JP2020030125A - Abnormality diagnosis device and abnormality diagnosis method - Google Patents

Abstract

To provide an abnormality diagnosis device and an abnormality diagnosis method for calculating the size of a gap from a partial discharge signal and diagnosing abnormality of a power cable, namely power equipment, from the size of the gap.SOLUTION: An abnormality diagnosis device includes: a measurement signal reception part for receiving a partial discharge signal as measurement data from a sensor for detecting the partial discharge signal of a power cable having an insulation medium; and an abnormality determination part for calculating an index value of the size of a gap formed in the insulation medium from a voltage phase and the amount of partial discharge electric charge derived from the measurement data received by the measurement signal reception part and executing an abnormality diagnosis of the power cable.SELECTED DRAWING: Figure 2

Description

  The present invention relates to a power device abnormality diagnosis apparatus and abnormality diagnosis method.

  Diagnosis of abnormalities in electric power equipment is important for stable operation of electric power systems and electric power systems within a predetermined area (such as a factory). Many power devices have a long life, and the environment used by each power device is different. In addition, since the deterioration mechanism of a power device is affected by various factors, it is generally difficult to diagnose abnormality of the power device. For this reason, various diagnosis algorithms and measurement sensors have been proposed, verified, and operated for abnormality diagnosis of power devices.

  As a background art in this technical field, there is Japanese Patent No. 2866533 (Patent Document 1). This Patent Document 1 aims to provide a high-frequency partial discharge detection system that can be applied to constant monitoring of a power cable and that does not require skill in determining the deterioration state of the power cable. A high-frequency partial discharge sensor composed of an electrode disposed on the peripheral surface of the device and an inductance element connected to the electrode, and processing a spectrum of a signal output from the high-frequency partial discharge sensor as data to obtain a time and a charge amount. Spectrum analysis means for obtaining a distribution chart showing a relationship between at least two elements selected from the group consisting of occurrence frequency and phase, and a void in the cable insulation section based on a pulse form of a signal output from the partial discharge sensor. And means for estimating the shape of

  Japanese Patent Application Laid-Open No. 5-80112 (Patent Document 2) is a background art of this technical field. In this patent document 2, a partial discharge sensor is provided on a peripheral surface of a cable for the purpose of providing a power cable abnormality diagnosis device for automatically detecting occurrence of an abnormality in a connection portion of the power cable after installation. The signal output from the sensor is processed by a signal processing unit and then supplied to a buffer memory, and the data processing unit performs a φ-q distribution ( Alternatively, it is described that an abnormality in the insulating portion of the cable is detected by an increase in the peak value of the partial discharge pulse near the zero cross of the applied voltage in the (φ-qn distribution) (see the summary).

Japanese Patent No. 2866533 JP-A-5-80112

  Patent Literature 1 and Patent Literature 2 disclose a high-frequency partial discharge detection system that determines deterioration of a power cable and a power cable abnormality diagnosis device that detects an abnormality of the power cable. However, Patent Literature 1 and Patent Literature 2 do not describe calculating the size of a gap from a partial discharge signal and performing abnormality diagnosis of a power cable, that is, a power device, from the size of the gap. .

  Therefore, the present invention provides an abnormality diagnosis device and an abnormality diagnosis method for calculating the size of a gap from a partial discharge signal and performing an abnormality diagnosis of a power cable, that is, a power device based on the size of the gap.

  In order to solve the above problem, an abnormality diagnosis device of the present invention includes a measurement signal receiving unit that receives a partial discharge signal as measurement data from a sensor that detects a partial discharge signal of a power cable having an insulating medium, and a measurement signal receiving unit. An abnormality determining unit that calculates an index value of the size of the gap formed in the insulating medium from the voltage phase and the partial discharge charge amount derived from the measurement data received, and performs an abnormality diagnosis of the power cable. Have

  ADVANTAGE OF THE INVENTION According to this invention, the magnitude | size of a gap is calculated from a partial discharge signal, and the abnormality diagnosis apparatus and abnormality diagnosis method which perform abnormality diagnosis of a power cable, ie, a power device, from the magnitude | size of this gap can be provided. .

  Problems, configurations, and effects other than those described above will be apparent from the following description of the embodiments.

FIG. 2 is an explanatory diagram illustrating a system configuration according to the embodiment. FIG. 3 is an explanatory diagram illustrating a functional configuration of the embodiment. FIG. 3 is an explanatory diagram illustrating a hardware configuration of the present embodiment. FIG. 9 is an explanatory diagram showing an execution procedure for diagnosing an abnormality in the embodiment. FIG. 4 is an explanatory diagram illustrating voltage phase characteristics according to the present embodiment. FIG. 4 is an explanatory diagram illustrating a relationship between a discharge starting voltage and a partial discharge according to the present embodiment. It is explanatory drawing which shows the shape estimation of the gap of a present Example. FIG. 9 is an explanatory diagram illustrating an execution procedure for calculating an index value according to the embodiment. It is an explanatory view showing the degree of abnormality of this example. FIG. 5 is an explanatory diagram illustrating phase estimation according to the embodiment.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings. The same components are denoted by the same reference numerals, and in cases where the description is duplicated, the description may be omitted.

  Aged power cables are concerned about insulation breakdown, and insulation breakdown may lead to damage such as power outages. In the present embodiment, such dielectric breakdown of the power cable is detected, and abnormality diagnosis is performed. In a power cable having an insulating medium (hereinafter, referred to as an “insulator”), when a gap (an oil gap, a void, or the like) is formed inside the insulator, a partial discharge occurs. This partial discharge is detected as a signal, and abnormality diagnosis of the power cable is performed using the partial discharge signal detected as a signal of the partial discharge.

  Partial discharge carbonizes the insulator, leading to dielectric breakdown. Therefore, since partial discharge is a cause and a sign of dielectric breakdown, it is important to construct an abnormality diagnosis algorithm based on the partial discharge signal. In the present embodiment, an index value of the thickness of the gap is calculated from the relationship between the voltage phase derived from the partial discharge signal and the amount of the partial discharge charge, and then an index value of the area of the bottom surface of the gap is calculated. Diagnose abnormalities based on index values.

  Thereby, abnormality diagnosis can be performed based on the partial discharge signal measured from the power cable. Further, from the result of the abnormality diagnosis, it is possible to predict a period until the insulation breakdown of the power cable, and take appropriate measures such as updating and maintenance of the power cable. In addition, for a plurality of power cables, it can be utilized for advanced asset management such as determining the priority of a measure according to the cost. For example, the budget of an operator such as a power device, a power system, and a user can be smoothed by taking measures such as changing the timing of updating the power cable according to the result of the abnormality diagnosis.

