Google Patents
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Abstract
The application discloses a fault early warning method and device for power equipment, a storage medium and a processor. The method comprises the following steps: acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: working data of a plurality of working parameters; generating a real-time working curve chart of the power equipment based on real-time data in a preset working time period; comparing a real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state; and generating early warning information according to the difference value change and the set early warning threshold value. Through the method and the device, the problem that fault identification and alarm cannot be realized when the fault type difference of the power equipment is large in the related technology is solved.
Description
Technical Field
The application relates to the technical field of power equipment fault early warning, in particular to a fault early warning method and device of power equipment, a storage medium and a processor.
Background
For example, for a transformer, most transformer faults occur due to long-term accumulation, so that the fault occurrence probability is high during the operation of the transformer, and therefore, timely and accurate discovery of potential operation hidden dangers of the transformer is of great significance for maintaining stable operation of the system.
In order to ensure the normal operation of the substation equipment, when fault diagnosis and analysis are required during the operation of the substation equipment, a large amount of monitoring equipment can acquire data in real time during the operation of the substation, compare the collected data with the previously stored historical data, and then make a rough judgment on the operation state. Specifically, a model analysis method can be adopted for implementation, but a traditional model based on theoretical analysis in the related technology is difficult to process multidimensional mass data set information, cannot timely display potential fault information existing in equipment, cannot accurately identify faults when the difference of the fault types of the power equipment is large, and cannot timely perform early warning.
Aiming at the problem that the fault identification and alarm cannot be realized when the fault type difference of the power equipment is large in the related technology, an effective solution is not provided at present.
Disclosure of Invention
The application provides a fault early warning method and device for power equipment, a storage medium and a processor, which are used for solving the problem that fault identification and warning cannot be realized when the fault type difference of the power equipment is large in the related technology.
According to one aspect of the application, a fault early warning method for power equipment is provided. The method comprises the following steps: acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: working data of a plurality of working parameters; generating a real-time working curve chart of the power equipment based on real-time data in a preset working time period; comparing a real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state; and generating early warning information according to the difference value change and the set early warning threshold value.
Optionally, before comparing the real-time operating profile of the power equipment with the standard profile, the method further includes: constructing a fault database of the power equipment, wherein the fault database records working sample data of the power equipment in an abnormal working state within a historical time period; extracting fault data under different working parameters from a fault database; based on fault data for different operating parameters, a standard graph of the electrical equipment over a historical period of time is generated.
Optionally, before building the fault database of the power equipment, the method further comprises: acquiring an infrared image of the power equipment, and performing image preprocessing on the infrared image; identifying the infrared image based on a convolutional neural network to obtain power equipment in the infrared image; adopting an infrared image to carry out image registration, and determining whether the power equipment is in a fault state through a depth confidence network; and if the power equipment is determined to be in the fault state, identifying the fault of the power equipment by using the measured data.
Optionally, constructing a fault database of the power equipment includes: acquiring historical fault data of the power equipment and preprocessing the historical fault data; performing secondary processing on the acquired historical fault data by using an offline model; and performing fault prediction and assignment marking on the secondarily processed historical fault data to construct a fault database of the power equipment.
Optionally, generating the warning information according to the difference change and the set warning threshold, including: initializing a weight matrix and a bias matrix which are formed by difference value changes, wherein the weight value satisfies normal distribution with the mean value of 0 and the variance of 0.1, and the bias value is defined as 0.1; extracting data characteristics in a fault database by adopting a multilayer convolutional neural network and a Bayesian principle; calculating a parameter error value of each data feature based on the weight matrix and the bias matrix; and correcting the parameter error value based on the set early warning threshold value to generate early warning information.
Optionally, calculating a parameter error value of each data feature based on the weight matrix and the bias matrix includes: the multilayer convolutional neural network extracts data features by utilizing convolutional layers and averagely extracts each convolutional feature by combining with a pooling layer; reducing the dimension of the extracted data features; selecting a multi-objective optimization algorithm as a minimum loss objective function, and performing iterative training; and reversely deducing the residual error to each parameter through a back propagation algorithm to obtain a parameter error value of the data characteristic.
