Secondary cable insulation mutation detection method, device and system

Abstract

The invention provides a secondary cable insulation mutation detection method, device and system, and belongs to the technical field of power system protection and control. Comprises: firstly, establishing a detection model; establishing an insulation data set {R1, R2,..., RN} based on the detection time sequence data; based on an insulation data set, a detection model is used for secondary cable insulation mutation detection, the detection model firstly performs grouping based on the insulation data set, then generates data sequences sorted according to numerical values, and further calculates characteristic quantities based on the data sequences; according to a comparison result of the characteristic quantity and a judgment threshold, obtaining a conclusion whether insulation sudden change exists or not currently; and when the detection result of the detection model is that insulation sudden change exists at present, insulation sudden change warning is performed. By processing and analyzing the insulation resistance data, the insulation mutation is simply and efficiently identified, and the operation and maintenance efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system protection and control, and in particular to a secondary cable insulation mutation detection method, device and system. Background Art

[0002] Secondary cables are important components of the power system, carrying functions such as signal transmission and power supply. Affected by factors such as the external environment, material aging, and mechanical stress, power secondary cables are at risk of damage, resulting in serious consequences such as reduced insulation or even short circuits, affecting the normal operation of equipment and safe operation of the system. Therefore, insulation testing of secondary cables is very important.

[0003] The existing insulation abnormality detection method usually compares the collected insulation resistance value with the threshold value. When the threshold value is exceeded, the insulation abnormality is identified and an alarm is issued. This method is relatively simple and can only analyze data based on instantaneous static analysis. When an alarm is identified, the insulation abnormality has already occurred. If an early warning can be issued before the insulation abnormality occurs, the operation and maintenance efficiency can be improved and the failure rate can be reduced. Summary of the invention

[0004] In view of this, the present invention provides a secondary cable insulation mutation detection method, device and system, which can simply and efficiently identify insulation mutations by processing and analyzing insulation resistance data, thereby improving operation and maintenance efficiency.

[0005] The technical solution adopted by the embodiment of the present invention to solve the technical problem is:

[0006] A secondary cable insulation mutation detection method, comprising:

[0007] Step S1, establishing a detection model, which is used for data input, data grouping and sorting processing, feature quantity calculation and judgment conclusion output;

[0008] Step S2: Establish an insulation data set {R1, R2, ..., R N};

[0009] Step S3, based on the insulation data set, using the detection model to perform secondary cable insulation mutation detection, the detection model first groups the insulation data set, then generates a data sequence sorted by numerical value, and further calculates a feature value based on each data sequence, and draws a conclusion on whether there is currently an insulation mutation based on a comparison result of the feature value and a discrimination threshold;

[0010] Step S4: When the detection result of the detection model indicates that there is currently an insulation mutation, an insulation mutation alarm is issued.

[0011] Preferably, the step S3 of performing secondary cable insulation mutation detection using the detection model comprises:

[0012] Step S31, setting parameters, including the minimum data window coefficient n, data length N, and discrimination threshold B, where n <N / 2;

[0013] Step S32, obtaining insulation data {R1, R2, ..., R N};

[0014] Step S33, initializing parameter k, setting k=n;

[0015] Step S34: for the data {R1, R2, ..., R k}, {R k+1 ,R k+2 ,...,R N} are sorted from small to large, and a new sequence {D1,D2,...,D k}, {D k+1 ,D k+2 ,...,D N};

[0016] Step S35, obtaining parameters L and R, the expressions are:

[0017]

[0018] Step S36, calculate feature quantity A k , A k The expression is:

[0019]

[0020] Step S37, determine whether k == n is currently satisfied; if not, proceed to step S38; if satisfied, proceed to step S39;

[0021] Step S38, determine whether A is currently satisfied k >A max Among them, A max is the maximum eigenvalue. If it is satisfied, continue to execute step S39; if it is not satisfied, continue to execute step S310;

[0022] Step S39, update the maximum eigenvalue A max , maximum position k Amax :

[0023] A max =A k

[0024] k Amax =k

[0025] Step S310, determine whether k<Nn is satisfied, if so, continue to step S311, if not, continue to step S312;

[0026] Step S311, update coefficient k, set k=k+1; and return to execute step S34;

[0027] Step S312, determine whether the current A is satisfied max >B; if satisfied, it is determined that there is currently an insulation mutation; if not satisfied, it is determined that there is currently no insulation mutation.

[0028] An electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the aforementioned method.

[0029] A secondary cable insulation mutation detection system comprises the aforementioned electronic device and an insulation monitoring device, wherein the insulation monitoring device is used to collect insulation data of the secondary cable according to a preset sampling frequency and provide the insulation data to the electronic device.