  In addition, according to the present embodiment, it is possible to estimate the shape of the gap, and to combine the estimated gap shape (size) with the knowledge of the operator to quickly and reliably diagnose abnormalities. Can be implemented.

  In the present embodiment, the voltage phase can be estimated from the periodicity of the measured partial discharge signal, and the installation of the voltage sensor can be omitted.

  FIG. 1 is an explanatory diagram illustrating the system configuration of the present embodiment. FIG. 1 shows a system configuration for performing abnormality diagnosis of the power cable 100.

  In the present embodiment, an abnormality diagnosis of the power cable 100 is performed as one of the abnormality diagnosis of the power device.

  The power cable 100 described in the present embodiment is used for, for example, a power grid (a power transmission network or a power distribution network) or a power transmission network or a power distribution network in a predetermined area (a large-scale customer such as a factory, a building, a commercial facility, or a station). Power cable.

  The power cable 100 transmits power. The power cable 100 has an insulator. As the mounting of the power cable 100, an OF (oil filled) (oil immersion paper) cable and a CV (crosslinked polyethylene) cable are exemplified.

  In this embodiment, the partial discharge signal of the power cable 100 is measured and the abnormality diagnosis of the power cable 100 is performed. However, if the power device can measure the partial discharge signal and perform the abnormality diagnosis, the present embodiment Examples can be applied. Examples of such power devices include circuit breakers, transformers, disconnectors, and the like.

  The sensor 101 is a sensor device that measures a partial discharge signal of the power cable 100, and is, for example, a measurement sensor that measures a partial discharge signal of the power cable 100. An example is a high-frequency CT (Current Transformer). The high-frequency CT can sample a high-frequency signal (hereinafter, sometimes referred to as a partial discharge signal, measurement data, or a measurement signal), and can sample a frequency of 100 MHz or more.

  The measurement / diagnosis device 102 executes each processing (processing related to abnormality diagnosis using measurement data) such as data processing, abnormality diagnosis, and data recording on the measurement data transmitted from the sensor 101. As the implementation of the measurement and diagnosis device 102, a personal computer, a field device in edge computing, a gateway in fog computing, a controller, a PLC (Programmable Logic Controller), an IED (Intelligent Electronic Device), an MU (Merging Unit), a protection relay, and the like. Is exemplified.

  Note that the measurement and diagnosis device 102 described in the present embodiment is an abnormality diagnosis device, and the abnormality diagnosis device may be the measurement and diagnosis device 102 in some cases.

  The coaxial cable 103 is a cable for transmitting measurement data from the sensor 101 to the measurement diagnostic device 102. Since the measurement data is a high-frequency signal, the coaxial cable 103 is generally used.

  The storage 104 stores the result of the abnormality diagnosis transmitted from the measurement / diagnosis device 102, the measurement data, information related to the abnormality diagnosis of the power cable 100, and the like. As information related to the abnormality diagnosis of the power cable 100, the material of the power cable 100, dimensions of each part (cable diameter, thickness of an insulator, etc.), parameters of electric characteristics (electric resistance, etc.), weight, cable length, manufacturing year The date, the installation date, and the operation information of the power cable 100 are exemplified. Examples of the operation information of the power cable 100 include a supply voltage, a tide flow, a past operation result (period, etc.), a power failure history, a connection configuration on the power system, and a connection relationship with another power system.

  As a mounting form of the data storage function in the storage 104, a database, a file server, a storage device, a NAS (Network Attached Storage), a SAN (Storage Area Network), a nonvolatile storage medium, and an external storage medium are exemplified. Examples of the nonvolatile storage medium include a hard disk drive (HDD), a solid state drive (SSD), and a flash memory. Examples of the easily removable external storage medium include a floppy disk (FD), a compact disk (CD), a digital video disk (DVD), and a USB (Universal Serial Bus) memory. When these external storage media are used, it is exemplified that the measurement and diagnosis device 102 has a dedicated connection interface.

  The server 105 comprehensively uses information transmitted from one or more measurement / diagnosis devices 102 and information accumulated in the storage 104 (results of abnormality diagnosis, measurement data, information related to abnormality diagnosis of the power cable 100, and the like). An abnormality diagnosis of the power cable 100 is performed. For example, sharing information with an operator, a central power supply dispatching station, a control station, a power supply station, a substation, and the like for a comprehensive abnormality diagnosis is exemplified.

  As the implementation of the server 105, a personal computer, server, workstation, cloud computing, field device in edge computing, gateway in fog computing, SCADA (Supervisory Control and Data Acquisition), control controller, PLC, IED, MU, protection relay Etc. are exemplified. A plurality of servers 105 may exist due to differences in processing contents and distribution of processing loads. The same applies to the storage 104.

  The network 106 is a communication network for transmitting information between each of the measurement and diagnosis apparatus 102, the storage 104, and the server 105.

  Examples of the implementation of the network 106 include IEEE 802.3, various industrial networks, IEC (International Electrotechnical Commission) 61784, IEC 61158, IEC 61850 (including IEC 61850-90-3), IEC 62439 and IEC 61850-. 7-420, IEC 60870-5-104, DNP (Distributed Network Protocol) 3, IEC 61970, IEEE 802.1 AVB, DeviceNet, RS-232C, RS-422, RS-485, IEEE 802.15, IEEE 802. 1. Mobile communication, communication rules used for power equipment control such as demand response and VPP (virtual power plant) Case etc. are illustrated.

  The communication format for transmitting information between the measurement / diagnosis device 102, the storage 104, and the server 105 also follows the above protocol.

  Further, as communication protocols of the application layer, web, HTTP (Hypertext Transfer Protocol), XML (Extensible Markup Language), SOAP (Soap), RPC (Remote Procedure Call), SQL (File Electronic), SQL (File Electronic), etc. May be used.

  Although the measurement and diagnosis apparatus 102 has been described as being connected to the network 106, the measurement and diagnosis apparatus 102 may exist alone without being connected to the network 106. At this time, a data storage function corresponding to the storage 104 may be provided inside the measurement and diagnosis apparatus 102.

