Patent Description:
In an urban area, a huge power transmission network is laid in the ground, and power generated in a power plant is transmitted to each power consumer via the power transmission network. Since underground power transmission facilities have increased in the high economic growth period, and many of them have been used for <NUM> years from the start of operation, a technique for diagnosing aged degradation has become important.

A partial discharge measurement method is one of degradation diagnosis techniques for an underground power transmission cable. The underground power transmission cable has a structure in which a conductor through which a current flows is covered with an insulator. In a case where a void is generated in the insulator due to aging degradation, partial discharge occurs in the void, and finally dielectric breakdown occurs. The partial discharge measurement method is for observing such a partial discharge and diagnosing a degree of insulation degradation of an underground power transmission cable based on the observation result, and various companies and research organizations have conducted studies to elucidate a partial discharge generation mechanism and estimate the degree of insulation degradation from the partial discharge property.

For example, NPL <NUM> discloses a measurement result of the phase angle property of a partial discharge pulse from the start of electric charge application to dielectric breakdown using an experimental electrode, and a degradation diagnosis estimation method to which a pattern recognition method is applied. The phase angle property of the partial discharge pulse is a distribution pattern of the charge quantity and the occurrence phase angle of the partial discharge pulse during a plurality of cycles of the applied voltage. The change in the range of the phase angle region where the partial discharge occurs and the occurrence charge quantity is shown in the five times from the start of the electric charge application to the dielectric breakdown. In the degradation diagnosis estimating method, the phase angle property of the partial discharge is patterned into the standardized charge quantity, the standardized phase angle, and the occurrence frequency, and the similarity between this pattern and the standard pattern according to the degradation degree created in advance is compared.

PTL <NUM> discloses a partial discharge measurement method capable of determining the presence or absence of a partial discharge using a neural network. Unlike the pattern of NPL <NUM>, the pattern used here is a standardized charge quantity/standardized phase angle that is listed in time series every t cycle. In the technique of NPL <NUM>, since the charge quantity and the phase angle are collectively patterned as the occurrence frequency for a plurality of cycles (<NUM> cycles), there is a problem that time information of the occurrence phase angle is lost and the presence or absence of the partial discharge may be erroneously determined.

Furthermore, PTL <NUM> discloses an insulation diagnosis system capable of improving diagnosis accuracy by applying a hidden Markov model. Since the neural network used in the insulation diagnosis system in the related art is not possible to include the temporal causal relationship of the feature amount stochastically changing with the lapse of time, there is a problem that the accuracy is low when the neural network is applied to the diagnosis in the insulation state by the partial discharge. In the hidden Markov model, data varying in time series is expressed by a probabilistic model. Furthermore, PTL <NUM> discloses a partial discharge monitoring and diagnosing system for power equipment using a signal detecting method based on a statistical phase pattern to remotely detect a partial discharge in internal power equipment and a power device system housing such as a high-voltage cable and the like. Eventually, PTL <NUM> discloses a partial discharge judging method capable of judging accurately the discharge state of a partial discharge by measuring a partial discharge signal, wherein a CPU divides a discharge electric charges amount and a charged potential phase into q and p pieces, respectively, to represent a phase ϕ-electric charges amount q by a plurality of elements, thereby acquiring a ϕ-q-n pattern allocating the number of occurrence to a value of each element.

Since the technique disclosed in NPL <NUM> uses the distribution patterns of the charge quantity, the phase angle, and the occurrence frequency of the partial discharge pulses in a plurality of cycles (for example, <NUM>), there is a disadvantage that the information on the temporal change of the partial discharge cannot be included while there is an advantage that the feature of the pattern as a whole appear and are easy to identify.

In addition, since the technique disclosed in PTL <NUM> uses the distribution patterns of the charge quantity, the phase angle, and the time of the partial discharge pulses in t cycle, and there is a problem that t as the number of measurement cycles cannot be increased as in NPL <NUM> while there is an advantage that the information on the temporal change of the partial discharge can be included. This is because the scale of the neural network increases. Therefore, the feature of the pattern as a whole is difficult to appear, and determination may be difficult.

Furthermore, according to the technique disclosed in PTL <NUM>, there are a normal state and each insulation degradation state of the insulator, and in each state, a property amount related to insulation is patterned and provided as a parameter, so that there is a possibility that diagnosis accuracy can be improved. On the other hand, in <FIG> of PTL <NUM>, there is a problem that a normal state S1, a next insulation degradation state S2, a next insulation degradation state S3, and a final state S4 of the insulator have only unidirectional state transition probabilities, and when the state transition is wrong, the insulation degradation state becomes worse than the actual state.

The present invention has been made in view of the above points, and an object of the present invention is to propose a partial discharge determination apparatus and a partial discharge determination method capable of increasing a feature amount by including temporal information of partial discharge in a distribution pattern of a charge quantity, a phase angle, and an occurrence frequency of a partial discharge pulse, and improving accuracy of partial discharge determination.

Preferable embodiments are defined in the dependent claims.

According to the partial discharge apparatus and the partial discharge method of the present invention, it is possible to determine the degree of progress of partial discharge including temporal information of the partial discharge.

According to the present invention, it is possible to realize a partial discharge determination apparatus and a partial discharge determination method capable of accurately determining the degree of progress of the partial discharge.

