Method and device for determining working state of disconnecting switch contact

Abstract

The invention discloses a method and a device for determining the working state of an isolating switch contact. The method comprises the following steps: determining position information and working data of an isolating switch contact in a power system; the position information and the working data are input into a temperature rise prediction model, temperature rise data of the disconnecting switch contact in the future time period are predicted, and the temperature rise prediction model is obtained through training based on historical position information of the disconnecting switch contact, historical working data of the disconnecting switch contact and historical temperature rise data of the disconnecting switch contact; the temperature rise data is used for representing the temperature change state of the disconnecting switch contact; and determining the working state of the disconnecting switch contact based on the temperature rise data. According to the invention, the technical problem of low monitoring efficiency of the disconnecting switch contact is solved.

Description

Technical Field

[0001] The present invention relates to the field of geographic system power system, and in particular to a method and device for determining the working state of a disconnector contact. Background Art

[0002] At present, with the aging of equipment in the power system and the increase in operating load, more and more faults occur in disconnectors and line changers. For example, the disconnectors have been in disrepair and aging for a long time, resulting in the loosening and deformation of the clamping springs of the disconnector contacts and insufficient clamping force. Due to the above abnormalities, the contact surface of the disconnector contacts is abnormal, which leads to abnormal heating of the disconnector contacts.

[0003] In the related art, the monitoring of the heating condition of the isolating switch contacts is usually carried out by combining three methods: general temperature measurement, key temperature measurement and suspected temperature measurement. However, the above method has the technical problem of low monitoring efficiency of the isolating switch contacts.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for determining the working state of an isolating switch contact, so as to at least solve the technical problem of low monitoring efficiency of the isolating switch contact.

[0006] According to one aspect of an embodiment of the present invention, a method for determining the working state of a transformer is provided, and the method may include: determining position information of disconnector contacts and working data of disconnector contacts in an electric power system; inputting the position information and working data into a temperature rise prediction model, and predicting the temperature rise data of the disconnector contacts in a future time period, wherein the temperature rise prediction model is trained based on historical position information of the disconnector contacts, historical working data of the disconnector contacts, and historical temperature rise data of the disconnector contacts, and the temperature rise data is used to represent the temperature change state of the disconnector contacts; based on the temperature rise data, determining the working state of the disconnector contacts.

[0007] Optionally, determining the position information and operating data of the isolating switch contacts in the power system includes: using an infrared sensor to monitor the position information and operating data, wherein the distance between the infrared device and the isolating switch contacts meets a distance threshold.

[0008] Optionally, using an infrared sensor to monitor the position information and working data includes: using an infrared probe in the infrared sensor to monitor the position information; using an infrared temperature sensor in the infrared sensor to monitor the working data.

[0009] Optionally, the position information and working data are input into the temperature rise prediction model to predict the temperature rise data of the disconnector contacts in a future period, including: filtering abnormal data in the working data based on the standard working data of the disconnector contacts; inputting the position information and the filtered working data into the temperature rise prediction model to predict the temperature rise data.

[0010] Optionally, the method may further include: constructing a plurality of neurons of a temperature rise prediction model; and training the temperature rise prediction model based on historical position information, historical working data, historical temperature rise data and the plurality of neurons.

[0011] Optionally, based on historical position information, historical working data, historical temperature rise data and multiple neurons, a temperature rise prediction model is trained, including: predicting historical position information and historical working data based on multiple neurons to obtain historical predicted temperature rise data; determining the mean square error between the historical predicted temperature rise data and the expected temperature rise data; based on the mean square error, determining the error signal of the neuron; using the error signal, historical predicted temperature difference data and historical temperature difference data, modifying the initial model parameters of the temperature rise prediction model to obtain the temperature rise prediction model.

[0012] Optionally, based on the mean square error, determining the error signal of the neuron includes: back-propagating the mean square error; distributing the propagated mean square error to multiple neurons; and determining the mean square error distributed to the multiple neurons as the error signal.

[0013] Optionally, determining the working state of the disconnector contact based on the temperature rise data includes: in response to the temperature rise data not meeting the expected temperature rise data, determining that the working state is an abnormal working state.

[0014] Optionally, the method may further include: in response to the working state being an abnormal working state, outputting prompt information.

[0015] According to another aspect of an embodiment of the present invention, a device for determining the working state of a transformer is also provided, and the device may include: a first determination unit, used to determine the position information of the isolating switch contacts and the working data of the isolating switch contacts in the power system; a prediction unit, used to input the position information and the working data into a temperature rise prediction model, and predict the temperature rise data of the isolating switch contacts in a future time period, wherein the temperature rise prediction model is trained based on the historical position information of the isolating switch contacts, the historical working data of the isolating switch contacts, and the historical temperature rise data of the isolating switch contacts, and the temperature rise data is used to represent the temperature change state of the isolating switch contacts; a second determination unit, used to determine the working state of the isolating switch contacts based on the temperature rise data.

[0016] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is further provided, which includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for determining the working state of the transformer of an embodiment of the present invention.

[0017] According to another aspect of an embodiment of the present invention, a processor is further provided. The processor is used to run a program, wherein when the program is run by the processor, the method for determining the working state of the transformer according to an embodiment of the present invention is executed.

[0018] According to another aspect of an embodiment of the present invention, a program product is further provided. The program product includes computer instructions. When the computer instructions are executed by a processor, the method for determining the working state of the transformer according to an embodiment of the present invention is implemented.