[0030] Preferably, the insulation monitoring device collects information in a manner that the secondary cable passes through a leakage current sensor of the insulation monitoring device, and the insulation monitoring device obtains leakage current information and converts it into an insulation resistance value.

[0031] Preferably, it further includes a cloud server, and the electronic device sends the detection result to the cloud server, and the cloud server is used to provide the detection result to the device with query permission.

[0032] It can be seen from the above technical solution that the secondary cable insulation mutation detection method, device and system provided by the embodiment of the present invention first establish a detection model; establish an insulation data set {R1, R2, ..., R N}; Based on the insulation data set, the detection model is used to perform secondary cable insulation mutation detection. The detection model first groups the insulation data set, then generates a data sequence sorted by numerical value, and further calculates the feature quantity based on each data sequence. According to the comparison result between the feature quantity and the discrimination threshold, it is concluded whether there is currently an insulation mutation. When the detection result of the detection model is that there is currently an insulation mutation, an insulation mutation alarm is issued. The present invention simply and efficiently identifies insulation mutations by processing and analyzing insulation resistance data, thereby improving operation and maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a flow chart of the secondary cable insulation mutation detection method.

[0034] Figure 2 The present invention is a block diagram of an electronic device for implementing the secondary cable insulation mutation detection method according to the embodiment disclosed in the present invention. DETAILED DESCRIPTION

[0035] The technical scheme and technical effects of the present invention are further elaborated in detail below in conjunction with the accompanying drawings of the present invention.

[0036] The present invention provides a secondary cable insulation mutation detection method, comprising:

[0037] Step S1, establishing a detection model, which is used for data input, data grouping and sorting, feature quantity calculation and judgment conclusion output;

[0038] Step S2: Establish an insulation data set {R1, R2, ..., R N};

[0039] Step S3, based on the insulation data set, the detection model is used to perform secondary cable insulation mutation detection. The detection model first groups the insulation data set, then generates a data sequence sorted by numerical value, and further calculates a feature value based on each data sequence. Based on the comparison result of the feature value and the discrimination threshold, a conclusion is drawn as to whether there is currently an insulation mutation.

[0040] Step S4: When the detection result of the detection model indicates that there is currently an insulation mutation, an insulation mutation alarm is issued.

[0041] refer to Figure 1 The discrimination process, step S3, uses the detection model to perform secondary cable insulation mutation detection, including:

[0042] Step S31, setting parameters, including the minimum data window coefficient n, data length N, and discrimination threshold B, where n <N / 2;

[0043] Step S32, obtaining insulation data {R1, R2, ..., R N};

[0044] Step S33, initializing parameter k, setting k=n;

[0045] Step S34: for the data {R1, R2, ..., R k}, {R k+1 ,R k+2 ,...,R N} are sorted from small to large, and a new sequence {D1,D2,...,D k}, {D k+1 ,D k+2 ,...,D N};

[0046] Step S35, obtaining parameters L and R, the expressions are:

[0047]

[0048] Step S36, calculate feature quantity A k , A k The expression is:

[0049]

[0050] Step S37, determine whether k == n is currently satisfied; if not, proceed to step S38; if satisfied, proceed to step S39;

[0051] Step S38, determine whether A is currently satisfied k >A max Among them, A max is the maximum eigenvalue. If it is satisfied, continue to execute step S39; if it is not satisfied, continue to execute step S310;

[0052] Step S39, update the maximum eigenvalue A max , maximum position k Amax :

[0053] A max =A k (4)

[0054] k Amax =k (5)

[0055] Step S310, determine whether k<Nn is satisfied, if so, continue to step S311, if not, continue to step S312;

[0056] Step S311, update coefficient k, set k=k+1; and return to execute step S34;

[0057] Step S312, determine whether the current A is satisfied max >B; if satisfied, it is determined that there is currently an insulation mutation; if not satisfied, it is determined that there is currently no insulation mutation.

[0058] Furthermore, the present invention provides an electronic device, comprising: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute Figure 1 The method shown.

[0059] A secondary cable insulation mutation detection system includes the aforementioned electronic device and an insulation monitoring device, wherein the insulation monitoring device is used to collect insulation data of the secondary cable according to a preset sampling frequency and provide the insulation data to the electronic device. The collection method of the insulation monitoring device is that the secondary cable passes through the leakage current sensor of the insulation monitoring device, and the insulation monitoring device obtains the leakage current information and converts it into an insulation resistance value.

[0060] Furthermore, the system also includes a cloud server, and the electronic device sends the detection result to the cloud server, and the cloud server is used to provide the detection result to the device with query permission.

[0061] The present invention analyzes insulation data, quickly analyzes change trends and mutation characteristics, and extracts early warnings, which can effectively improve operation and maintenance efficiency and reduce failure rates.

[0062] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0063] Figure 2 A schematic block diagram of an example electronic device 200 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, 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, 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 disclosure described and / or claimed herein.