In <FIG>, reference numeral <NUM> denotes an underground power transmission cable degradation monitoring system to which the present invention is applied as a whole. An underground power transmission cable degradation monitoring system <NUM> is a system that monitors degradation of an underground power transmission cable <NUM>, and includes a clamp type high-frequency current transformer (CT) <NUM>, a partial discharge determination apparatus <NUM>, and a cable degradation monitoring apparatus <NUM>.

In the case of an OF (Oil Filled) cable that maintains insulation with kraft paper and oil, the underground power transmission cable <NUM> is configured by sequentially laminating an insulator <NUM> made of kraft paper immersed in insulating oil, a metal sheath <NUM> for enclosing oil, and an anticorrosion layer <NUM> for corrosion prevention on a conductor <NUM> through which electricity flows. The metal sheath <NUM> is grounded via a metal sheath ground line <NUM>, so that when a partial discharge occurs in the underground power transmission cable <NUM>, the partial discharge pulse can be released to the ground via the metal sheath ground line <NUM>.

The clamp type high-frequency CT3 is configured to include a clamp high frequency current sensor, and outputs a partial discharge pulse signal including a pulse that rises to a voltage level corresponding to the charge quantity of each partial discharge pulse PL flowing through the metal sheath ground line <NUM> to the partial discharge determination apparatus <NUM>. In the following description, the pulse included in the partial discharge pulse signal is referred to as a partial discharge pulse PL for easy understanding.

The partial discharge determination apparatus <NUM> is equipped with a partial discharge determination function of determining a degree of progress of partial discharge in the target underground power transmission cable (hereinafter, this is referred to as a target underground power transmission cable) <NUM> based on a partial discharge pulse signal given from the clamp type high-frequency CT3. The partial discharge determination apparatus <NUM> executes a partial discharge determination process of determining a degree of progress of the partial discharge based on the partial discharge determination function, and transmits a processing result as a partial discharge determination signal to the cable degradation monitoring apparatus <NUM> via the network <NUM>.

The cable degradation monitoring apparatus <NUM> includes, for example, a computer device such as a personal computer or a workstation, records necessary information included in a partial discharge determination signal given from the partial discharge determination apparatus <NUM>, estimates a degradation degree of the underground power transmission cable <NUM> by combining a determination result of the partial discharge with a time change, and displays the estimation result.

<FIG> illustrates a schematic configuration of the partial discharge determination apparatus <NUM>. As illustrated in <FIG>, the partial discharge determination apparatus <NUM> includes a computer device including a central processing unit (CPU) <NUM>, a memory <NUM>, a storage device <NUM>, an analog/digital (A/D) converter <NUM>, a data registration unit <NUM>, and a transmitter <NUM>.

The CPU <NUM> is a processor that controls the entire operation of the partial discharge determination apparatus <NUM>. The memory <NUM> includes a volatile semiconductor memory and the like, and is used as a work memory of the CPU <NUM>. Programs such as a distribution pattern generation program <NUM>, a differential data generation program <NUM>, an artificial intelligence (AI) program <NUM>, and a transmission frame generation program <NUM> to be described later are loaded from the storage device <NUM> and held in the memory <NUM>.

The storage device <NUM> includes a nonvolatile large-capacity storage device such as a hard disk device, a solid state drive (SDD), or a flash memory, and stores various programs, data to be stored for a long period of time, and the like. The storage device <NUM> also stores and hold partial discharge data <NUM>, standardized distribution pattern data <NUM>, and data of the neural network <NUM>, which will be described later.

The A/D converter <NUM> is configured to include a general-purpose A/D converter. The data registration unit <NUM> is configured to include a field programmable gate array (FPGA). A function of the data registration unit <NUM> will be described later. The transmitter <NUM> is configured to include, for example, a network interface card (NIC), and transmits the determination result of the partial discharge determination by the partial discharge determination apparatus <NUM> to the cable degradation monitoring apparatus <NUM> (<FIG>) via the network <NUM> (<FIG>).

<FIG> illustrates a flow of the partial discharge determination process performed by the partial discharge determination apparatus <NUM>. In the drawing, a distribution pattern generation unit <NUM>, a differential data generation unit <NUM>, an AI unit <NUM>, and a transmission frame generation unit <NUM> are functional units implemented by the CPU <NUM> executing the distribution pattern generation program <NUM>, the differential data generation program <NUM>, the AI program <NUM>, or the transmission frame generation program <NUM> described above with respect to <FIG> loaded from the storage device <NUM> to the memory <NUM>.

As illustrated in <FIG>, in the partial discharge determination apparatus <NUM>, an applied voltage signal SG1 as illustrated in the second stage of <FIG> obtained by stepping down the voltage (hereinafter, this is referred to as an applied voltage) of electricity flowing through the underground power transmission cable <NUM> (<FIG>) to about <NUM> V is provided to the A/D converter <NUM>. Then, the A/D converter <NUM> performs A/D conversion on the applied voltage signal SG1, and outputs digital data of the applied voltage signal SG1 thus obtained to the data registration unit <NUM>.

A partial discharge pulse signal SG2 including each partial discharge pulse PL generated in the underground power transmission cable <NUM> as illustrated in the uppermost stage of <FIG> and given from the clamp type high-frequency CT3 (<FIG>) is input to the A/D converter <NUM>. Then, the A/D converter <NUM> performs A/D conversion on the partial discharge pulse signal SG2, and outputs digital data of the partial discharge pulse signal SG2 thus obtained to the data registration unit <NUM>.