  As an implementation form of the data storage function inside the measurement and diagnosis apparatus 102, an implementation such as a database, a file server, a storage device, a NAS, a SAN, a nonvolatile storage medium, and an external storage medium is exemplified. Examples of the nonvolatile storage medium include an HDD, an SSD, and a flash memory. In addition, as an easily removable external storage medium, use of an FD, CD, DVD, USB memory, or the like is exemplified. When these external storage media are used, it is exemplified that the measurement and diagnosis device 102 has a dedicated connection interface.

  FIG. 2 is an explanatory diagram illustrating a functional configuration of the present embodiment. FIG. 2 shows a functional configuration of the measurement and diagnosis apparatus 102.

  The measurement signal receiving unit 113 receives measurement data from the sensor 101 (measurement data inside the measurement and diagnosis device 102 is described as a “measurement signal”). That is, the measurement signal receiving unit 113 receives the partial discharge signal from the sensor 101 as a measurement signal. As the implementation of the measurement signal receiving unit 113, a CPU (Center Processing Unit), a microcomputer, a specific hardware function module inside the CPU or the microcomputer, an FPGA (Field Programmable Gate Array), a CPLD (Complex Programmable Logic Device), a dedicated An IC (Integrated Circuit) and an analog / digital conversion circuit are exemplified.

  The abnormality determination unit 112 performs abnormality diagnosis of the power cable 100 based on the measurement signal received by the measurement signal receiving unit 113. Alternatively, it is determined whether or not the received measurement signal should be stored. The result of the abnormality diagnosis, the received measurement signal and the predetermined calculation result are transmitted to the storage unit 117 or the communication unit 114. That is, the abnormality determination unit 112 calculates the index value of the size of the gap formed in the insulator of the power cable 100 from the voltage phase and the partial discharge charge amount derived from the measurement signal received by the measurement signal receiving unit 113. After the calculation, the abnormality diagnosis of the power cable 100 is performed. The abnormality diagnosis method in the abnormality determination unit 112 will be described later.

  The communication unit 114 is a functional unit that receives a command or a setting packet to the measurement / diagnosis device 102 via the network 106 and transmits a command or a setting packet from the measurement / diagnosis device 102 to the storage 104 or the server 105. It is shaped and transmitted according to the protocol format of the network 106 and transmitted.

  In addition, the communication unit 114 causes the other measurement / diagnosis devices 102, the storage 104, and the server 105 to execute processing within a certain period of time (for example, processing for storing measurement signals in the other measurement / diagnosis devices 102). It is exemplified to provide a real-time communication system that guarantees a delay.

  The power device information storage unit 115 stores various information (information related to the abnormality diagnosis of the power cable 100) used when the abnormality determination unit 112 performs the abnormality diagnosis. Examples of information to be stored include the material of the power cable 100, dimensions of each part, parameters of electrical characteristics, weight, cable length, manufacturing date, laying date, and operation information of the power cable 100. Examples of the operation information of the power cable 100 include a supply voltage, a tide flow, a past operation result, a power failure history, a connection configuration on a power system, and a connection relationship with another power system. In addition, when an OF cable is used as the power cable 100, oil pressure information (for example, a history of oil pressure) of the OF cable is also stored and can be used for abnormality diagnosis.

  Note that an example of the implementation of the power device information storage unit 115 is an HDD, an SSD, and a flash memory. FDs, CDs, DVDs, USB memories, and the like are examples of easily removable external storage media.

  The cycle detection unit 116 detects an AC cycle (cycle) of the voltage applied to the power cable 100 and sends a start time of the AC cycle (cycle), a zero-cross timing, and the like to the measurement signal receiving unit 113 and other functional units. Notice.

  The storage unit 117 stores a result of the abnormality diagnosis transmitted from the abnormality determination unit 112, information related to the abnormality diagnosis of the power cable 100, and the like. Examples of information related to the abnormality diagnosis of the power cable 100 include the material of the power cable 100, dimensions of each part, parameters of electrical characteristics, weight, cable length, manufacturing date, laying date, and operation information of the power cable 100. Is done. Examples of the operation information of the power cable 100 include a supply voltage, a tide flow, a past operation result, a power failure history, a connection configuration on a power system, and a connection relationship with another power system.

  The time synchronization unit 118 synchronizes the time of the measurement and diagnosis apparatus 102 with one or more of the other measurement and diagnosis apparatuses 102, the storage 104, and the server 105 according to a predetermined time synchronization protocol. At this time, examples of the time synchronization means include a system such as a global positioning system (GPS), a network time protocol (NTP), an IEEE 1588, and an interrange instrumentation group (IRIG) -B. The time system to be synchronized may be a relative time when the system described in FIG. 1 is operated in a closed state, but an absolute time is preferable when the system is linked to an external system.

  The phase estimating unit 119 estimates the voltage phase from the periodicity of the partial discharge charge amount derived from the measured measurement signal.

  The functional units of the abnormality determination unit 112, the communication unit 114, the cycle detection unit 116, the time synchronization unit 118, and the phase estimation unit 119 are a CPU, a microcomputer, a specific hardware function module inside the CPU or the microcomputer, It is exemplified that it is implemented by hardware such as an FPGA, a CPLD, and a dedicated IC. Each of these functional units may be configured by a single device or IC, or some functional units may be configured inside one device or IC.

  FIG. 3 is an explanatory diagram illustrating the hardware configuration of the present embodiment. FIG. 3 shows a hardware configuration of the measurement and diagnosis apparatus 102.

  The CPU 120 transmits the program from the nonvolatile storage medium 125 to the memory 124 and executes the program. Examples of the execution processing program include an operating system (hereinafter, referred to as an OS) and an application program operating on the OS. As the CPU 120, a multi-core CPU, a many-core CPU, or a CPU with a built-in FPGA may be used.

  A LAN (Local area network) 121 receives a transmission request from software running on the CPU 120 and transmits the request to the network 106. The LAN 121 transmits the measurement data received from the network 106 to the CPU 120, the memory 124, and the nonvolatile storage medium 125 via the bus 126.

  Examples of the implementation of the LAN 121 include an IC such as an FPGA, a CPLD, an Application Specific Integrated Circuit (ASIC), and a gate array. Alternatively, it may be configured integrally with the CPU 120.

  The LAN 121 may be an IEEE 802.3 communication device including a MAC (Media Access Control) layer and a PHY (Physical layer) (physical) layer, or the LAN 121 may include a PHY function.

  In this case, the implementation of the LAN 121 includes a MAC chip and a PHY chip conforming to the IEEE 802.3 standard, and a composite chip of MAC and PHY. The LAN 121 may be included in a chipset that controls an information path inside the CPU 120 or the computer.