Optionally, generating the warning information according to the difference change and the set warning threshold, including: if the difference value change is smaller than a set early warning threshold value, the power equipment is in a normal state; if the difference value change is larger than or equal to the set early warning threshold value, the equipment is in a fault state, and early warning information is output.
According to another aspect of the present application, a fault early warning apparatus of an electrical device is provided. The device includes: the first acquisition module is used for acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: working data of a plurality of working parameters; the second generation module is used for generating a real-time working curve chart of the power equipment based on real-time data in a preset working time period; the comparison module is used for comparing a real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state; and the early warning module is used for generating early warning information according to the difference value change and the set early warning threshold value.
According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium, which includes a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute a fault warning method for an electric power device when the program is running.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory stores computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute a fault early warning method of the power equipment when running.
Through the application, the following steps are adopted: acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: working data of a plurality of working parameters; generating a real-time working curve chart of the power equipment based on real-time data in a preset working time period; comparing a real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state; according to the difference value change and the set early warning threshold value, the early warning information is generated, and the problem that the fault identification and warning cannot be realized when the fault type difference of the power equipment is large in the related technology is solved. And then reached the effect that promotes the precision of power equipment trouble early warning.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a fault warning method for an electrical device according to an embodiment of the present disclosure;
fig. 2 is a deep confidence network diagram in a fault early warning method for an electrical device according to an embodiment of the present application;
fig. 3 is a flowchart of an alternative fault warning method for an electrical device according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of a classification result of image features of a substation device in a fault early warning method for a power device according to an embodiment of the present application;
fig. 5 is a schematic diagram illustrating a casing test result in a normal state in a fault early warning method for an electrical device according to an embodiment of the present application;
fig. 6 is a schematic diagram illustrating a casing test result in an abnormal state in a fault early warning method for an electrical device according to an embodiment of the present application;
fig. 7 is a schematic diagram of a fault warning device of an electrical device according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to the embodiment of the application, a fault early warning method of power equipment is provided.
Fig. 1 is a flowchart of a fault warning method for an electrical device according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S102, acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: operational data for a plurality of operational parameters.
Specifically, the power equipment may be substation equipment, and when fault early warning is performed in the operation process of the substation equipment, a large amount of monitoring equipment can acquire data in real time during the operation of the substation, and the operation state is judged based on the collected data equipment.
And step S104, generating a real-time working curve chart of the power equipment based on the real-time data in the preset working time period.
Specifically, a real-time curve of each data type may be generated from real-time data of normal operation of the device.
Optionally, in the fault early warning method for the power equipment provided in the embodiment of the present application, before comparing the real-time working curve of the power equipment with the standard curve, the method further includes: constructing a fault database of the power equipment, wherein the fault database records working sample data of the power equipment in an abnormal working state within a historical time period; extracting fault data under different working parameters from a fault database; based on fault data for different operating parameters, a standard graph of the electrical equipment over a historical period of time is generated.
Specifically, an equipment fault database can be constructed based on the Bayesian principle, and then the database is utilized to generate a standard curve of each data type by combining with the historical data of normal operation of the equipment.
And S106, comparing the real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state.
And step S108, generating early warning information according to the difference value change and the set early warning threshold value.
Specifically, the standard curve and the real-time curve of the same data type are subjected to difference, an early warning result is obtained according to the difference value change and a set early warning threshold value, and early warning is carried out by utilizing the early warning result.
Optionally, in the fault early warning method for the power device provided in the embodiment of the present application, the generating early warning information according to the difference change and the set early warning threshold includes: if the difference value change is smaller than a set early warning threshold value, the power equipment is in a normal state; if the difference value change is larger than or equal to the set early warning threshold value, the equipment is in a fault state, and early warning information is output.
Specifically, if the difference is smaller than the set early warning threshold, the device is in a normal state and cannot give out early warning, and if the difference is larger than or equal to the set early warning threshold, the device can give out early warning to remind a worker to check and analyze the device fault in time.
According to the fault early warning method for the power equipment, the real-time data of the power equipment in the preset working time period are acquired, and the real-time data comprise: working data of a plurality of working parameters; generating a real-time working curve chart of the power equipment based on real-time data in a preset working time period; comparing a real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state; according to the difference value change and the set early warning threshold value, the early warning information is generated, and the problem that the fault identification and warning cannot be realized when the fault type difference of the power equipment is large in the related technology is solved. And then reached the effect that promotes the precision of power equipment trouble early warning.