[0064] like Figure 2 As shown, the device 200 includes a computing unit 201, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 202 or a computer program loaded from a storage unit 208 into a random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the device 200 can also be stored. The computing unit 201, the ROM 202, and the RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0065] A number of components in the device 200 are connected to the I / O interface 205, including: an input unit 206, such as a keyboard, a mouse, etc.; an output unit 207, such as various types of displays, speakers, etc.; a storage unit 208, such as a disk, an optical disk, etc.; and a communication unit 209, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 209 allows the device 200 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0066] The computing unit 201 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 201 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, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 201 performs the various methods and processes described above, such as the secondary cable insulation mutation detection method. For example, in some embodiments, the secondary cable insulation mutation detection method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 208. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 200 via the ROM 202 and / or the communication unit 209. When the computer program is loaded into the RAM 203 and executed by the computing unit 201, one or more steps of the secondary cable insulation mutation detection method described above may be performed. Alternatively, in other embodiments, the computing unit 201 may be configured to perform the secondary cable insulation mutation detection method in any other appropriate manner (e.g., by means of firmware).

[0067] Various implementations of the systems and techniques described above herein can 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can 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.

[0068] The program code for implementing the method disclosed in the present invention 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 as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0069] In the context disclosed by the present invention, 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.

[0070] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0071] The systems and techniques described herein may be implemented in a computing system that includes backend 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 frontend 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 backend components, middleware components, or frontend components. The components of the system may 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), a blockchain network, and the Internet.

[0072] 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 between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0073] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in the present invention can be achieved, and this document is not limited here.

[0074] What is disclosed above is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of rights of the present invention. A person skilled in the art can understand that all or part of the processes of the above embodiments and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A secondary cable insulation mutation detection method, characterized in that: include: Step S1, establishing a detection model, which is used for data input, data grouping and sorting processing, feature quantity calculation and judgment conclusion output; Step S2: Establish an insulation data set {R1, R2, ..., R N }; Step S3, based on the insulation data set, using the detection model to perform secondary cable insulation mutation detection, the detection model first groups the insulation data set, then generates a data sequence sorted by numerical value, and further calculates a feature value based on each data sequence, and draws a conclusion on whether there is currently an insulation mutation based on a comparison result of the feature value and a discrimination threshold; Step S4: When the detection result of the detection model indicates that there is currently an insulation mutation, an insulation mutation alarm is issued.

2. The secondary cable insulation mutation detection method according to claim 1, characterized in that: The step S3 uses the detection model to perform secondary cable insulation mutation detection, including: Step S31, setting parameters, including the minimum data window coefficient n, data length N, and discrimination threshold B, where n <N / 2; Step S32, obtaining insulation data {R1, R2, ..., R N }; Step S33, initializing parameter k, setting k=n; Step S34: for the data {R1, R2, ..., R k }, {R k+1 ,R k+2 ,...,R N } are sorted from small to large, and a new sequence {D1,D2,...,D k }, {D k+1 ,D k+2 ,...,D N }; Step S35, obtaining parameters L and R, the expressions are: Step S36, calculate feature quantity A k , A k The expression is: Step S37, determine whether k == n is currently satisfied; if not, proceed to step S38; if satisfied, proceed to step S39; Step S38, determine whether A is currently satisfied k >A max Among them, A max is the maximum eigenvalue. If it is satisfied, continue to execute step S39; if it is not satisfied, continue to execute step S310; Step S39, update the maximum eigenvalue A max , maximum value position k Amax : A max =A k k Amax =k Step S310, determine whether k<Nn is satisfied, if so, continue to step S311, if not, continue to step S312; Step S311, update coefficient k, set k=k+1; and return to execute step S34; Step S312, determine whether A is currently satisfied max >B; if satisfied, it is determined that there is currently an insulation mutation; if not satisfied, it is determined that there is currently no insulation mutation.

3. An electronic device, characterized in that: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1 to 2.

4. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-2.

5. A computer program product, characterized in that The invention comprises a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 2.

6. A secondary cable insulation mutation detection system, characterized in that: It comprises the electronic device as claimed in claim 3 and an insulation monitoring device, wherein the insulation monitoring device is used to collect insulation data of the secondary cable according to a preset sampling frequency and provide the insulation data to the electronic device.

7. The secondary cable insulation mutation detection system according to claim 6, characterized in that: The collection method of the insulation monitoring device is that the secondary cable passes through the leakage current sensor of the insulation monitoring device, and the insulation monitoring device obtains the leakage current information and converts it into an insulation resistance value.

8. The secondary cable insulation mutation detection system according to claim 7, characterized in that: It also includes a cloud server, and the electronic device sends the detection result to the cloud server, and the cloud server is used to provide the detection result to the device with query permission.

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