[0019] In an embodiment of the present invention, the position information of the isolating switch contacts and the working data of the isolating switch contacts in the power system are determined; the position information and the working data are input into the temperature rise prediction model to predict the temperature rise data of the isolating switch contacts in the future period, wherein the temperature rise prediction model is trained based on the historical position information of the isolating switch contacts, the historical working data of the isolating switch contacts, and the historical temperature rise data of the isolating switch contacts, and the temperature rise data is used to represent the temperature change state of the isolating switch contacts; based on the temperature rise data, the working state of the isolating switch contacts is determined. That is to say, in an embodiment of the present invention, the position information and working data of the isolating switch contacts are processed using the temperature rise prediction model to obtain the temperature rise data, and based on the temperature rise data, the working state of the isolating switch contacts can be determined, thereby achieving the technical effect of improving the monitoring efficiency of the isolating switch contacts and solving the technical problem of low monitoring efficiency of the isolating switch contacts. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0021] Figure 1 is a flow chart of a method for determining a working state of a transformer according to an embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of deploying infrared sensors according to an embodiment of the present invention;

[0023] Figure 3 is a schematic diagram of a device for determining a working state of a transformer according to an embodiment of the present invention;

[0024] Figure 4is a structural block diagram of a computer terminal according to an embodiment of the present invention;

[0025] Figure 5 It is a block diagram of an electronic device for determining a working state of a transformer according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present invention, an embodiment of a method for determining the working state of a transformer is provided. The steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] In this embodiment, a method for determining the working state of a transformer is proposed. The method can utilize a temperature rise prediction model to process the position information and working data of the disconnector contacts to obtain temperature rise data. Based on the temperature rise data, the working state of the disconnector contacts can be determined, thereby achieving the technical effect of improving the monitoring efficiency of the disconnector contacts and solving the technical problem of low monitoring efficiency of the disconnector contacts.

[0030] Figure 1 FIG. 1 is a flow chart of a method for determining the working state of a transformer according to an embodiment of the present invention. Figure 1 As shown, the method may include the following steps:

[0031] Step S102: determining the position information of the disconnector contacts and the working data of the disconnector contacts in the power system.

[0032] In the technical solution provided in the above step S102 of the present invention, the above-mentioned isolating switch contact can be a knife edge of a knife switch in the power system, that is, it can be a knife edge part in the isolating switch, and can include the contact part of the moving contact and the static contact of the isolating switch (also called a knife switch). The above-mentioned isolating switch can be an electrical device used to open or close a circuit, and can be used to ensure the safe operation of the power system. The above-mentioned position information can be used to determine the position of the isolating switch contact, which can be a three-phase knife edge position. The above-mentioned working data can be used to determine information such as the working condition and working environment of the isolating switch contact, and can be used at least to characterize the working state of the isolating switch contact, for example, it can be data such as knife edge temperature, room temperature, humidity, etc. It should be noted that this is only for example, and there is no specific restriction on the content and type of the position information and working data.

[0033] Optionally, the blade part is the core of the disconnector. When the disconnector is closed, the moving contact (blade head) contacts the static contact (blade holder) to form a circuit conduction; when the knife switch is opened, the moving contact separates from the static contact to disconnect the circuit. In the case of long-term operation and frequent operation, the knife switch blade may be worn, oxidized, and have poor contact, causing the blade to heat up and even cause equipment failure. Therefore, monitoring and maintaining the status of the knife switch blade can effectively ensure the stable operation and safety of the power system.

[0034] For example, in the power system, various monitoring equipment and technologies can be used to determine the position information and working data of the knife edge of the knife switch. The method of obtaining the above position information and working data may include using infrared temperature measurement, visual detection, position sensors and other equipment to collect the above data, and by judging the position information and working data, the potential fault hazards of the disconnector contacts can be prevented and dealt with in a timely manner.

[0035] Step S104, input the position information and working data into the temperature rise prediction model, and predict the temperature rise data of the disconnector contacts in the future time period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contacts, the historical working data of the disconnector contacts, and the historical temperature rise data of the disconnector contacts, and the temperature rise data is used to indicate the temperature change state of the disconnector contacts.

[0036] In the technical solution provided in the above step S104 of the present invention, the above temperature rise prediction model can be a neural network model, a nonlinear uncertainty mathematical model, a multi-layer feedforward artificial neural network with a continuous transfer function, which can be used to determine the temperature change state (i.e., temperature rise) of the disconnector contact within a period of time, and can be trained based on the historical position information of the disconnector contact, the historical working data of the disconnector contact, and the historical temperature rise data of the disconnector contact. For example, the above temperature rise prediction model can be a back propagation neural network (Back Propagation, referred to as BP) model. It should be noted that this is only an example, and there is no specific restriction on the type of temperature rise prediction model. The above temperature rise data can be used to represent the temperature change state of the disconnector contact within a period of time, and can be used to determine the value of the disconnector contact temperature increase relative to the pre-operation or ambient temperature after the disconnector contact has been running for a period of time due to the effects of resistance heating caused by the passage of current.

[0037] Optionally, in the monitoring of power system equipment, the prediction of temperature rise is particularly important because it can indicate whether the disconnector contacts are at risk of overheating. Overheating may be a precursor to disconnector contact failure. Timely prediction and taking measures can effectively avoid disconnector contact damage and system failure. Furthermore, the BP neural network model analyzes the current position information and working data (e.g., edge temperature, room temperature, and humidity) of the knife edge, and the output result of the prediction is the temperature rise of the knife edge in the future.

[0038] Optionally, the temperature rise prediction model is trained in advance based on the historical position information of the isolating switch contact, the historical working data of the isolating switch contact, and the historical temperature rise data of the isolating switch contact. When the position information and the working data are obtained, the position information and the working data can be input into the temperature rise prediction model to predict the temperature rise data of the isolating switch contact in the future period, that is, the temperature rise of the isolating switch contact in the future period can be determined.