The data registration unit <NUM> extracts each partial discharge pulse PL included in the partial discharge pulse signal SG2, and acquires a digital value of each partial discharge pulse PL as a charge quantity of the partial discharge pulse PL. For each partial discharge pulse PL, the data registration unit <NUM> acquires a phase angle (hereinafter, this is referred to as a phase angle or an occurrence phase angle of the partial discharge pulse PL) of the applied voltage signal SG1 at a time point when the partial discharge pulse PL is generated. As will be described later, the phase angle of the partial discharge pulse PL acquired by the data registration unit <NUM> at this time is a phase angle (hereinafter, this is referred to as a standardized phase angle) obtained by standardizing <NUM> degrees to <NUM> degrees to an integer value of <NUM> to <NUM>. Then, the data registration unit <NUM> stores the charge quantity and the standardized phase angle of each partial discharge pulse PL acquired in this manner in the storage device <NUM> (<FIG>) as partial discharge data <NUM>.

Based on the partial discharge data <NUM> of each partial discharge pulse PL stored in the storage device <NUM>, the distribution pattern generation unit <NUM> sequentially generates a standardized phase-resolved partial discharge pattern T' to be described later with respect to <FIG> obtained by standardizing a distribution pattern (hereinafter, this is referred to as a phase-resolved partial discharge pattern) T of a combination of a charge quantity and a standardized phase angle of each partial discharge pulse PL generated in several cycle periods of an applied voltage signal SG1 to be described later with respect to <FIG>, and sequentially stores data of the generated standardized phase-resolved partial discharge pattern T' in the storage device <NUM> as standardized distribution pattern data <NUM>.

The standardized distribution pattern data <NUM> of the current standardized phase-resolved partial discharge pattern T' stored in the storage device <NUM> is then read by the AI unit <NUM>. Furthermore, at this time, the differential data generation unit <NUM> reads the standardized distribution pattern data <NUM> of the current standardized phase-resolved partial discharge pattern T' and the standardized distribution pattern data <NUM> of the previous standardized phase-resolved partial discharge pattern T' stored in the storage device <NUM>, generates differential data representing a difference between the previous and current standardized phase-resolved partial discharge patterns T' based on these two pieces of standardized distribution pattern data <NUM>, and outputs the generated differential data to the AI unit <NUM>.

Based on the standardized distribution pattern data <NUM> of the current standardized phase-resolved partial discharge pattern T' and the differential data of the previous and current standardized phase-resolved partial discharge patterns T', the AI unit <NUM> performs machine learning to determine whether a degree of the progress of the partial discharge of the current target underground power transmission cable <NUM> belongs to the category at the start of the partial discharge, the middle stage of the partial discharge, or immediately before the dielectric breakdown.

Here, <FIG> illustrate an example of a phase-resolved partial discharge pattern T in which points representing the respective partial discharge pulses PL generated in a plurality of cycles (for example, <NUM> cycles) of the applied voltage are plotted on a coordinate plane in which the charge quantity of the partial discharge pulse PL is taken on the vertical axis and the phase angle of the applied voltage is taken on the horizontal axis.

<FIG> is an example of a phase-resolved partial discharge pattern T at the start of partial discharge. In this example, a positive partial discharge pulse occurs from the vicinity of the zero-cross point of the applied voltage from negative to positive, and a negative partial discharge pulse occurs from the vicinity of the zero-cross point of the applied voltage from positive to negative. Specifically, it is illustrated that a partial discharge pulse having a positive charge quantity occurs when the phase angle of the applied voltage is in the range of-<NUM> degrees to <NUM> degrees, and a partial discharge pulse having a negative charge quantity occurs when the phase angle of the applied voltage is in the range of <NUM> degrees to <NUM> degrees.

<FIG> is an example of a phase-resolved partial discharge pattern T at the middle stage of partial discharge. In the middle stage of the partial discharge, the charge quantity of the partial discharge pulse increases as compared with the distribution pattern of <FIG>, and the range of the phase angle at which the partial discharge pulse occurs also expands.

<FIG> illustrates an example of a phase-resolved partial discharge pattern T in the late stage of the partial discharge and immediately before the dielectric breakdown. <FIG> illustrates that a partial discharge pulse occurs at all phase angles of the applied voltage, and the charge quantity ranges from + tens of thousands of pC to - tens of thousands of pC.

As described above, the phase-resolved partial discharge pattern T gradually changes from the initial stage of the partial discharge to immediately before the dielectric breakdown. Specifically, as the degradation of the underground power transmission cable <NUM> due to the partial discharge progresses, the number of places where the partial discharge occurs increases as described above, and the charge quantity of the partial discharge also increases.

Therefore, it is considered that the temporal variation of the partial discharge occurring in the target underground power transmission cable <NUM> can be detected based on the difference between the plurality of phase-resolved partial discharge patterns T acquired continuously in time, and the determination accuracy of the partial discharge determination can be improved by using the variation amount as one of the determination elements of the degree of progress of the partial discharge.