  The data processing IC 122 processes the measurement data transferred from the ADC (analog to digital converter) 123 by using hardware, and transmits the processing result via the bus 126 to the CPU 120, the memory 124, the nonvolatile storage medium 125, and the LAN 121. To send to one or more. Further, the data processing IC 122 may be set by access from the CPU 120, the LAN 121, or the like. Examples of the information to be set include circuit configuration information (configuration data) as an IC (FPGA, CPLD, etc.) and parameters relating to processing of measurement data.

  Examples of the implementation of the data processing IC 122 include an FPGA, a CPLD, a general-purpose IC, a dedicated ASIC, a gate array, an FPGA with a built-in processor, and software operating on them. Further, the circuit configuration information in the case where the circuit configuration is implemented by the FPGA and the CPLD may be stored in the non-volatile storage medium 125 and read as needed. Note that the nonvolatile storage medium 125 for storing circuit configuration information may be directly connected to the data processing IC 122 using the dedicated nonvolatile storage medium 125 without connecting to the bus 126.

  The ADC 123 is an analog / digital conversion circuit that converts an analog signal transmitted from the sensor 101 into a digital signal and transmits the digital signal to the data processing IC 122. Note that a plurality of ADCs 123 may be provided corresponding to the plurality of sensors 101. At this time, the partial discharge signal may be measured by shifting the respective capture phases. By doing so, it is possible to measure a high-frequency partial discharge signal even if the single sampling frequency of the ADC 123 has a low resolution. A single ADC 123 may correspond to a plurality of sensors 101, or a single ADC 123 may correspond to a single sensor 101. Similarly, a single data processing IC 122 may correspond to a plurality of ADCs 123, or a single data processing IC 122 may correspond to a single ADC 123.

  The memory 124 is a temporary storage area for the CPU 120 to operate, and stores the OS transmitted from the non-volatile storage medium 125, application programs operating on the OS, and the like. Examples of the memory 124 include a static RAM (Random Access Memory), a DRAM (Dynamic RAM), and an NVRAM (Non Volatile RAM).

  The non-volatile storage medium 125 is a storage medium for information, and is used for storing an OS, an application, a device driver, a program for operating the CPU 120, and a result of executing the program. Examples of the non-volatile storage medium 125 include an HDD, an SSD, and a flash memory. In addition, as an easily removable external storage medium, use of an FD, CD, DVD, USB memory, or the like is exemplified.

  The bus 126 connects the CPU 120, the LAN 121, the memory 124, and the nonvolatile storage medium 125, respectively. Examples of the bus 126 include a PCI (Peripheral Component Interconnect) bus, an ISA (Industry Standard Architecture) bus, a PCI Express bus, a system bus, a memory bus, and an on-chip bus.

  FIG. 4 is an explanatory diagram illustrating an execution procedure for diagnosing an abnormality in the present embodiment. FIG. 4 shows the procedure of the abnormality diagnosis.

  The operation of the measurement and diagnosis apparatus 102 according to the present embodiment will be described. In the present embodiment, in the voltage phase characteristic derived from the measured partial discharge signal, the shape (size) of the gap is estimated from the voltage phase and the partial discharge charge amount, and abnormality diagnosis is performed based on the estimation result. I do.

  First, it is determined whether or not the start of the cycle of the AC cycle of the voltage for measuring the partial discharge signal is standby (S001). For example, this is the starting point of the AC cycle of the power system. If it is standby (Y of S001), the process returns to the starting point of S001, and if it is not standby, the process proceeds to (N of S001).

  Second, a partial discharge signal in a predetermined cycle (measurement period) is measured (S002). The measurement of the partial discharge signal records the partial discharge charge amount and the voltage phase. The predetermined cycle is, for example, one AC cycle or a plurality of AC cycles. Here, the partial discharge charge amount is a charge amount in a partial discharge signal when a gap occurs in the insulator of the power cable, a partial discharge signal is measured, and the partial discharge signal is measured.

  Third, it is determined whether a partial discharge signal has been measured in a predetermined cycle (S003). If the measurement has not been performed (N in S003), the process returns to the starting point of S001, and if the partial discharge signal has been measured, the process proceeds to (Y in S003).

  Fourth, an evaluation value for diagnosing abnormality is calculated (S004). The abnormality diagnosis method will be described later.

  Fifth, it is determined whether the evaluation value is larger than the threshold value (S005). Note that the threshold value is determined in advance based on results obtained through experiments, past cases, and the like, and stores a partial discharge charge amount with respect to a voltage phase.

  Sixth, if the evaluation value is larger than the threshold value, it is diagnosed as "abnormal" (S006). This diagnosis is determined depending on the evaluation value and the determination method, and it is exemplified that when the threshold value is equal to, smaller than the threshold value, when the threshold value is included, and when the threshold value is not included, the abnormality is diagnosed. You.

  Seventh, the abnormality is notified to the outside (S007). This is done by notifying the storage 104 or the server 105 that the abnormality has been diagnosed via the network 106, displaying the measurement / diagnosis device 102 on its own screen, lighting up an LED (light emitting diode) or the like, or Outputting an alarm such as a warning sound is exemplified.

  Finally, it is determined whether or not to end (S008). The end condition is, for example, whether or not a specific date and time, that the storage capacity of the measurement data in the measurement and diagnosis apparatus 102 satisfies a predetermined condition (full or reaches a predetermined ratio), and the like. If it is not finished (N of S008), the process returns to S001.

  Note that the measurement data may be output after confirming that the measurement in S002 has been performed correctly. This is because there is a possibility of an abnormality in the measurement device (sensor 101) or ADC 123, or a soft error in an FPGA or the like used for the data processing IC 122. When such measurement abnormality is found, invalidating the measurement data is exemplified. Alternatively, the fact that measurement could not be performed may be stored. Further, whether the abnormality is a temporary abnormality or a permanent abnormality may be determined and notified to the outside. For example, the abnormality is stored in the storage unit 117 or the storage 104.

  FIG. 5 is an explanatory diagram showing the voltage phase characteristics of the present embodiment. FIG. 6 is an explanatory diagram showing the relationship between the discharge starting voltage and the partial discharge in the present embodiment. FIG. 7 is an explanatory diagram illustrating the shape estimation of the gap according to the present embodiment. These figures show an abnormality diagnosis method.