[0039] Step S106: determining the working state of the disconnector contacts based on the temperature rise data.

[0040] In the technical solution provided in the above step S106 of the present invention, the above working state may include a normal working state and an abnormal working state. The above abnormal working state may be a faulty working state.

[0041] For example, if the temperature rise data of the isolating switch contact is too high, it can be determined that the working state of the isolating switch contact is an abnormal working state. The temperature rise data can be compared with the standard temperature rise data obtained in advance to determine whether the working state of the isolating switch contact is in a normal working state at this time.

[0042] In this embodiment, based on the position information, blade temperature, room temperature, humidity and other data of the isolating switch contacts detected online, a BP neural network is used to predict the temperature rise of the contact surface of the isolating switch contacts over a period of time to obtain the temperature rise data of the isolating switch contacts.

[0043] Optionally, this embodiment can use the BP neural network algorithm to train a model using the historical position information of the knife switch blade of historical fault information and historical working data (e.g., blade temperature, room temperature, humidity) to obtain a temperature rise prediction model. By utilizing the temperature rise prediction model, the purpose of predicting whether the disconnector contacts will fail can be achieved based on current information (e.g., position information and working data).

[0044] Through the above-mentioned steps S102 and S106 of the present invention, the position information of the disconnector contact and the working data of the disconnector contact in the power system are determined; the position information and the working data are input into the temperature rise prediction model to predict the temperature rise data of the disconnector contact in the future period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contact, the historical working data of the disconnector contact, and the historical temperature rise data of the disconnector contact, and the temperature rise data is used to represent the temperature change state of the disconnector contact; based on the temperature rise data, the working state of the disconnector contact is determined. That is to say, in the embodiment of the present invention, the temperature rise prediction model is used to process the position information and working data of the disconnector contact to obtain the temperature rise data, and based on the temperature rise data, the working state of the disconnector contact can be determined, thereby achieving the technical effect of improving the monitoring efficiency of the disconnector contact and solving the technical problem of low monitoring efficiency of the disconnector contact.

[0045] The above method of this embodiment is further introduced below.

[0046] As an optional implementation, step S102, determining the position information and working data of the isolating switch contacts in the power system, includes: using an infrared sensor to monitor the position information and working data, wherein the distance between the infrared device and the isolating switch contacts meets a distance threshold.

[0047] In this embodiment, the infrared sensor may be a sensor deployed within the target range of the isolating switch contact, and may be an air monitoring device, and the distance between the infrared sensor and the isolating switch contact satisfies the distance threshold. The distance threshold may be a preset value, such as 1 meter, 2 meters, etc., which is only used as an example, and the size of the distance threshold is not specifically limited.

[0048] Optionally, the infrared sensor may include at least an infrared probe and an infrared temperature sensor. It should be noted that there is no specific limitation on the type of the infrared sensor.

[0049] Optionally, the blade of the knife switch has a very high voltage and cannot be monitored at close range, let alone direct contact monitoring. In order to more accurately determine the correctness of the position information of the isolating switch contacts, it is necessary to monitor the blade position more directly. Therefore, in order to improve the accuracy of position information monitoring, in this embodiment, a remote monitoring device (i.e., an infrared sensor) is proposed. The infrared sensor can effectively monitor the position information of the blade at a certain safe distance and give a signal.

[0050] As an optional implementation, using an infrared sensor to monitor position information and working data includes: using an infrared probe in the infrared sensor to monitor position information; using an infrared temperature sensor in the infrared sensor to monitor working data.

[0051] In this embodiment, the infrared sensor may include at least an infrared probe and an infrared temperature sensor. The infrared probe in the infrared sensor may be used to monitor position information, and the infrared temperature sensor in the infrared sensor may be used to monitor working data.

[0052] Optionally, Figure 2 is a schematic diagram of deploying infrared sensors according to an embodiment of the present invention, such as Figure 2 As shown, the current isolating switch contact is in the closed state, and an infrared sensor 22 can be deployed near the isolating switch contact 21. The infrared probe in the infrared sensor 22 can be used as a trigger device for long-distance detection of the knife edge of the knife switch. It can be installed on the beam below the insulating porcelain bottle of the knife switch (that is, the aluminum box universal bracket), away from the high-voltage live parts, and the knife edge position can be judged at a safe distance, which solves the existing method of indirect confirmation of the knife edge position.

[0053] Alternatively, if Figure 2 As shown, the infrared sensor 22 can also be configured with an adjustable universal bracket and a rotating base. The universal bracket can be used to adapt to the installation on any type of knife switch crossbeam. The rotating base under the probe can adjust the angle of the arc control according to the structure and position of the knife switch blade to adapt to the blade; the infrared temperature sensor can measure and record working data such as the knife switch blade temperature and room temperature.

[0054] Optionally, a stainless steel shell may be disposed outside the infrared sensor 22 to protect the infrared sensor, and a waterproof aviation plug (eg, IP67) may also be disposed below the infrared sensor.

[0055] Optionally, the knife switch cross beam (ie, the bracket) may be provided with a bracket installation slot. The cross beam may include a position sensor installation position, and a plurality of infrared sensors may be installed.

[0056] For example, an infrared sensor may be used to record the position information of a three-phase blade (ie, a disconnector contact), as well as operating data such as blade temperature, room temperature, and humidity.

[0057] At present, with the aging of equipment and the increase of operating load in the power system, more and more faults occur in disconnectors and line changers. Due to the disrepair of equipment over the years, it tends to age, the contact clamping spring becomes loose and deformed, the clamping force is insufficient, and the contact between some contact fingers and contacts is reduced, resulting in poor contact and deviation in the closing angle of the knife switch. As a result, the contact surface of the disconnector contact is abnormal, which leads to the problem of heating of the contact surface of the moving and static contacts of the knife switch.