Therefore, in the present embodiment, as described above, based on the standardized distribution pattern data <NUM> of the current standardized phase-resolved partial discharge pattern T' and the differential data of the previous and current standardized phase-resolved partial discharge patterns T', the machine learning to the degree of the progress of the partial discharge of the current target underground power transmission cable <NUM>. In addition, the AI unit <NUM> determines the degree of progress of the partial discharge in the target underground power transmission cable <NUM> using the neural network <NUM> obtained by the machine learning, and outputs the determination result to the transmission frame generation unit <NUM>.

The transmission frame generation unit <NUM> generates a transmission frame in a predetermined format storing the determination result given from the AI unit <NUM>, and outputs the generated frame to the transmitter <NUM>. Thus, the transmitter <NUM> transmits the transmission frame provided from the transmission frame generation unit <NUM> as a partial discharge determination signal to the cable degradation monitoring apparatus <NUM> (<FIG>) via the network <NUM> (<FIG>).

<FIG> illustrates the above-described standardized phase-resolved partial discharge pattern T' obtained by standardizing the phase-resolved partial discharge pattern T illustrated in <FIG>. In order for the AI unit <NUM> (<FIG>) to easily classify the distribution pattern of the partial discharge pulse PL into the category (at start of partial discharge, middle stage of partial discharge or immediately before dielectric breakdown) according to the degree of progress of the partial discharge using the neural network <NUM> (<FIG>), the distribution pattern generation unit <NUM> (<FIG>) standardizes the phase-resolved partial discharge pattern T as illustrated in <FIG> based on the partial discharge data <NUM> stored in the storage device <NUM>, and generates the standardized phase-resolved partial discharge pattern T' illustrated in <FIG> in which the occurrence number of partial discharges for each combination of the standardized charge quantity and the standardized phase angle are aggregated.

Specifically, the distribution pattern generation unit <NUM> first sets a range (hereinafter, this is referred to as a window) <NUM> including all the points representing the partial discharge pulse PL in <FIG> on the phase-resolved partial discharge pattern T in <FIG>. At this time, the vertical length of a window <NUM> representing the discharge charge quantity of the partial discharge is set so that the charge quantity from <NUM> to the top and the charge quantity from <NUM> to the bottom are the same. That is, the larger absolute value of the positive maximum value and the negative maximum value of the partial discharge charge quantity is the length from <NUM> to the top and the length from <NUM> to the bottom of the window <NUM>.

Next, the distribution pattern generation unit <NUM> equally divides each of the longitudinal direction and the lateral direction of the window <NUM> in <FIG> into a predetermined number, divides the inside of the window <NUM> into a plurality of small regions (hereinafter, this is referred to as a cell) <NUM> as in <FIG>, and sets a counter (hereinafter, this is referred to as a partial discharge pulse counter) for counting the number of partial discharge pulses corresponding to each cell <NUM>.

<FIG> illustrates an example in which the window <NUM> is equally divided into <NUM> pieces in both the longitudinal direction and the lateral direction. In the vertical direction of <FIG>, one of the cells <NUM> represents a standardized charge quantity (hereinafter, this is referred to as a standardized charge quantity) sq. In <FIG>, since the range of the window <NUM> in the longitudinal direction is-<NUM>. pC to +<NUM> pC, in <FIG>, sq = <NUM> corresponds to a range of -<NUM> pC or more and less than -<NUM> pC, and sq = <NUM> corresponds to a range of -<NUM> pC or more and less than -<NUM> pC. The same applies to sq = <NUM> to sq = <NUM>. In addition, sq = <NUM> corresponds to a range of -<NUM> pC or more and less than <NUM> pC, sq = <NUM> corresponds to a range of more than <NUM> pC and <NUM> pC or less, and sq = <NUM> corresponds to a range of more than <NUM> pC and <NUM> pC or less. The same applies to sq = <NUM> to sq = <NUM>. sq = <NUM> corresponds to a range of more than <NUM> and <NUM> pC or less.

In the lateral direction of <FIG>, one cell <NUM> represents one standardized phase angle sd. Therefore, in <FIG>, sd = <NUM> corresponds to a range of <NUM> degrees or more and less than <NUM> degrees, and sd = <NUM> corresponds to a range of <NUM> degrees or more and less than <NUM> degrees. The same applies to sd = <NUM> to sd = <NUM>.

Next, the distribution pattern generation unit <NUM> standardizes the charge quantity of each target partial discharge pulse PL to an integer value of <NUM> to <NUM>. Then, for each partial discharge pulse PL, the distribution pattern generation unit <NUM> counts up a partial discharge pulse counter of the cell <NUM> corresponding to a combination of the standardized charge quantity (standardized charge quantity) and the standardized phase angle generated by the distribution pattern generation unit <NUM>. As a result, the number (hereinafter, this is referred to as the number of partial dischargepulses) sqc of the corresponding partial discharge pulses PL is counted for each cell <NUM>.

In <FIG>, for easy understanding, each cell <NUM> is colored at a concentration corresponding to the number of partial discharge pulses sqc counted by the partial discharge pulse counter of the cell <NUM>. Specifically, in <FIG>, colorless indicates that the standardized partial discharge pulse number sqc is <NUM>, and each cell <NUM> is colored such that the concentration increases in the order of light gray, dark gray, and black as the value of the standardized partial discharge pulse number sqc increases.