Optionally, in the fault early warning method for the power device provided in the embodiment of the present application, before constructing the fault database for the power device, the method further includes: acquiring an infrared image of the power equipment, and performing image preprocessing on the infrared image; identifying the infrared image based on a convolutional neural network to obtain power equipment in the infrared image; adopting an infrared image to carry out image registration, and determining whether the power equipment is in a fault state through a depth confidence network; and if the power equipment is determined to be in the fault state, identifying the fault of the power equipment by using the measured data.
For example, taking power equipment as a transformer as an example, potential faults of the transformer can be divided into internal faults and external faults, the external faults of a cabinet mainly include external connection faults of conductors, faults of a cooling device and an oil way system and leakage faults, the reason of poor external connection of the conductors is that the connection point of a transformer box and an external lead is not firm enough, then the resistance is large, and then more heat is emitted, the faults of the cooling device and the oil way system mainly occur in a cooler, an explosion-proof pipe or some external oil way systems, the fault characteristics can be clearly reflected in an infrared image, when the transformer leaks magnetism, overheating and loss can occur, so that the temperature of corresponding screws is abnormally high, and finally the normal working state of the transformer is disturbed.
Therefore, the infrared image of the power equipment is collected firstly, the image preprocessing is carried out on the infrared image, the infrared image is identified based on the convolutional neural network, the equipment in the infrared image is obtained to be combined with the infrared image to carry out image registration, the network is deeply trusted, whether the equipment is in a fault state or not is determined, and the whole fault of the substation equipment is identified by utilizing the actually measured data.
As shown in fig. 2, it should be noted that the Deep Belief Network (DBN) is composed of multiple layers of Restricted Boltzmann Machines (RBMSs), and the RBMs are probabilistic graphical models of stochastic neural networks, and the output of neurons thereof has only two states: active and inactive, and each output state has a determined probability comprising a visible layer and a hidden layer; the last layer of the DBN is logistic regression, and the training process of the network includes two processes: pre-training and fine-tuning.
Specifically, the RBMs are pre-trained layer by layer under the unsupervised condition, and the hidden characteristic information of the data is deeply mined, wherein each training epoch only trains one layer of RBMs; training and stacking the next layer of RBMs, combining the label with the sample for use, and then performing supervised adjustment by adopting back propagation to realize fault classification; updating network parameters of the deep confidence network model by adopting a back propagation strategy, and defining a cost function as follows:
wherein E is the average square error, N is the number of hidden elements,
and XiRespectively representing the output of the output layer and the ideal output, i being the sample index, (W)l，bl) Representing the weights to be learned and the bias parameters at level l.
Further, the weight and the bias parameters are updated by adopting a gradient descent method:
wherein λ is learning efficiency, then partial codes of the DBN model training are as follows:
function DBN＝DBNsetup(DBN,x,opts)。
n ═ size (x, 2); # n is the characteristic dimension of a single sample.
-dbn. sizes ═ n, dbn. sizes ]; sizes are dimensions of RBMs.
for 1: numel (dbn. sizes) -1# numel (dbn. sizes) returns the number of elements in dbn. sizes.
Rbms { u }. alpha ═ ops.alpha; # initializes the learning rate of the RBMs.
DBN.RBMs{u}.momentum＝opts.momentum。
Rbms { u }. W ═ zeros (dbn. sizes (u +1), dbn. sizes (u)); # all initial values are 0.
DBN.RBMs{u}.vW＝zeros(DBN.sizes(u+1)，DBN.sizes(u))。
Rbms { u }. b ═ zeros (dbn. sizes (u), 1); bias values of # display layer were all 0 at the initial value.
Rbms { u }. vb ═ zeros (dbn. sizes (u), 1); # the first RBMs is 100 and the second RBMs is 100.
DBN.RBMs{u}.c＝zeros(DBN.sizes(u+1),1)。
DBN.RBMs{u}.vc＝zeros(DBN.sizes(u+1),1)。
Further, by inputting the registered image to the model, outputting the fault type of the equipment by the model, and identifying the fault equipment by adopting the depth confidence network, the errors and the uncertainty caused by manually reading the infrared image can be avoided.