  As shown in FIG. 5, first, a phase characteristic is created from the relationship between the measured voltage phase (voltage phase) (dotted line) and the partial discharge charge amount (solid line). In the present embodiment, the shape (size) of the gap or the evaluation value (hereinafter, referred to as an index value) is estimated from this voltage phase characteristic, and abnormality diagnosis is performed.

  Then, as shown in FIG. 6, the occurrence of the partial discharge is related to the discharge starting voltage. This is because partial discharge occurs when the voltage applied to the power cable 100 exceeds the discharge start voltage. In this embodiment, attention is paid particularly to this discharge starting voltage.

  Note that the discharge starting voltage is a measurement point of measurement data in a small voltage phase among measurement points of measurement data indicating the same partial discharge charge amount in a set of measurement points of measurement data.

  That is, in this embodiment, the index value of the size of the gap formed in the insulator of the power cable 100 is calculated using the discharge starting voltage in the voltage phase derived from the measurement data.

  At this time, the firing voltage is proportional to the thickness of the gap (gap length) according to Paschen's law. Therefore, an index value of the thickness of the gap can be obtained from the voltage phase at which the partial discharge is measured (the phase of the firing voltage). Here, the reason why the index value is used is that the measurement data indicating the partial discharge charge amount attenuates depending on the position where the partial discharge occurs.

  Further, the measured partial discharge charge amount can be considered to have a correlation with the volume of the gap (knowledge). In the present embodiment, attention is paid to this finding. The index value is a relative value. In particular, it is preferable to store a predetermined correlation in advance with respect to the correlation between the amount of partial discharge charge and the volume of the gap.

  Then, as shown in FIG. 7, by dividing the measured partial discharge charge amount by an index value of the gap thickness to approximate the gap (void) to a rectangular parallelepiped, the gap thickness is obtained. The index value of the area S of the bottom surface of the gap in the direction d can be calculated. Thereby, the size of the gap (thickness and area of the bottom surface) can be calculated.

That is, in this embodiment, the index value of the area of the bottom surface of the gap orthogonal to the thickness of the gap is calculated from the index value of the gap thickness and the partial discharge charge amount.
When the power cable 100 breaks down, the size of the gap has a particularly large effect.

  That is, in the present embodiment, the size of the gap is an index value of the thickness of the gap and / or an index value of the area of the bottom surface of the gap.

  As described above, the abnormality diagnosis method described in the present embodiment uses the voltage phase characteristic generated from the partial discharge signal. The index value of the thickness of the gap is calculated from the voltage phase of the measured partial discharge charge amount, the index value of the area of the bottom surface of the gap is calculated from the index value of the partial discharge charge amount and the index value, and these index values are calculated. This is used to perform abnormality diagnosis.

  That is, the abnormality diagnosis method described in the present embodiment detects the partial discharge signal of the power cable 100 having the insulating medium, receives the partial discharge signal as measurement data, and compares the voltage phase derived from the received measurement data with the voltage phase. An index value of the size of the gap formed in the insulating medium is calculated from the partial discharge charge amount, and abnormality diagnosis of the power cable 100 is performed.

  FIG. 8 is an explanatory diagram illustrating an execution procedure for calculating an index value according to the present embodiment.

  First, a voltage phase (a position on the x-axis) that appears first is extracted from measurement data (a value on the y-axis) of the same partial discharge charge amount (S010). The reason why the measurement data appears first is that the second and subsequent measurement data are considered to be measurement data generated each time the AC cycle of the voltage exceeds the discharge start voltage in the same gap.

  Next, an index value of the gap thickness is calculated from the voltage phase (x-axis value) of the measurement point of the measurement data extracted in S010 (S011).

  Finally, an index value of the area of the bottom surface of the gap is calculated from the partial discharge charge amount (y-axis value) of the same measurement data and the index value of the gap thickness obtained in S011 (S012).

  By the above procedure, the index value of the shape (size) of the gap, such as the thickness of the gap (hereinafter, referred to as the thickness) and the area of the bottom surface of the gap (hereinafter, referred to as the area), is calculated. In this embodiment, abnormality diagnosis is performed based on these index values.

  For example, it is exemplified that when the thickness exceeds a predetermined threshold, it is diagnosed as abnormal. Alternatively, it is exemplified that an abnormality is diagnosed when the area exceeds a predetermined threshold. In addition, the thickness and the area are each multiplied by a predetermined weighting coefficient, the sum is calculated, and when the sum exceeds a predetermined threshold value, an abnormality is diagnosed. At this time, the weight multiplied by the thickness and the area may be determined by a statistical method such as machine learning from the results obtained by experiments, past cases, and the like.

  In addition, abnormality diagnosis may be performed based on a time change (time-dependent change) of these index values. At regular intervals, the index value between the thickness and the area is calculated, or the time when the index value between the thickness and the area is calculated or the interval between the calculated times is recorded and the abnormality diagnosis is performed based on the change. Is exemplified. For example, if the degree of change exceeds a threshold, it is diagnosed as abnormal.

  The abnormality diagnosis may be performed based on the distribution of the gap. Assuming that a gap exists for each piece of partial discharge charge measurement data, an index value of thickness or area is calculated for each piece of partial discharge charge measurement data. Since this corresponds to a plurality of gaps, performing abnormality diagnosis based on the distribution of the gaps is exemplified.

  That is, in the present embodiment, the index value is calculated for a plurality of measurement points having different partial discharge charge amounts in the measurement data, the distribution of the index value of the gap size at the plurality of measurement points is estimated, and the estimated distribution is calculated. The abnormality diagnosis may be performed based on

  For example, when it is assumed that there are a plurality of gaps, a plurality of measurement points are classified according to the difference in thickness, and when the number of gaps of the class is larger than a predetermined number, it is diagnosed as abnormal. Alternatively, a weighting factor is determined for each class, and the sum of a value obtained by multiplying the number of gaps included in each class by the weighting factor is obtained. The classification may be performed not for each thickness but for each area.

  Further, abnormality diagnosis may be performed based on a temporal change of these distributions. For example, based on a change in the number of classes is exemplified. Assuming that the number of classes exceeds a predetermined number, and thereafter, a set of gaps is combined, an example of diagnosing an abnormality when the number of classes becomes smaller than the predetermined number is exemplified. The above-mentioned weighting coefficient may be dynamically changed based on the number of classes, the change thereof, or the elapsed time of measurement.