The charge quantity of the partial discharge pulse PL can be standardized as follows. <FIG> illustrates a partial discharge pulse PL for a period of two cycles of the applied voltage. In the drawings, PL1 is the first partial discharge pulse of the first cycle of the applied voltage, and PL2 is the first negative partial discharge pulse of the first cycle of the applied voltage. PL3 is a partial discharge pulse having a negative charge quantity with the largest absolute value among the partial discharge pulses measured this time, and PL4 is a positive partial discharge pulse PL3 with the largest absolute value among the partial discharge pulses measured this time. Here, the charge quantity of the partial discharge pulse PL3 is referred to as nqmax, and the charge quantity of the partial discharge pulse PL4 is referred to as pqmax.

<FIG> illustrates a state in which the charge quantity of each partial discharge pulse PL for a cycle period of two applied voltages corresponding to <FIG> is standardized. In the drawing, PL1' to PL4' correspond to the partial discharge pulses PL1 to PL4 of <FIG>, respectively. The standardized charge quantity sq of each partial discharge pulse PL can be calculated by the following equation:
[Equation <NUM>] <MAT> and
the following equation:
[Equation <NUM>] <MAT>.

Equation (<NUM>) represents that the larger one of the absolute values of the maximum value pqmax of the positive charge quantity of the partial discharge pulse PL and the maximum value nqmax of the negative charge quantity is referred to as qmax. In addition, Equation (<NUM>) represents that the charge quantity q is converted so as to always have a positive value by adding qmax to the charge quantity q of the partial discharge pulse PL, the addition result is then divided by twice qmax (that is, the vertical length of the window in <FIG>), further multiplied by the vertical standardization number (here, <NUM>), and then the fractional value portion is discarded from the multiplication result to obtain the integer value ("int ()"), thereby obtaining the standardized charge quantity sq.

On the other hand, <FIG> illustrates specific processing contents of the differential data generation unit <NUM>. As described above, the differential data generation unit <NUM> acquires the previous standardized phase-resolved partial discharge pattern T1' and the current standardized phase-resolved partial discharge pattern T2' from the storage device <NUM>, and calculates the absolute value (hereinafter, this is referred to as a partial discharge occurrence number difference absolute value) of the difference between the occurrence number of the partial discharge pulses PL in these two standardized phase-resolved partial discharge patterns T1' and T2' for each cell <NUM>.

The distribution (hereinafter, this is referred to as a partial discharge occurrence number difference absolute value distribution) <NUM> of the partial discharge occurrence number difference absolute value between the previous and current standardized phase-resolved partial discharge patterns T1' and T2' calculated in this way represents a difference between the previous standardized phase-resolved partial discharge pattern T1' and the current standardized phase-resolved partial discharge pattern T2'. The difference between the charge quantities in the two phase-resolved partial discharge patterns T is reflected in the value of the cell <NUM> in the vertical direction, and the difference between the phase angles is reflected in the value of the cell <NUM> in the horizontal direction. As a result, the degree of variation in the charge quantity of the partial discharge pulse PL and the degree of variation in the occurrence phase angle can be recognized based on the standardized partial discharge occurrence number difference absolute value distribution patterns T1' and T2'.

<FIG> illustrates a configuration example of the neural network <NUM> used by the AI unit <NUM>. <FIG> is an example of a case where the neural network <NUM> includes a perceptron including an input layer, a hidden layer, and an output layer.

In the neural network <NUM>, a first unit 60A corresponding to each cell <NUM> of the standardized partial discharge occurrence number difference absolute value distribution pattern T' described above with reference to <FIG> is provided in the input layer, and the count value sqc [sq] [sd] of the partial discharge pulse counter of the corresponding cell <NUM> in the current standardized phase-resolved partial discharge pattern T2' is input to each of the first units 60A.

In the neural network <NUM>, a first unit 60B corresponding to each cell <NUM> (refer to <FIG>) of the partial discharge occurrence number difference absolute value distribution <NUM> described above with reference to <FIG> is also provided in the input layer, and the partial discharge occurrence number difference absolute value of the corresponding cell <NUM> in the previous and current standardized phase-resolved partial discharge patterns T1' and T2' calculated by the differential data generation unit <NUM> is input to the first unit 60B.

The hidden layer is provided with a smaller number of second units <NUM> than the total number of first units 60A and 60B in the input layer. The value input to each of the first units 60A and 60B of the input layer is weighted by the weight set between each of the first units 60A and 60B and each of the second units <NUM>, and is output to each of the second units <NUM>. Each second unit <NUM> calculates a sum of input values from each of the first units 60A and 60B.

The output layer is provided with a smaller number of third units <NUM> than the total number of second units <NUM>. The sum of the input values to the second unit calculated in each of the second units <NUM> of the hidden layer is weighted by the weight set between the second unit <NUM> and each of the third units <NUM> and is output to each of the third units <NUM>. Each of the third units <NUM> calculates a sum of input values from each of the second units <NUM>, and outputs a calculation result.

Note that, in the present embodiment, three third units <NUM> of the output layer are provided, and thereby inputs to the input layer are classified into three categories and output from the neural network <NUM>. Then, the output of the neural network <NUM> is transmitted as a partial discharge determination, signal to the cable degradation monitoring apparatus <NUM> (<FIG>) via the transmission frame generation unit <NUM> (<FIG>) and the transmitter <NUM> (<FIG>) as a determination result of the progress of the partial discharge.