[0058] In view of the heating of the contact surface of the moving and static contacts of the above-mentioned knife switch, in the related technology, the position information of the knife edge is usually determined by a certain link of the knife switch transmission part, that is, the on-off of the auxiliary switch contact in the operating mechanism is used to determine the on-off of the knife switch knife edge, or the position of the knife switch knife edge can be determined by a secondary component, and then the knife switch knife edge is manually determined again on site to determine whether it is in place, or a travel switch or an electromagnetic proximity switch can be installed on the rotating base of the knife switch to obtain the position information; for the acquisition of working data, the working data of the isolating switch contact can be determined by combining three methods: general temperature measurement, key temperature measurement and suspicious temperature measurement. However, the above method can only output prompt information when the heating is obvious, which has the problem of difficult accurate warning and troublesome operation. In addition, the existing method of using the on-off of the auxiliary switch contact to determine the position of the knife switch knife edge is only an indirect judgment of the knife edge position, because there are multiple transmission links and components in the middle from the output torque of the mechanism to the operation of the knife switch knife edge, and it is not possible to really judge whether the knife edge is in place. It is more reliable to have manual on-site confirmation again, but it is still time-consuming and labor-intensive, and the problem of not being able to find the problem in time.

[0059] To solve the above problems, in this embodiment, the infrared probe is installed outside a safe distance, for example, it can be installed at a position directly aligned with the disconnector contact, so that the infrared probe in the infrared sensor can be used to monitor the position information. This embodiment uses infrared rays to determine the position of the knife edge of the knife switch in a non-contact and long-distance manner, thereby solving the problem that it is time-consuming and laborious to obtain position information and the problem cannot be discovered in time. At the same time, the infrared temperature sensor in the infrared sensor is used to monitor the working data, and the working data is processed to achieve the purpose of accurately determining the working state of the disconnector contact, thereby solving the technical problem of low monitoring efficiency of the disconnector contact.

[0060] As an optional implementation, step S104, inputs the position information and working data into the temperature rise prediction model to predict the temperature rise data of the disconnector contacts in a future period, including: filtering abnormal data in the working data based on the standard working data of the disconnector contacts; inputs the position information and the filtered working data into the temperature rise prediction model to predict the temperature rise data.

[0061] In this embodiment, the standard working data may be pre-set data, and the acquisition may be determined based on at least one working data, for example, it may be an average value of a plurality of temperature data, or it may be an average value of a plurality of humidity data, etc. The standard working data may include data such as the average temperature value and the average humidity value, which are only used as examples here, and the type of the standard working data is not specifically limited. The position information may be used to determine the position of the three-phase knife switch blade.

[0062] Optionally, based on standard working data, the working data is screened to determine abnormal data in the working data, the abnormal working data in the working data is eliminated, and the screened working data is determined as target working data. Further, based on the position information and the target working data, the temperature rise data of the disconnector contacts can be determined.

[0063] For example, assuming that the standard working data is the average value of the working data corresponding to multiple moments, the location information and the temperature, room temperature, and humidity corresponding to multiple moments can be obtained. The Raida criterion (that is, the 3σ criterion) can be used to eliminate abnormal data in the working data. The following uses temperature as an example to further explain the elimination method. The processing method for other types of working data is similar to that for temperature data: determine the average value of multiple temperatures Determine the temperature (X n ) and the mean value Compare the absolute value of the difference with 3σ. If the deviation is greater than 3σ, the temperature data can be determined to be an abnormal value. The abnormality judgment criteria are as follows:

[0064]

[0065] Furthermore, the working data after the abnormal data is removed is the target working data, and the target working data can be used for subsequent analysis.

[0066] To take another example, standard working data is set in advance, and the standard working data can be the working data of the isolating switch contacts under normal working conditions. When the working data is obtained, the difference between the standard working data and the working data can be determined. If the difference is greater than the difference threshold, it can be determined that the working data is abnormal data. The abnormal data in multiple working data can be screened out in the above manner, so as to obtain the target working data corresponding to the normal state, and the position information and the target working data are input into the temperature rise prediction model to predict the temperature rise data.

[0067] It should be noted that the above method of determining target working data based on standard working data is only for illustration and is not specifically limited here.

[0068] As an optional implementation, the method may further include: constructing a plurality of neurons of a temperature rise prediction model; and training the temperature rise prediction model based on historical position information, historical working data, historical temperature rise data and a plurality of neurons.

[0069] In this embodiment, the temperature rise prediction model can be a BP neural network, a nonlinear uncertainty mathematical model, a multi-layer feedforward artificial neural network with a continuous transfer function, and can also be called a BP neural network knife switch blade temperature rise prediction model.

[0070] Optionally, the temperature rise prediction model can use the error back propagation algorithm (BP algorithm) as a training method, and continuously modify the weights and thresholds of the network with the goal of minimizing the mean square error, so as to achieve the purpose of fitting the data with high precision, thereby achieving the purpose of improving the accuracy of the temperature rise prediction model.

[0071] Optionally, historical location information and historical working data are obtained, and the Laida criterion can be used to remove abnormal data in the historical working data. The historical working data after removing the abnormal values ​​is used for subsequent analysis. Furthermore, multiple neurons can be set to construct an initial temperature rise prediction model, wherein the above neurons can include input layer neurons, hidden layer neurons, and output layer neurons, and the above input layer neurons, hidden layer neurons, and output layer neurons can be expressed as follows:

[0072] P=[p1,p2,...,p n ]

[0073] S=[s1,s2,...,s n ]

[0074] A=[a1,a2,...,a n ]

[0075] Among them, P, S, and A can be used to represent the weight coefficients corresponding to different neurons respectively.