  That is, in the present embodiment, it is also possible to classify the index values of the gap size at a plurality of measurement points into classes, and to perform abnormality diagnosis based on the number of index values of each class.

  FIG. 9 is an explanatory diagram illustrating the degree of abnormality in the present embodiment.

  The abnormality diagnosis method described in the present embodiment is an embodiment for diagnosing whether or not there is an abnormality. However, the abnormality diagnosis method may be configured to output the degree of abnormality (degree of abnormality). In such a case, in the above-described configuration for comparison with the threshold value, an example is given in which the calculated value based on the index value of the shape (size) is directly shown as the degree of abnormality.

  As a method of displaying such a degree of abnormality to the operator, a numerical value may be indicated. Further, as shown in FIG. 9, the time t may be taken on the x-axis and the degree of abnormality may be shown on the vertical axis.

  Any one of the abnormality diagnosis methods may be used, or a plurality of abnormality diagnosis methods may be applied. When a plurality of abnormality diagnosis methods are applied, each abnormality diagnosis method is indexed (for example, a difference from a threshold value), and a value obtained by adding the index value of each abnormality diagnosis method is compared with a predetermined threshold value. May be. Alternatively, when an abnormality is diagnosed in any of the abnormality diagnosis methods, the abnormality may be diagnosed, or when a predetermined number or more of the abnormality diagnosis methods have been diagnosed as abnormal, the abnormality may be diagnosed.

  Further, a plurality of abnormality diagnosis methods may be applied in parallel or may be applied in order. Alternatively, the abnormality diagnosis method may be applied hierarchically. For example, first, it is applied to an abnormality diagnosis regarding the thickness of the gap. If an abnormality is diagnosed here, it is applied to an abnormality diagnosis relating to the area of the bottom surface of the gap. Alternatively, if no abnormality is diagnosed based on the thickness of the gap, an abnormality diagnosis method for the number of classes classified based on the thickness of the gap is applied. This aims at sequentially changing the index to be diagnosed by applying the abnormality diagnosis method hierarchically.

  In addition, knowledge on the shape (size) of the gap can be used. In other words, a specific shape pattern set diagnosed as abnormal (an operator's past knowledge) may be defined, and the abnormality may be diagnosed in combination with the output information on the shape (size) of the gap. By dividing the functions in this way, the knowledge and know-how of the operator can be utilized.

  When the diagnosis target is an OF cable, the abnormality diagnosis may be performed in cooperation with the oil pressure information of the OF cable. For example, when the oil pressure is low, it is considered that the oil gap is likely to develop and become abnormal. Therefore, it is exemplified to lower the threshold value of the abnormality diagnosis and increase the weight of the calculation of the abnormality degree. You.

  Further, it is exemplified that the hydraulic pressure information is used as the reliability of the determination of the degree of abnormality. For example, when the oil pressure is low, it is considered that an abnormality is likely to occur. Therefore, when the degree of abnormality is high in such a case, the reliability is determined to be high. Conversely, when the oil pressure is within the normal range, it is considered that the oil pressure is unlikely to be abnormal. Therefore, when the degree of abnormality is low, the reliability is determined to be high.

  That is, in this embodiment, when the power cable is an OF cable, the reliability of the index value can be output based on the oil pressure information of the OF cable.

  As described above, in addition to the abnormalities, the reliability is quantified and displayed in addition to the visualization means as shown in FIG.

  Further, abnormality diagnosis can be performed in cooperation with the operation information of the power system. Similarly to the oil pressure information, the operation information of the power system may be used for determining the degree of abnormality and reliability. For example, the tide flow and its integrated value may be reflected in the determination of the degree of abnormality and the calculation of the degree of reliability.

  FIG. 10 is an explanatory diagram illustrating the phase estimation according to the present embodiment. FIG. 10 illustrates a case where the voltage phase cannot be measured.

  In the abnormality diagnosis method illustrated in FIG. 8, the index value is calculated based on the voltage phase. However, there may be a case where the voltage phase cannot be measured due to a setting restriction of the measurement sensor 101 (for example, additional installation is impossible). In such a case, it is expected that the pattern of the measurement data of the partial discharge charge amount to be measured has a temporal periodicity, and since one cycle is the AC frequency (50 Hz, 60 Hz), the cycle of the cycle is not changed. Based on the fact that the length is fixed, the position where the voltage phase becomes 0 can be estimated.

  Since the partial discharge occurs when the voltage applied to the gap exceeds the discharge starting voltage, a sample of the first measurement data in the case of the partial discharge charge amount having many measurement points of the measurement data (for example, A in FIG. 10). It is exemplified that the phase before (for example, B in FIG. 10) (left side of the x-axis) is estimated to be a phase of 270 degrees (minus 90 degrees). Alternatively, based on a similar concept, it may be estimated that there is a phase of 270 degrees (minus 90 degrees) in front of the chevron shape (on the left side of the x-axis) of the measurement point of the measurement data in FIG. In this embodiment, the degree of abnormality is determined based on the index value of the shape (size) of the gap, and therefore, it is sufficient that the approximate position of the phase 0 degree can be estimated.

  In addition, a description will be given of how the abnormality determination unit 112 determines whether storage of measurement data is necessary. When the abnormality determination unit 112 determines that the measurement data needs to be stored, the measurement data is stored in the storage unit 117 or the measurement data is transmitted to the storage 104 via the communication unit 114. When it is determined that storage is necessary, the same as the criterion for the abnormality determination unit 112 to diagnose an abnormality is the same. Alternatively, the degree of abnormality may be calculated and stored for a change (evaluating the differential value) or when there is no change for a certain period. This can be used for the abnormality determination unit 112 and the failure determination of the storage processing.

  The format of the stored measurement data may be the measured measurement data itself, the superimposed measurement data, or a format to which a time series data compression method is applied. Such an algorithm is exemplified by the swinging door algorithm and the like.

  After determining that storage is necessary based on the criteria of the abnormality diagnosis, it may be determined that storage is required based on the storable capacity or the storable throughput.

  Regarding the storable capacity, it is determined whether or not it is determined that the storage is necessary according to the storage capacity of the storage unit 117 or the storage 104. For example, if the remaining free space is small, it is determined that storage is unnecessary. In such a case, the storage unit 117 or the storage 104 may be instructed to delete or compress the stored data, or may be notified to the outside such as an operator that the storage capacity is small.