Specifically, for an equipment cabinet, because the internal fault of the cabinet is mainly caused by the defects and defects of internal equipment such as coils, iron cores, lead wires and the like, the current method cannot directly detect the fault inside the cabinet, but different internal fault defects cause different distributions of thermal states on the surface of the cabinet, so that the invention can also roughly analyze the fault type existing in the equipment by analyzing the thermal state distribution diagram displayed outside the cabinet.
And to dry-type transformer, when taking place the short circuit between the iron core or when connecting the iron core in a plurality of positions and a plurality of point department, because the short circuit can produce heat energy, and some heat can directly distribute away, can directly detect through infrared imaging, to wet voltage equipment, because winding and iron core will place the centre at the oil tank, consequently be full of transformer oil around, when inside local fault and production heat take place, because the cooling and the diffusion effect of oil, can not show this trouble on the thermal image usually, and adopt this embodiment, then can carry out accurate fault diagnosis early warning.
Optionally, in the fault early warning method for the power device provided in the embodiment of the present application, constructing a fault database for the power device includes: acquiring historical fault data of the power equipment and preprocessing the historical fault data; performing secondary processing on the acquired historical fault data by using an offline model; and performing fault prediction and assignment marking on the secondarily processed historical fault data to construct a fault database of the power equipment.
Specifically, performing the secondary treatment includes:
wherein A isiTo normalized values，ΔaiThe index mean value is B, and the index variance value is B.
Optionally, in the fault early warning method for the power device provided in the embodiment of the present application, the generating early warning information according to the difference change and the set early warning threshold includes: initializing a weight matrix and a bias matrix which are formed by difference value changes, wherein the weight value satisfies normal distribution with the mean value of 0 and the variance of 0.1, and the bias value is defined as 0.1; extracting data characteristics in a fault database by adopting a multilayer convolutional neural network and a Bayesian principle; calculating a parameter error value of each data feature based on the weight matrix and the bias matrix; and correcting the parameter error value based on the set early warning threshold value to generate early warning information.
Specifically, a weight matrix and a bias matrix are initialized, normal distribution with a mean value of 0 and a variance of 0.1 is defined for the weight, and all bias values are defined as 0.1; extracting data characteristics in the database based on a multilayer convolutional neural network and the Bayesian principle, solving each data characteristic parameter error, and correcting the error to obtain a correction result, namely the early warning result.
Optionally, in the fault early warning method for the power device provided in the embodiment of the present application, calculating a parameter error value of each data feature based on the weight matrix and the bias matrix includes: the multilayer convolutional neural network extracts data features by utilizing convolutional layers and averagely extracts each convolutional feature by combining with a pooling layer; reducing the dimension of the extracted data features; selecting a multi-objective optimization algorithm as a minimum loss objective function, and performing iterative training; and reversely deducing the residual error to each parameter through a back propagation algorithm to obtain a parameter error value of the data characteristic.
Specifically, the data of different specifications can be converted into the unified specification by using non-dimensionalization; processing the data and converting the processed data into standard normal distribution; evaluating each feature according to divergence, and setting the threshold to select the feature; calculating the variance of each feature and selecting the feature with the variance larger than the threshold value according to the threshold value.
Fig. 3 is a flowchart of another fault warning method for an electrical device according to an embodiment of the present application. As shown in fig. 3, the apparatus includes:
and constructing a fault database, generating a standard curve, performing difference on the real-time curve and the standard curve to obtain an early warning result, and comparing the early warning result with a set early warning threshold value so as to determine whether to send out early warning according to the comparison result.
In addition, the embodiment also provides a test comparison description of the power equipment fault early warning method based on the image, and in order to better verify and describe the technical effect adopted in the method, the embodiment respectively selects the traditional threshold segmentation, region extraction and edge detection method and adopts the method to perform comparison test, and compares the test results by means of scientific demonstration to verify the real effect of the method.
In order to verify that the method has higher identification precision and segmentation quality compared with the traditional threshold segmentation, region extraction and edge detection methods, the traditional method and the method of the invention are adopted to respectively identify and compare the acquired infrared images of the equipment in the embodiment.