[0076] Optionally, for forward information transmission, each neuron in the input layer is connected to each neuron in the hidden layer with corresponding weights, and the first neuron in the hidden layer Get the output value from each neuron in the input layer and sum it up Add threshold By exciting the function f 1 , and get the output value of the neuron:

[0077]

[0078] The first neuron a1 in the output layer receives the output value of each neuron in the hidden layer and adds them up to get Add threshold By exciting the function f 2 , get the output value of the neuron in the output layer:

[0079]

[0080] in, It can be used to represent the connection weight between the i-th neuron in the input layer and the k-th neuron in the hidden layer; It can be used to represent the connection weight between the kth neuron in the hidden layer and the jth neuron in the output layer; the activation function of the hidden layer can be the activation function of the hidden layer is f 1 , the excitation function of the output layer can be f2, It can be used to represent the threshold of each neuron in the hidden layer. It can be used to represent the threshold of each neuron in the output layer.

[0081]

[0082] Optionally, an initial temperature rise prediction model can be constructed based on multiple neurons, and the initial temperature rise prediction model is called to predict historical position information and historical working data to obtain historical predicted temperature difference data. Based on the historical predicted temperature difference data and historical temperature difference data, the initial model parameters of the initial temperature rise prediction model can be adjusted to train the temperature rise prediction model.

[0083] The following is a further example of the process of training a temperature rise prediction model based on historical position information, historical working data, historical temperature rise data and multiple neurons.

[0084] As an optional implementation, a temperature rise prediction model is trained based on historical position information, historical working data, historical temperature rise data and multiple neurons, including: predicting historical position information and historical working data based on multiple neurons to obtain historical predicted temperature rise data; determining the mean square error between the historical predicted temperature rise data and the expected temperature rise data; determining the error signal of the neuron based on the mean square error; and using the error signal, historical predicted temperature difference data and historical temperature difference data to modify the initial model parameters of the temperature rise prediction model to obtain the temperature rise prediction model.

[0085] In this embodiment, historical position information and historical working data are predicted based on multiple neurons to obtain historical predicted temperature rise data; the mean square error between the historical predicted temperature rise data and the expected temperature rise data is determined; based on the mean square error, the error signal of the neuron is determined; the initial model parameters of the temperature rise prediction model are modified using the error signal, the historical predicted temperature difference data and the historical temperature difference data to obtain the temperature rise prediction model. The above initial model parameters can be weights and thresholds corresponding to different neurons or neural network layers.

[0086] As an optional implementation, based on the mean square error, determining the error signal of the neuron includes: back-propagating the mean square error; distributing the propagated mean square error to multiple neurons; and determining the mean square error distributed to the multiple neurons as the error signal.

[0087] In this embodiment, the mean square error can be back-propagated; the propagated mean square error is distributed to multiple neurons; and the mean square error distributed to multiple neurons is determined as an error signal. The mean square error obtained by the above neuron distribution can be the error signal MSE J (j=1,2,3).

[0088] For example, N samples with M-dimensional index vectors (i.e., historical position information and historical working data) are input into the input layer of the initial temperature rise prediction model. After being processed by the neurons in the hidden layer, they are transmitted to the output layer to obtain the actual output A (i.e., historical predicted temperature difference data). In the output layer, the actual output A is compared with the expected output T (i.e., expected temperature difference data) to determine the mean square error MSE between the expected output and the actual output = ∑(AT) 2 / N. Furthermore, if the MSE does not meet the predetermined requirement ε, the back propagation process is entered, and the output error signal MSE is transmitted back layer by layer in the form of a gradient along the original forward propagation path, and the error signal MSE is apportioned to all neurons in each layer, thereby obtaining the error signal MSE of each layer of neurons. J (j=1,2,3), the error signal MSE can be JAs the basis for correcting the connection weights and thresholds (that is, the initial model parameters), they are modified and the two processes of forward propagation of information and reverse propagation of error are repeatedly run until the error signal MSE converges to ε, thereby obtaining a trained temperature rise prediction model.

[0089] Optionally, a loss function may be set, and the initial model parameters may be adjusted based on the loss function, historical temperature difference data, and historical predicted temperature difference data to obtain a temperature rise prediction model. It should be noted that the above is only an example, and the method for adjusting the initial model parameters is not specifically limited.

[0090] As an optional implementation, step S106, determining the working state of the disconnector contact based on the temperature rise data, includes: in response to the temperature rise data not meeting the expected temperature rise data, determining that the working state is an abnormal working state.

[0091] In this embodiment, the above-mentioned expected temperature rise data may be temperature rise data set in advance according to a test or experiment. The expected temperature rise data may be a temperature value or a value range. For example, the expected temperature rise data may be 5 degrees Celsius, or 8 degrees Celsius, or 5 to 8 degrees Celsius, etc., and may be used to judge the rationality of the temperature rise data. It should be noted that this is only an example, and there is no specific restriction on the setting method of the expected temperature rise data and the size of the expected temperature rise data.

[0092] Optionally, expected temperature rise data can be set in advance. When the temperature rise prediction model outputs the temperature rise data, the temperature rise data can be compared with the expected temperature rise data. If the temperature rise data does not meet the expected temperature rise data, that is, the temperature rise data is greater than the expected temperature rise data, or the temperature rise data is not within the range of the expected temperature rise data, it can be determined that the working state is an abnormal working state, that is, a fault state.

[0093] As an optional implementation, the method may further include: in response to the working state being an abnormal working state, outputting prompt information.