  Regarding the storable throughput, the necessity of storage is determined based on the storable capacity per unit time. This is determined according to the storable capacity of the storage unit 117 or the storage 104 per unit time or the data transfer speed in the network 106. For example, if the data transfer speed of the network 106 is limited, it is determined that storage is unnecessary. Also in the case of such a determination, it may be notified to the outside such as an operator.

  In this embodiment, the result of the abnormality diagnosis can be used as a countermeasure in the event of an abnormality. If an abnormality is diagnosed, take prescribed measures. For example, system operation is performed so as not to flow a power flow into the power cable 100 to be diagnosed, or a period until insulation breakdown is estimated, and a periodic inspection that can be performed within the period is planned and executed, and Update etc.

  In the period up to the dielectric breakdown, a similar environment may be used, and a result obtained in advance by a past case or experiment may be used, or information stored in the power device information storage unit 115, a past record or history may be used. The estimation may be performed using information. For this estimation, estimation using machine learning or a statistical method is exemplified. In the experiment, the power may be applied to the power cable 100 until the dielectric breakdown, or the power may be stopped before the dielectric breakdown occurs, and the state of the disassembled power cable 100 may be visually or image-processed. Evaluation is exemplified.

  In the present embodiment, abnormality diagnosis is performed, and the position of the gap (position where partial discharge occurs) can be located. The cooperation between the plurality of measurement and diagnosis apparatuses 102 will be described.

  The plurality of measurement / diagnosis devices 102 include a time synchronization unit 118 and are time-synchronized with each other. The measurement data measured by the plurality of measurement diagnostic devices 102 is added to time information at the time of measurement and stored in the storage 104. The server 105 estimates the occurrence position of the partial discharge using the measurement data stored in the storage 104 or the result of the abnormality diagnosis.

  Since each of the measurement and diagnosis apparatuses 102 is time-synchronized with each other, measurement data of another measurement and diagnosis apparatus 102 at a time when an abnormality is diagnosed by one measurement and diagnosis apparatus 102 is confirmed. At this time, the partial discharge charge amount or the amplitude value of the specific frequency (the frequency having the maximum amplitude or the minimum, or the specific frequency considered to be the partial discharge) in the frequency spectrum, and the positions of the plurality of measurement / diagnosis devices 102 on the power system. Is used to estimate the position where the partial discharge occurs. Alternatively, the occurrence position of the partial discharge may be estimated from the positional relationship of the plurality of measurement / diagnosis devices 102 on the power system using the ratio of the value of the partial discharge charge amount in the specific phase.

  It has been described that the measurement data is recorded only when the measurement data indicating the partial discharge matches a specific condition. However, the measurement / diagnosis device 102 informs the nearby measurement / diagnosis device 102 of the time when the abnormality was diagnosed. It may be instructed to record or output measurement data. Alternatively, the measurement / diagnosis device 102 may record the measurement data in response to an external instruction. This is because the position where the gap is generated is farther than the measurement / diagnosis device 102 diagnosed as abnormal, and measurement data indicating partial discharge may not be measured.

  At this time, in order to instruct measurement and recording of measurement data from one measurement / diagnosis device 102 to another measurement / diagnosis device 102, the network 106 may include a real-time communication method for transmitting the instruction within a predetermined time. Illustrated. Further, it is exemplified that the measurement and diagnosis apparatus 102 receiving the instruction includes the storage unit 117 or the temporary data storage buffer having a capacity corresponding to the maximum guaranteed delay in such a real-time communication method.

  Note that even the single measurement and diagnosis device 102 may estimate the position of the gap from the voltage phase characteristics of the measured partial discharge charge amount. In this case, a position prediction algorithm may be constructed by a technique such as machine learning from existing performance data or the like, or the absolute value of the partial discharge charge amount and the amount of attenuation due to the resistance component of the power cable 100 may be estimated, and the gap of the gap may be estimated. The position may be estimated.

  In addition, the server 105 may present a proposal for a new installation location of the measurement and diagnosis device 102 based on the measurement results of the plurality of measurement and diagnosis devices 102 or the measurement results of the single measurement and diagnosis device 102. For example, when the partial discharge signal is measured by the two measurement / diagnosis devices 102, but the partial discharge charge amount is weak and the position of the gap cannot be accurately located, the power cable to be diagnosed by the two measurement / diagnosis devices 102 It is exemplified that the measurement and diagnosis device 102 is installed between 100 observation positions. The transfer of another measurement / diagnosis device 102 (for example, the measurement / diagnosis device 102 that does not measure partial discharge) to the presented position is exemplified.

  Similarly, the server 105 obtains operation history, operation information, and control target state information by using communication with an operator, a central power supply command center, a control station, a power supply station, and a substation, or information sharing means. Thus, the output of the measurement data in the measurement diagnostic device 102 or the storage 104 may be instructed.

  For example, a change in power flow in the power cable 100 on the power system (operation: increase, decrease, reverse power flow) or comparison with a predetermined threshold value (power flow increased and exceeded a predetermined threshold value, etc.) May be instructed to output the measurement data during the predetermined time period before and after the time when the error occurs. Alternatively, it may be instructed to output measurement data for a predetermined period before and after the abnormality and change of the hydraulic system of the OF cable and the date and time when the oil-in-gas analysis was performed.

  In response to an event of switching operation (system operation) to a circuit breaker or disconnector, an instruction may be issued to output measurement data for a predetermined period before and after the event. Since the switching surge due to the switching operation may trigger partial discharge, it is important to observe the measurement data associated with the switching operation.

  Alternatively, measurement data may be output based on the time of occurrence of a weather event such as a lightning strike. For example, when a lightning strike occurs near the power cable 100 to be diagnosed, the server 105 may acquire a lightning strike time and instruct to output measurement data for a predetermined period before and after the time.

  In addition, in order to remove the influence of noise caused by an inverter or the like connected to the power system, the measured partial discharge charge amount is converted into a frequency band, and a signal of a predetermined frequency related to such noise is filtered, and inverse Fourier transform is performed. For example, it is exemplified that the phase region (time region) is set again.

  Alternatively, since it is conceivable that noise such as a measurement error by the sensor 101 is included, outliers are removed by applying a statistical method to measurement data for a plurality of cycles.

  If the measurement data at the time when the output is instructed is not stored in the measurement / diagnosis device 102 or the storage 104, it means that the measurement / diagnosis device 102 has not determined that the time is abnormal or requires storage. Therefore, a standard data set in such a case may be defined and used.