Next, specific processing contents of various types of processes executed in the partial discharge determination apparatus <NUM> based on the partial discharge determination function will be described.

<FIG> illustrates a processing procedure of a process of registering partial discharge pulse information executed by the data registration unit <NUM> (<FIG>). The data registration unit <NUM> detects the charge quantity and the standardized phase angle of each partial discharge pulse PL included in the partial discharge pulse signal SG2 according to the processing procedure illustrated in <FIG>, and registers them in the storage device <NUM>. In the following description, it is assumed that the charge quantity and the standardized phase angle of each partial discharge pulse occurring in the period of <NUM> cycles of the applied voltage are stored in the storage device <NUM>.

When the partial discharge determination apparatus <NUM> is activated, the data registration unit <NUM> starts the process of registering the partial discharge pulse information as illustrated in <FIG>. First, the data registration unit resets (sets to <NUM>) a count value cc of a cycle counter for counting cycles of an applied voltage flowing through the underground power transmission cable <NUM> (<FIG>), and resets a count value qc of a partial discharge pulse counter for counting the number of detected partial discharge pulses PL (S1).

Subsequently, the data registration unit <NUM> determines whether the applied voltage of the electricity flowing through the underground power transmission cable <NUM> has changed from negative to positive based on the applied voltage signal SG1 (<FIG>) (S2). Then, when a negative result is obtained in this determination, the data registration unit <NUM> proceeds to step S5.

On the other hand, when an affirmative result is obtained in the determination in step S2, the data registration unit <NUM> determines whether the count value cc of the cycle counter is less than <NUM> (S3). Then, when an affirmative result is obtained in this determination, the data registration unit <NUM> increments the counter value cc of the cycle counter (increments by <NUM>), and clears (resets) a timer (not illustrated) that counts a clock of <NUM> used as a counter (hereinafter, this is referred to as a phase angle counter) of the occurrence phase angle of the partial discharge pulse PL (S4).

Next, the data registration unit <NUM> monitors the partial discharge pulse signal SG2 (<FIG>) provided from the clamp type high-frequency CT3 (<FIG>) and waits for detection of the partial discharge pulse PL (S5). Then, when the data registration unit <NUM> eventually detects the partial discharge pulse PL included in the partial discharge pulse signal SG2, the data registration unit acquires the charge quantity of the partial discharge pulse PL and the standardized phase angle obtained by standardizing the occurrence phase angle, and stores them in the storage device <NUM> in association with the partial discharge pulse PL (S6).

Specifically, in step S6, the data registration unit <NUM> first acquires the value of the timer at the moment when the partial discharge pulse PL is detected as the count value dc of the phase angle counter, and increments the count value qc of the partial discharge pulse counter. Thereafter, the data registration unit <NUM> acquires a digital value of the partial discharge pulse signal SG2 provided from the A/D converter <NUM> (<FIG>) at that time as the charge quantity q [qc] of the partial discharge pulse PL. Further, the data registration unit <NUM> divides the count value dc of the phase angle counter at that time by <NUM> (when the partial discharge pulse charge quantity is equally divided into <NUM> as in <FIG>), and further calculates a value obtained by discarding the fractional value from the division result as the standardized phase angle of the partial discharge pulse PL.

Thereafter, the data registration unit <NUM> returns to step S1, and thereafter, repeats the processes after step S1 in the same manner as described above.

<FIG> illustrates a processing procedure of a process of standardizing partial discharge pulse charge quantity executed by the distribution pattern generation unit <NUM> (<FIG>). The distribution pattern generation unit <NUM> standardizes the charge quantity of each partial discharge pulse PL occurring in the period of <NUM> cycles of the applied voltage according to the processing procedure illustrated in <FIG>.

In practice, the distribution pattern generation unit <NUM> starts the process of standardizing the partial discharge pulse charge quantity at the timing when a negative result is obtained in step S3 of <FIG>. First, the distribution pattern generation unit resets (sets to <NUM>) the stored maximum absolute value (hereinafter, this is referred to as a charge quantity maximum absolute value) qmax of the charge quantity of the partial discharge pulse PL, and resets a count value i of a loop counter to be described later (S10).

Subsequently, the distribution pattern generation unit <NUM> increments the count value i of the loop counter, substitutes the absolute value of the charge quantity q [i] of the i-th detected partial discharge pulse PL in the target partial discharge pulse group (an aggregate of the partial discharge pulses detected in the previous process of registering the partial discharge pulse information, hereinafter, referred to as a target partial discharge pulse group) at that time into the charge quantity q0 (S11), and determines whether or not the value of the charge quantity q0 at this time is larger than the current charge quantity maximum absolute value qmax (S12).

If a negative result is obtained in this determination, the distribution pattern generation unit <NUM> proceeds to step S14. On the other hand, when obtaining an affirmative result in the determination of step S12, the distribution pattern generation unit <NUM> updates the value of the maximum absolute value of the charge quantity to the value of the charge quantity q0 (S13).

Thereafter, the distribution pattern generation unit <NUM> determines whether or not the value of the count value i of the loop counter has become the count value qc of the partial discharge pulse counter finally obtained in step S6 of <FIG> for the target partial discharge pulse group (that is, the number of partial discharge pulses PL constituting the target partial discharge pulse group) (S14).