And (3) testing environment: (1) each type of power equipment is represented in one category, while the faulty components are labeled in another category, respectively.
(2) Respectively using Threshold Segmentation (TS), Area Extraction (AE), Edge Detection (ED) and the method to compare Pixel Accuracy (PA), Mean Pixel Accuracy (MPA), Mean Intersection over unit (MIoU) and Weighted Intersection over unit (FWIoU) to measure the processing quality of each method on the image, wherein the calculation formulas are as follows:
where K is the number of predictions, pijIs the number of pixels belonging to class i, but predicted to be class j, pjiIs the number of pixels belonging to class j, but predicted as class i, piiIs the number of pixels belonging to the category i and is predicted as the number of pixels of the category i.
Table 1: the results of the calculation accuracy of the traditional method and the method of the invention are compared in a table.
Referring to table 1, it can be seen that the method of the present invention improves the segmentation quality of PA and MPA by more than 5% compared to the conventional method.
(3) Performance evaluation of fault status classification of devices using recall and accuracy rates:
the recall ratio is as follows: recall TP/(TP + FN)
The precision ratio is as follows: precision TP/(TP + FP)
Wherein, tp (true positive): the correct number of samples determined to be positive, fp (false positive): in the samples judged as positive, the number of errors is judged, tn (true negative): the correct number of samples judged to be negative, fn (false negative): the number of errors is determined for the samples determined to be negative.
The extracted hidden information is used as input to the DBN Network and 9 types of devices are used as output, as shown in the following tables (DBN, SVM (support vector machine), and BPNN (Back Propagation Neural Network)):
table 2: and comparing the classification performance of different types of substation equipment.
As can be seen from table 2, the method of the present invention has a high accuracy in transformer fault classification, and performs best in recall rate and accuracy, and the classification effect of BPNN is the worst.
In order to better verify the effectiveness of the method for early warning of the equipment fault, the whole process of the transformer substation equipment fault analysis based on the infrared image is tested; firstly, performing image recognition on an input RGB image, secondly, using an infrared image for image registration, and thirdly, using a trained deep confidence network for determining the fault type of the temperature change of the combined equipment.
As shown in fig. 4, 5, and 6, which are actual result diagrams of the method for identifying the equipment fault, it can be seen from the diagrams that the method of the present invention can accurately extract the target of the substation equipment existing in the diagrams, and the image registration accuracy is higher by combining the infrared image.
As shown in fig. 5, in the extracted device, the detected minimum temperature is 39.02 ℃, the detected maximum temperature is 39.28 ℃, the difference between the two temperatures is less than 1%, and the device can be judged to be in a normal state currently according to the analysis result of the deep belief network; as shown in fig. 6, in the extracted devices, the lowest temperature is found to be 39.42 ℃, the highest temperature is found to be 41.68 ℃, the difference between the two temperatures exceeds 5%, and meanwhile, the result output by the deep confidence network model also determines that the devices are in an abnormal operation mode, and at this time, an early warning signal needs to be sent out in time to inform the staff of further device operation state investigation.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The embodiment of the present application further provides a fault early warning device for an electrical device, and it should be noted that the fault early warning device for an electrical device according to the embodiment of the present application may be used to execute the fault early warning method for an electrical device according to the embodiment of the present application. The fault early warning device for the power equipment provided by the embodiment of the application is introduced below.
Fig. 7 is a schematic diagram of a fault warning device of an electrical device according to an embodiment of the present application. As shown in fig. 7, the apparatus includes: the system comprises a first acquisition module 10, a second generation module 20, a comparison module 30 and an early warning module 40.
Specifically, the first obtaining module 10 is configured to obtain real-time data of the power device in a predetermined operating time period, where the real-time data includes: operational data for a plurality of operational parameters.
And the second generating module 20 is configured to generate a real-time operation graph of the power equipment based on the real-time data in the predetermined operation time period.
The comparison module 30 is configured to compare the real-time working curve graph of the power device with a standard curve graph, and obtain a difference change, where the standard curve graph is used to represent a working trend graph of the power device in a fault working state.