  In order to increase the accuracy of the diagnosis, the synchronization accuracy between the measurement / diagnosis device 102, the server 105, and various control systems cooperating with the server 105 may be increased.

  At the time of output, electronic means may be used, or non-electronic means for outputting with paper or the like may be used.

  Estimating the period until insulation breakdown based on the measured data using power system information, machine learning, etc., and applying the estimated period to asset management (maintenance service) of the power cable 100 Can be.

  The priority of the power cable 100 to be updated is determined based on the number of days until dielectric breakdown, the importance of the power cable 100, the cost for updating, and the budget. At this time, external events (system operation, operation, weather, etc.) that may affect the partial discharge may be considered. For example, increasing the update priority of the power cable 100 in a region where the probability of occurrence of lightning is high is exemplified. An example is to raise the update priority of the power cable 100 connected to a substation that can be opened and closed. Alternatively, the update priority may be reflected in accordance with the degree of influence (such as a power failure range and the number of branches of the connected power cable 100) when the power cable 100 is subjected to insulation breakdown. It is exemplified that the update priority of the power cable 100 having a high degree of influence is increased.

  Alternatively, it is exemplified that, based on a budget plan for a predetermined period (for example, a plurality of years), the update timing of the power cable is determined in consideration of the abnormalities and priorities of the plurality of power cables. This illustrates, for example, updating the power cable ahead of the actual appropriate update time.

  The information of a plurality of measurement data and the results of the abnormality diagnosis (such as the occurrence of insulation breakdown and traces of partial discharge) are aggregated in the host server 105, and statistical methods such as big data analysis and neural networks are applied. It is exemplified that the abnormality diagnosis is performed with high accuracy by aggregating information in the system cloud. For example, a high precision of the weight coefficient and the threshold value is indicated.

  The measurement / diagnosis device 102 may set all of the power cables 100 to be diagnosed, or may periodically change the location. They may be relocated and installed with regular inspections by maintenance personnel.

  As described above, according to the present embodiment, it is possible to estimate the state of the oil gap and the gap in the power cable such as the OF cable and the CV cable, and it is possible to diagnose the deterioration state of the power cable. This makes it possible to take appropriate measures, such as updating the power cable, and to stably operate the power system.

  Furthermore, by estimating the period until the insulation breakdown and determining the priority of updating in a plurality of power cables, asset management can be appropriately performed. By taking measures such as bringing forward the timing of replacing the power cable in accordance with the degree of abnormality, the budget of the operator can be smoothed. In addition, the knowledge of the operator can be utilized by combining the estimation of the shape (size) of the gap according to the present embodiment with the knowledge of the operator such as the shape (size) of the gap leading to abnormality such as insulation breakdown. Further, the voltage phase can be estimated from the periodicity of the measured partial discharge charge amount, and the installation of the voltage sensor can be eliminated.

  Note that the present invention is not limited to the above-described embodiment, and includes various modifications. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described above.

100 ... power cable, 101 ... sensor, 102 ... measurement and diagnosis device, 103 ... coaxial cable, 104 ... storage, 105 ... server, 106 ... network, 112 ... abnormality determination unit, 113 ... measurement signal receiving unit, 114 ... communication unit, 115 ... power device information storage unit, 116 ... cycle detection unit, 117 ... storage unit, 118 ... time synchronization unit, 119 ... phase estimation unit, 120 ... CPU, 121 ... LAN, 122 ... data processing IC, 123 ... ADC, 124 … Memory, 125… non-volatile storage medium, 126… bus

Claims (13)

From a sensor that detects a partial discharge signal of a power cable having an insulating medium, a measurement signal receiving unit that receives the partial discharge signal as measurement data,
From the voltage phase and the partial discharge charge amount derived from the measurement data received by the measurement signal receiving unit, an index value of the size of the gap formed in the insulating medium is calculated, and the abnormality diagnosis of the power cable is performed. An abnormality diagnosis device comprising: an abnormality determination unit that performs the abnormality determination.   Detecting a partial discharge signal of a power cable having an insulating medium, receiving the partial discharge signal as measurement data, and forming the partial discharge signal on the insulating medium from a voltage phase and a partial discharge charge amount derived from the received measurement data. An abnormality diagnosis method for calculating an index value of the determined gap size and performing abnormality diagnosis of the power cable. The abnormality diagnosis method according to claim 2, wherein
In the voltage phase derived from the measurement data, an index value of a size of a gap formed in the insulating medium is calculated using a discharge starting voltage, and an abnormality diagnosis of the power cable is performed. Abnormality diagnosis method. The abnormality diagnosis method according to claim 2, wherein
An abnormality diagnosis method, wherein the size of the gap is an index value of a thickness of the gap or an index value of an area of a bottom surface of the gap. The abnormality diagnosis method according to claim 2, wherein
An abnormality diagnosis method, wherein an index value of the area of the bottom surface of the gap is calculated from the index value of the thickness of the gap and the partial discharge charge amount. The abnormality diagnosis method according to claim 2, wherein
An index value is calculated for a plurality of measurement points where the partial discharge charge amounts in the measurement data are different, an index value distribution of a gap size at the plurality of measurement points is estimated, and based on the estimated distribution, An abnormality diagnosis method characterized by performing abnormality diagnosis of a power cable. The abnormality diagnosis method according to claim 6, wherein
An abnormality diagnosis method, comprising: classifying index values of gap sizes at a plurality of measurement points into classes; and performing abnormality diagnosis of the power cable based on the number of index values of each class. The abnormality diagnosis method according to claim 2, wherein
An abnormality diagnosis method, wherein an abnormality diagnosis of the power cable is performed based on a time change of the index value. The abnormality diagnosis method according to claim 2, wherein
An abnormality diagnosis method, wherein the index value is output as an abnormality degree. The abnormality diagnosis method according to claim 2, wherein
An abnormality diagnosis method, wherein the power cable is an oil immersion paper cable, and the reliability of the index value is output based on oil pressure information in the oil immersion paper cable. The abnormality diagnosis device according to claim 1,
An abnormality diagnosis device, comprising: a period detection unit that detects an AC period of a voltage applied to the power cable. The abnormality diagnosis device according to claim 1,
An abnormality diagnosis device comprising a time synchronization unit for synchronizing time with another abnormality diagnosis device according to a predetermined time synchronization protocol. The abnormality diagnosis device according to claim 1,
An abnormality diagnosis device comprising a phase estimating unit for estimating a voltage phase from a periodicity of a partial discharge charge amount derived from the measurement data.

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