Then, when a negative result is obtained in this determination, the distribution pattern generation unit <NUM> returns to step S12, and thereafter, repeats the processes of steps S12 to S14 until an affirmative result is obtained in step S14. By this repetitive processing, the charge quantity of the partial discharge pulse PL having the largest absolute value of the charge quantity among the partial discharge pulses PL constituting the target partial discharge pulse group is set to the value of the maximum absolute value qmax of the charge quantity.

Then, when obtaining the affirmative result in step S14 by finishing the processes in steps S12 to S13 for all the partial discharge pulses PL constituting the target partial discharge pulse group in due course, the distribution pattern generation unit <NUM> resets the count value i of the loop counter (S15).

Subsequently, after incrementing the count value i of the loop counter, the distribution pattern generation unit <NUM> calculates a standardized charge quantity sq [i] obtained by standardizing the charge quantity q [i] of the i-th detected partial discharge pulse PL in the target partial discharge pulse group by the above-described Equation (<NUM>) (S16).

Next, the distribution pattern generation unit <NUM> determines whether or not the count value i of the loop counter has become the count value qc of the partial discharge pulse counter finally obtained in step S6 of <FIG> for the target partial discharge pulse group, similarly to step S14 (S17).

When a negative result is obtained in this determination, the distribution pattern generation unit <NUM> returns to step S16, and thereafter repeats a loop of steps S16-S17-S16 until an affirmative result is obtained in step S17. By this repetitive processing, the standardized charge quantity sq of each partial discharge pulse PL constituting the target partial discharge pulse group is calculated.

Then, when obtaining the affirmative result in step S17 by finishing the calculation of the standardized charge quantity sq of all the partial discharge pulses PL constituting the target partial discharge pulse group in due course, the distribution pattern generation unit <NUM> finishes the process of standardizing the partial discharge pulse charge quantity.

<FIG> illustrates a processing procedure of process of initializing counter executed by the distribution pattern generation unit <NUM>. The distribution pattern generation unit <NUM> initializes the partial discharge pulse counter of each cell <NUM> in the standardized phase-resolved partial discharge pattern T' described above with reference to <FIG> according to the processing procedure illustrated in <FIG>.

In practice, when starting the process of initializing the counter, the distribution pattern generation unit <NUM> first resets (sets to <NUM>) the count value i of the first loop counter associated with the standardized charge quantity (standardized charge quantity sq) (S20), and resets the count value j of the second loop counter associated with the standardized phase angle (standardized phase angle sd) (S21).

Subsequently, the distribution pattern generation unit <NUM> resets the value of the count value sqc of the partial discharge pulse counter of the cell 51in which the value of the standardized charge quantity sq matches the count value i of the first loop counter at that time and the value of the standardized phase angle sd matches the count value j of the second loop counter at that time to <NUM>, and increments the count value j of the second loop counter (S22).

Next, the distribution pattern generation unit <NUM> determines whether or not the value of the count value j of the second loop counter is less than <NUM> (S23). When the affirmative result is obtained in this determination, the distribution pattern generation unit <NUM> returns to step S22, and then repeats the loop of steps S22-S23-S22.

When obtaining the negative result in step S23 by finishing resetting the count values sqc of the partial discharge pulse counters of all the cells <NUM> in which the value of the standardized phase angle sd is <NUM> in due course, the distribution pattern generation unit <NUM> increments the count value i of the first loop counter (S24), and thereafter, determines whether or not the count value i is less than <NUM> (S25).

When the affirmative result is obtained in this determination, the distribution pattern generation unit <NUM> returns to step S21, and then repeats the loop of steps S21 to S25. Then, when obtaining the negative result in step S25 by finishing resetting the count value sqc of the partial discharge pulse counter of all the cells <NUM> in due course, the distribution pattern generation unit <NUM> finishes the process of initializing counter.

<FIG> illustrates a processing procedure of process of aggregating partial discharge pulse executed by the distribution pattern generation unit <NUM> after finishing the process of initializing counter (<FIG>). The distribution pattern generation unit <NUM> aggregates the number of partial discharge pulses corresponding to each cell <NUM> of the standardized phase-resolved partial discharge pattern T' illustrated in <FIG> according to the processing procedure illustrated in <FIG>.

In practice, the distribution pattern generation unit <NUM> first resets the value of the count value i of the loop counter (S30), then increments the value of the count value i, and substitutes the standardized charge quantity sq [i] of the i-th detected partial discharge pulse PL among the partial discharge pulses PL constituting the target partial discharge pulse group into the standardized charge quantity sq0 (S31).

Subsequently, the distribution pattern generation unit <NUM> determines whether or not the value of the standardized charge quantity sq0 is <NUM> (S32). If a negative result is obtained in this determination, the distribution pattern generation unit <NUM> proceeds to step S34. On the other hand, when the affirmative result is obtained in the determination of step S32, the distribution pattern generation unit <NUM> changes the value of the standardized charge quantity sq0 to <NUM> (S33).

Next, the distribution pattern generation unit <NUM> substitutes the standardized phase angle sd[i] of the i-th detected partial discharge pulse PL among the partial discharge pulses PL constituting the target partial discharge pulse group into the standardized phase angle sd0, and increments the count values sqc [sq0] [sd0] of the partial discharge pulse counter of the cell <NUM> (<FIG>) in which the standardized charge quantity is sq0 and the standardized phase angle is sd0 (S34).