And the early warning module 40 is used for generating early warning information according to the difference value change and the set early warning threshold value.
The fault early warning device of the power equipment provided by the embodiment of the application acquires real-time data of the power equipment in a preset working time period through the first acquisition module 10, wherein the real-time data comprises: working data of a plurality of working parameters; the second generation module 20 generates a real-time working curve chart of the power equipment based on real-time data in a preset working time period; the comparison module 30 compares the real-time working curve graph of the power equipment with a standard curve graph to obtain the variation of the difference, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state; the early warning module 40 generates early warning information according to the difference change and the set early warning threshold, so that the problem that in the related technology, the identification and warning of faults cannot be realized when the difference of the fault types of the power equipment is large is solved, and the effect of improving the precision of the early warning of the faults of the power equipment is achieved.
Optionally, in the fault early warning apparatus for electrical equipment provided in an embodiment of the present application, the apparatus further includes: the system comprises a construction module, a fault database and a fault analysis module, wherein the construction module is used for constructing a fault database of the power equipment, and the fault database records working sample data of the power equipment in an abnormal working state within a historical time period; the extraction module is used for extracting fault data under different working parameters from a fault database; and the second generation module 20 is used for generating a standard curve graph of the power equipment in a historical time period based on the fault data of different working parameters.
Optionally, in the fault early warning apparatus for electrical equipment provided in an embodiment of the present application, the apparatus further includes: the acquisition module is used for acquiring an infrared image of the power equipment; the preprocessing module is used for preprocessing the infrared image; the second acquisition module is used for identifying the infrared image based on the convolutional neural network and acquiring the power equipment in the infrared image; the determining module is used for carrying out image registration by adopting the infrared image and determining whether the power equipment is in a fault state or not through the depth confidence network; and the identification module is used for identifying the fault of the power equipment by utilizing the measured data if the power equipment is determined to be in the fault state.
Optionally, in the fault early warning apparatus for an electrical device provided in an embodiment of the present application, the building module includes: the acquisition module is used for acquiring and preprocessing historical fault data of the power equipment; the processing module is used for carrying out secondary processing on the collected historical fault data by utilizing the offline model; and the creating module is used for performing fault prediction and assignment marking on the secondarily processed historical fault data and constructing a fault database of the power equipment.
Optionally, in the fault early warning apparatus for an electrical device provided in the embodiment of the present application, the early warning module 40 includes: the initialization module is used for initializing a weight matrix and a bias matrix which are formed by difference value changes, wherein the weight value meets the normal distribution that the mean value is 0 and the variance is 0.1, and the bias value is defined as 0.1; the extraction module is used for extracting data characteristics in the fault database by adopting a multilayer convolutional neural network and a Bayesian principle; the calculation module is used for calculating a parameter error value of each data characteristic based on the weight matrix and the bias matrix; and the correction module is used for correcting the parameter error value based on the set early warning threshold value and generating early warning information.
Optionally, in the fault early warning apparatus for an electrical device provided in an embodiment of the present application, the initialization module includes: the extraction module is used for extracting data features by utilizing the convolutional layer through the multilayer convolutional neural network and performing average extraction on each convolutional feature by combining with the pooling layer; the reduction processing module is used for reducing the dimension of the extracted data features; the iterative training module is used for selecting a multi-objective optimization algorithm as a minimum loss objective function to carry out iterative training; and the sub-acquisition module is used for reversely deducing the residual error to each parameter through a back propagation algorithm to acquire a parameter error value of the data characteristic.
Optionally, in the fault early warning apparatus for electrical equipment provided in an embodiment of the present application, the apparatus further includes: the first output module is used for enabling the power equipment to be in a normal state if the difference value change is smaller than a set early warning threshold value; and the second output module is used for outputting the early warning information if the difference value change is larger than or equal to the set early warning threshold value, wherein the equipment is in a fault state.
The fault early warning device of the power equipment comprises a processor and a memory, wherein the first acquisition module 10, the second generation module 20, the comparison module 30, the early warning module 40 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem that in the related technology, when the difference of the fault types of the power equipment is large, the fault identification and the alarm cannot be realized is solved by adjusting the kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
The embodiment of the application also provides a nonvolatile storage medium, wherein the nonvolatile storage medium comprises a stored program, and the program controls the equipment where the nonvolatile storage medium is located to execute a fault early warning method of the power equipment during running.