[0094] In this embodiment, if the working state is determined to be an abnormal working state, a prompt message can be output to prompt the operator to repair and process the disconnector contacts. The prompt message can be text, voice, video, etc., for example, a mobile phone text message, a program reminder message, etc. It should be noted that this is only an example, and the type of prompt message is not specifically limited.

[0095] Optionally, if the predicted temperature rise data exceeds a preset safety threshold (ie, expected temperature rise data), an alarm may be issued so that power operation and maintenance personnel can promptly inspect and process the disconnector contacts to prevent safety problems caused by overheating of the disconnector contacts.

[0096] Optionally, based on the current position information of the isolating switch contacts, blade temperature, room temperature, humidity and other working data detected online, the BP neural network method is used to predict the temperature rise of the knife switch contact surface over a period of time to obtain the temperature rise data of the isolating switch contacts. When the temperature rise data is greater than the preset value (that is, the expected temperature rise data), it can be determined that the working state is an abnormal working state. In response to the working state being an abnormal working state, an alarm can be issued.

[0097] In an embodiment of the present invention, a temperature rise prediction model is used to process the position information and working data of the isolating switch contacts to obtain temperature rise data. Based on the temperature rise data, the working state of the isolating switch contacts can be determined, thereby achieving the technical effect of improving the monitoring efficiency of the isolating switch contacts and solving the technical problem of low monitoring efficiency of the isolating switch contacts.

[0098] According to an embodiment of the present invention, a device for determining the working state of a transformer is also provided. It should be noted that the device for determining the working state of a transformer of this embodiment can be used to execute the method for determining the working state of a transformer of Embodiment 1 of the present invention.

[0099] Figure 3 Schematic diagram of a device for determining the working state of a transformer according to an embodiment of the present invention. Figure 3 As shown, the device 30 for determining the working state of the transformer may include: a first determining unit 302 , a predicting unit 304 and a second determining unit 306 .

[0100] The first determining unit 302 is used to determine the position information of the disconnector contacts and the working data of the disconnector contacts in the power system.

[0101] The prediction unit 304 is used to input the position information and working data into the temperature rise prediction model to predict the temperature rise data of the disconnector contacts in a future period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contacts, the historical working data of the disconnector contacts, and the historical temperature rise data of the disconnector contacts, and the temperature rise data is used to indicate the temperature change state of the disconnector contacts.

[0102] The second determining unit 306 is used to determine the working state of the disconnector contact based on the temperature rise data.

[0103] The device for determining the working state of the transformer of this embodiment determines the position information of the isolating switch contacts and the working data of the isolating switch contacts in the power system through the first determination unit; inputs the position information and the working data into the temperature rise prediction model through the prediction unit, and predicts the temperature rise data of the isolating switch contacts in the future time period, wherein the temperature rise prediction model is trained based on the historical position information of the isolating switch contacts, the historical working data of the isolating switch contacts, and the historical temperature rise data of the isolating switch contacts, and the temperature rise data is used to represent the temperature change state of the isolating switch contacts; determines the working state of the isolating switch contacts based on the temperature rise data through the second determination unit, thereby achieving the technical effect of improving the monitoring efficiency of the isolating switch contacts and solving the technical problem of low monitoring efficiency of the isolating switch contacts.

[0104] The embodiment of the present invention can provide a computer terminal, which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal can also be replaced by a terminal device such as a mobile terminal.

[0105] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0106] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for determining the working status of the transformer: determining the position information of the disconnector contacts and the working data of the disconnector contacts in the power system; inputting the position information and the working data into the temperature rise prediction model to predict the temperature rise data of the disconnector contacts in the future time period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contacts, the historical working data of the disconnector contacts, and the historical temperature rise data of the disconnector contacts, and the temperature rise data is used to indicate the temperature change state of the disconnector contacts; based on the temperature rise data, determining the working state of the disconnector contacts.

[0107] Optionally, Figure 4 is a structural block diagram of a computer terminal according to an embodiment of the present invention. Figure 4 As shown, the computer terminal 408 may include: one or more (only one is shown in the figure) processors 402 , a memory 404 and a transmission device 406 .

[0108] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the working state of the transformer in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned method for determining the working state of the transformer is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computer terminal 408 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0109] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: determine the position information of the isolating switch contacts and the working data of the isolating switch contacts in the power system; input the position information and the working data into the temperature rise prediction model to predict the temperature rise data of the isolating switch contacts in the future time period, wherein the temperature rise prediction model is trained based on the historical position information of the isolating switch contacts, the historical working data of the isolating switch contacts, and the historical temperature rise data of the isolating switch contacts, and the temperature rise data is used to indicate the temperature change state of the isolating switch contacts; based on the temperature rise data, determine the working state of the isolating switch contacts.

[0110] It can be understood by those skilled in the art that Figure 4 The structure shown is for illustration only, and the computer terminal 408 may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, referred to as MID), a PAD, or other terminal devices. Figure 4 The structure of the computer terminal 408 is not limited. For example, the computer terminal 408 may also include Figure 4 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 4 Different configurations are shown.

[0111] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0112] According to an embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored program, wherein the program executes the method for determining the working state of the transformer in Embodiment 1.

[0113] Optionally, in this embodiment, the computer-readable storage medium may be located in any one of the computer terminals in a computer terminal group in a computer network, or in any one of the mobile terminals in a mobile terminal group.

[0114] Optionally, in this embodiment, the computer-readable storage medium is configured to store program codes for performing the following steps: determining position information of disconnector contacts and operating data of disconnector contacts in the power system; inputting the position information and operating data into a temperature rise prediction model to predict the temperature rise data of the disconnector contacts in a future period, wherein the temperature rise prediction model is trained based on historical position information of the disconnector contacts, historical operating data of the disconnector contacts, and historical temperature rise data of the disconnector contacts, and the temperature rise data is used to represent the temperature change state of the disconnector contacts; based on the temperature rise data, determining the operating state of the disconnector contacts.

[0115] Optionally, the computer-readable storage medium may also execute program codes of the following steps: using an infrared sensor to monitor position information and working data, wherein the distance between the infrared device and the disconnector contact meets a distance threshold.

[0116] Optionally, the computer-readable storage medium may also execute program codes of the following steps: using an infrared probe in the infrared sensor to monitor position information; using an infrared temperature sensor in the infrared sensor to monitor working data.

[0117] Optionally, the computer-readable storage medium may also execute program codes of the following steps: based on standard working data of the isolating switch contacts, filtering abnormal data in the working data; inputting the position information and the filtered working data into a temperature rise prediction model to predict the temperature rise data.

[0118] Optionally, the computer-readable storage medium may also execute program codes of the following steps: constructing multiple neurons of a temperature rise prediction model; and training a temperature rise prediction model based on historical position information, historical working data, historical temperature rise data and multiple neurons.

[0119] Optionally, the computer-readable storage medium can also execute the program code of the following steps: predict historical position information and historical working data based on multiple neurons to obtain historical predicted temperature rise data; determine the mean square error between the historical predicted temperature rise data and the expected temperature rise data; based on the mean square error, determine the error signal of the neuron; use the error signal, historical predicted temperature difference data and historical temperature difference data to modify the initial model parameters of the temperature rise prediction model to obtain the temperature rise prediction model.

[0120] Optionally, the computer-readable storage medium may also execute program codes for the following steps: back-propagating the mean square error; distributing the propagated mean square error to a plurality of neurons; and determining the mean square error distributed to the plurality of neurons as an error signal.

[0121] Optionally, the computer-readable storage medium may further execute program code for the following steps: in response to the temperature rise data not meeting the expected temperature rise data, determining that the operating state is an abnormal operating state.

[0122] Optionally, the computer-readable storage medium may also execute program code for the following steps: in response to the working state being an abnormal working state, outputting prompt information.

[0123] In this embodiment, the temperature rise prediction model is used to process the position information and working data of the isolating switch contacts to obtain temperature rise data. Based on the temperature rise data, the working state of the isolating switch contacts can be determined, thereby achieving the technical effect of improving the monitoring efficiency of the isolating switch contacts and solving the technical problem of low monitoring efficiency of the isolating switch contacts.

[0124] According to an embodiment of the present invention, a processor is further provided, the processor being used to run a program, wherein when the program is run by the processor, the method for determining the working state of the transformer in Embodiment 1 is executed.

[0125] Optionally, in this embodiment, the computer terminal may be located in at least one network device among a plurality of network devices of the computer network.

[0126] In this embodiment, the above-mentioned computer terminal can execute the program code of the following steps in the method for determining the working status of the transformer: determining the position information of the disconnector contacts and the working data of the disconnector contacts in the power system; inputting the position information and the working data into the temperature rise prediction model to predict the temperature rise data of the disconnector contacts in the future time period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contacts, the historical working data of the disconnector contacts, and the historical temperature rise data of the disconnector contacts, and the temperature rise data is used to indicate the temperature change state of the disconnector contacts; based on the temperature rise data, determining the working state of the disconnector contacts.

[0127] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the method and device for determining the working state of the transformer in the embodiment of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned method for determining the working state of the transformer is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0128] The processor can call the information and application programs stored in the memory through the transmission device to perform the following steps: determine the position information of the isolating switch contacts and the working data of the isolating switch contacts in the power system; input the position information and the working data into the temperature rise prediction model to predict the temperature rise data of the isolating switch contacts in the future time period, wherein the temperature rise prediction model is trained based on the historical position information of the isolating switch contacts, the historical working data of the isolating switch contacts, and the historical temperature rise data of the isolating switch contacts, and the temperature rise data is used to indicate the temperature change state of the isolating switch contacts; based on the temperature rise data, determine the working state of the isolating switch contacts.

[0129] Optionally, in this embodiment, the processor is configured to store program code for executing the following steps: determining position information of the isolating switch contacts and operating data of the isolating switch contacts in the power system; inputting the position information and operating data into a temperature rise prediction model to predict the temperature rise data of the isolating switch contacts in a future time period, wherein the temperature rise prediction model is trained based on historical position information of the isolating switch contacts, historical operating data of the isolating switch contacts, and historical temperature rise data of the isolating switch contacts, and the temperature rise data is used to represent the temperature change state of the isolating switch contacts; based on the temperature rise data, determining the operating state of the isolating switch contacts.

[0130] Optionally, the processor may also execute program codes of the following steps: using an infrared sensor to monitor position information and working data, wherein the distance between the infrared device and the disconnector contact meets a distance threshold.

[0131] Optionally, the processor may also execute program codes of the following steps: using an infrared probe in the infrared sensor to monitor position information; and using an infrared temperature sensor in the infrared sensor to monitor working data.

[0132] Optionally, the processor may also execute program codes of the following steps: based on standard working data of the isolating switch contacts, filtering abnormal data in the working data; inputting the position information and the filtered working data into a temperature rise prediction model to predict the temperature rise data.

[0133] Optionally, the processor may also execute program codes of the following steps: construct multiple neurons of a temperature rise prediction model; and train the temperature rise prediction model based on historical position information, historical working data, historical temperature rise data and multiple neurons.