The embodiment of the application also provides an electronic device, which comprises a processor and a memory; the memory stores computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute a fault early warning method of the power equipment when running. The electronic device herein may be a server, a PC, a PAD, a mobile phone, etc.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A fault early warning method for power equipment is characterized by comprising the following steps:
acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: working data of a plurality of working parameters;
generating a real-time working curve graph of the power equipment based on the real-time data in the preset working time period;
comparing the real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state;
and generating early warning information according to the difference value change and a set early warning threshold value.
2. The method of claim 1, wherein prior to comparing the real-time operating profile of the power equipment to a standard profile, the method further comprises:
constructing a fault database of the electric power equipment, wherein the fault database records working sample data of the electric power equipment in an abnormal working state within a historical time period;
extracting fault data under different working parameters from the fault database;
generating a standard graph of the electrical equipment over a historical period of time based on the fault data for the different operating parameters.
3. The method of claim 1, wherein prior to building the fault database for the electrical device, the method further comprises:
acquiring an infrared image of the power equipment, and performing image preprocessing on the infrared image;
identifying the infrared image based on a convolutional neural network to obtain power equipment in the infrared image;
adopting the infrared image to carry out image registration, and determining whether the power equipment is in a fault state through a depth confidence network;
and if the power equipment is determined to be in the fault state, identifying the fault of the power equipment by using the measured data.
4. The method of claim 3, wherein building a fault database for the electrical device comprises:
acquiring historical fault data of the power equipment and preprocessing the historical fault data;
performing secondary processing on the acquired historical fault data by using an offline model;
and performing fault prediction and assignment marking on the historical fault data subjected to secondary processing to construct a fault database of the power equipment.
5. The method according to any one of claims 2 to 4, wherein generating early warning information according to the difference change and a set early warning threshold comprises:
initializing a weight matrix and a bias matrix which are formed by the difference value change, wherein the weight value satisfies normal distribution with the mean value of 0 and the variance of 0.1, and the bias value is defined as 0.1;
extracting data characteristics in the fault database by adopting a multilayer convolutional neural network and a Bayesian principle;
calculating a parameter error value of each of the data features based on the weight matrix and a bias matrix;
and correcting the parameter error value based on the set early warning threshold value to generate the early warning information.
6. The method of claim 5, wherein calculating a parameter error value for each of the data features based on the weight matrix and a bias matrix comprises:
the multilayer convolutional neural network extracts the data features by utilizing convolutional layers and performs average extraction on each convolutional feature by combining pooling layers;
reducing the dimension of extracting the data features;
selecting a multi-objective optimization algorithm as a minimum loss objective function, and performing iterative training;
and reversely deducing the residual error to each parameter through a back propagation algorithm to obtain the parameter error value of the data characteristic.
7. The method of claim 1, wherein generating early warning information according to the difference change and a set early warning threshold comprises:
if the difference value change is smaller than the set early warning threshold value, the power equipment is in a normal state;
if the difference value change is larger than or equal to the set early warning threshold value, the equipment is in a fault state, and the early warning information is output.
8. A fault early warning device of power equipment is characterized by comprising:
the first acquisition module is used for acquiring real-time data of the power equipment in a preset working time period, wherein the real-time data comprises: working data of a plurality of working parameters;
the second generation module is used for generating a real-time working curve graph of the power equipment based on the real-time data in the preset working time period;
the comparison module is used for comparing a real-time working curve graph of the power equipment with a standard curve graph to obtain difference value change, wherein the standard curve graph is used for representing a working trend graph of the power equipment in a fault working state;
and the early warning module is used for generating early warning information according to the difference value change and a set early warning threshold value.
9. A non-volatile storage medium, comprising a stored program, wherein the program controls a device in which the non-volatile storage medium is located to execute the method for early warning of a failure of a power device according to any one of claims 1 to 7 when the program is executed.
10. An electronic device, comprising a processor and a memory, wherein the memory stores computer readable instructions, and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions are executed to execute the fault pre-warning method for electric power equipment according to any one of claims 1 to 7.
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