[0134] Optionally, the processor may also execute the program code of the following steps: predicting historical position information and historical working data based on multiple neurons to obtain historical predicted temperature rise data; determining the mean square error between the historical predicted temperature rise data and the expected temperature rise data; determining the error signal of the neuron based on the mean square error; and modifying the initial model parameters of the temperature rise prediction model using the error signal, historical predicted temperature difference data and historical temperature difference data to obtain the temperature rise prediction model.

[0135] Optionally, the processor may also execute program codes of the following steps: back-propagating the mean square error; distributing the propagated mean square error to a plurality of neurons; and determining the mean square error distributed to the plurality of neurons as an error signal.

[0136] Optionally, the processor may further execute program code of the following steps: in response to the temperature rise data not meeting the expected temperature rise data, determining that the working state is an abnormal working state.

[0137] Optionally, the processor may also execute program code of the following steps: in response to the working state being an abnormal working state, outputting prompt information.

[0138] By adopting the embodiment of the present invention, the position information and working data of the isolating switch contacts are processed using the temperature rise prediction model to obtain temperature rise data. Based on the temperature rise data, the working state of the isolating switch contacts can be determined, thereby achieving the technical effect of improving the monitoring efficiency of the isolating switch contacts and solving the technical problem of low monitoring efficiency of the isolating switch contacts.

[0139] According to an embodiment of the present invention, a computer program product is further provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the method for determining the working state of the transformer in Embodiment 1 is implemented.

[0140] An embodiment of the present application may provide an electronic device, which may include a memory and a processor.

[0141] Figure 5It is a block diagram of an electronic device according to a method for determining the working state of a transformer according to an embodiment of the present application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0142] like Figure 5 As shown, the device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 to a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0143] A number of components in the device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 504, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0144] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as a verification method for data. For example, in some embodiments, the verification method for data may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 500 via the ROM 502 and / or the communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the data verification method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the data verification method in any other appropriate manner (e.g., by means of firmware).

[0145] According to an embodiment of the present application, a method for determining the working state of a transformer is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0146] Various embodiments of the systems and techniques described above herein may be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include: being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor that may be a dedicated or general purpose programmable processor that may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] The program code for implementing the method of the present application can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0148] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0149] To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or an LCD (liquid crystal display, monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a path ball) through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0150] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a Local Area Network (LAN), a Wide Area Network (WAN), and the Internet.

[0151] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0152] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0153] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0154] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic, for example, the division of units can be a logical function division, and there can be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0155] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0157] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0158] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the working state of a disconnector contact, characterized in that: include: Determining position information of disconnector contacts and operating data of disconnector contacts in a power system; Inputting the position information and the working data into a temperature rise prediction model to predict the temperature rise data of the disconnector contact in a future period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contact, the historical working data of the disconnector contact, and the historical temperature rise data of the disconnector contact, and the temperature rise data is used to indicate the temperature change state of the disconnector contact; Based on the temperature rise data, the working state of the disconnector contact is determined.

2. The method according to claim 1, characterized in that The determining of the position information of the isolating switch contacts and the working data of the isolating switch contacts in the power system includes: The position information and the working data are monitored by using an infrared sensor, wherein the distance between the infrared device and the disconnector contact meets a distance threshold.

3. The method according to claim 2, characterized in that The using of infrared sensors to monitor the position information and the working data includes: Using an infrared probe in the infrared sensor to monitor the position information; The infrared temperature measuring sensor in the infrared sensor is used to monitor the working data.

4. The method according to claim 1, characterized in that: The step of inputting the position information and the working data into a temperature rise prediction model to predict the temperature rise data of the disconnector contact in a future period includes: Based on the standard working data of the isolating switch contact, screening abnormal data in the working data; The position information and the filtered working data are input into a temperature rise prediction model to predict the temperature rise data.

5. The method according to claim 1, characterized in that: The method further comprises: Constructing a plurality of neurons of the temperature rise prediction model; The temperature rise prediction model is trained based on the historical position information, the historical working data, the historical temperature rise data and the plurality of neurons.

6. The method according to claim 5, characterized in that The training to obtain the temperature rise prediction model based on the historical position information, the historical working data, the historical temperature rise data and the plurality of neurons includes: Predicting the historical position information and historical working data based on the plurality of neurons to obtain historical predicted temperature rise data; Determining a mean square error between the historical predicted temperature rise data and the expected temperature rise data; determining an error signal of the neuron based on the mean square error; The error signal, the historical predicted temperature difference data and the historical temperature difference data are used to modify the initial model parameters of the temperature rise prediction model to obtain the temperature rise prediction model.

7. The method according to claim 6, characterized in that The step of determining the error signal of the neuron based on the mean square error comprises: Back-propagating the mean square error; Distributing the propagated mean square error to a plurality of neurons; The mean square error distributed to the plurality of neurons is determined as the error signal.

8. The method according to claim 1, characterized in that The determining the working state of the isolating switch contact based on the temperature rise data includes: In response to the temperature rise data not meeting the expected temperature rise data, it is determined that the operating state is an abnormal operating state.

9. The method according to claim 8, characterized in that The method further comprises: In response to the working state being the abnormal working state, outputting prompt information.

10. A device for determining the working state of a disconnector contact, characterized in that: include: A first determining unit, used to determine position information of a disconnector contact and working data of the disconnector contact in a power system; a prediction unit, used for inputting the position information and the working data into a temperature rise prediction model, and predicting the temperature rise data of the disconnector contact in a future period, wherein the temperature rise prediction model is trained based on the historical position information of the disconnector contact, the historical working data of the disconnector contact, and the historical temperature rise data of the disconnector contact, and the temperature rise data is used to indicate the temperature change state of the disconnector contact; The second determining unit is used to determine the working state of the disconnector contact based on the temperature rise data.

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