Next, the distribution pattern generation unit <NUM> determines whether or - not the count value i of the loop counter matches the count value qc of the partial discharge pulse counter finally obtained in step S6 of <FIG> for the target partial discharge pulse group, that is, the total number of partial discharge pulses PL constituting the target partial discharge pulse group (S35).

When a negative result is obtained in this determination, the distribution pattern generation unit <NUM> returns to step S31, and thereafter, repeats the processes of steps S31 to S35 until an affirmative result is obtained in step S35. By this repetitive processing, the count value sqc of the standardized partial discharge pulse number counter of the cell <NUM> for each partial discharge pulse PL constituting the target partial discharge pulse group is counted up.

When obtaining the affirmative result in step S35 by finishing the process in step S34 for all the partial discharge pulses PL constituting the target partial discharge pulse group in due course, the distribution pattern generation unit <NUM> finishes the process of aggregating partial discharge pulse.

As described above, in the partial discharge determination apparatus <NUM> of the present embodiment, the current standardized phase-resolved partial discharge pattern T' is classified into thecategory according to the degree of progress of the partial discharge in the underground power transmission cable <NUM> using the differential data between the previous and current standardized phase-resolved partial discharge patterns T' in addition to the data of the current phase-resolved partial discharge pattern T.

Therefore, according to the present partial discharge determination apparatus <NUM>, the information indicating the variation in the charge quantity and the occurrence phase angle of the partial discharge is included as the determination element, the current standardized phase-resolved partial discharge pattern T' can be classified into the category according to the degree of progress of the partial discharge of the underground power transmission cable <NUM>, and based on this, the degradation diagnosis of the underground power transmission cable <NUM> can be performed. Therefore, the diagnosis can be performed with higher accuracy as compared with the case of performing the diagnosis based only on the distribution pattern of the charge quantity and the phase angle of the partial discharge pulse PL.

In <FIG>, reference numeral <NUM> denotes a differential data generation unit according to the second embodiment applied to the partial discharge determination apparatus <NUM> instead of the differential data generation unit <NUM> in <FIG>.

A differential data generation unit <NUM> according to the present embodiment is different from the differential data generation unit <NUM> according to the first embodiment in that differential data is calculated using not only the previous and current standardized phase-resolved partial discharge patterns T1' and T2' but also a next-to-last standardized phase-resolved partial discharge pattern T0'.

In practice, the differential data generation unit <NUM> of the present embodiment calculates the partial discharge occurrence number difference absolute value for each of the cells <NUM> in the next-to-last and previous standardized phase-resolved partial discharge patterns T0' and T1', thereby acquiring the distribution (hereinafter, this is referred to as a first partial discharge occurrence number difference absolute value distribution) 71A of the partial discharge occurrence number difference absolute value between the next-to-last and previous standardized phase-resolved partial discharge patterns T0' and T1'.

In addition, the differential data generation unit <NUM> of the present embodiment calculates the standardized partial discharge occurrence number difference absolute value for each of the cells <NUM> in the previous and current standardized phase-resolved partial discharge patterns T1' and T2', thereby acquiring the distribution (hereinafter, this is referred to as a second partial discharge occurrence number difference absolute value distribution) 71B of the partial discharge occurrence number difference absolute value between the previous and current standardized phase-resolved partial discharge patterns T1' and T2'.

Then, the differential data generation unit <NUM> adds the partial discharge occurrence number difference absolute value of each cell 72A in the first partial discharge occurrence number difference absolute value distribution 71A acquired as described above and the partial discharge occurrence number difference absolute value of each cell 72B in the second standardized partial discharge occurrence number difference absolute value distribution 71B for each of the corresponding cells 72A and 72B to generate one partial discharge occurrence number difference absolute value distribution <NUM>, and outputs data of the generated partial discharge occurrence number difference absolute value distribution <NUM> to the neural network <NUM> described above with reference to <FIG> as differential data.

By using such a differential data generation unit <NUM> of the present embodiment, it is possible to obtain the partial discharge occurrence number difference absolute value distribution <NUM> in which the temporal variation of the partial discharge is more emphasized as compared with the partial discharge occurrence number difference absolute value distribution <NUM> of the first embodiment described above with reference to <FIG>, and as a result, it is possible to determine the degree of progress of the partial discharge of the target underground power transmission cable <NUM> more accurately as compared with the first embodiment.

In <FIG> in which a part corresponding to that in <FIG> is denoted by the same reference numeral, reference numeral <NUM> denotes a differential data generation unit according to the third embodiment applied to the partial discharge determination apparatus <NUM> instead of the differential data generation unit <NUM> in <FIG>.

The differential data generation unit <NUM> is different from the differential data generation unit <NUM> of the first embodiment in that the sum of the partial discharge occurrence number for each standardized charge quantity and for each standardized phase angle of the partial discharge occurrence number difference absolute value distribution <NUM> obtained based on the previous and current standardized phase-resolved partial discharge patterns T1' and T2' is calculated.

In practice, similarly to the differential data generation unit <NUM> of the first embodiment, the differential data generation unit <NUM> of the present embodiment calculates the partial discharge occurrence number difference absolute value for each of the cells <NUM> in the previous and current standardized phase-resolved partial discharge patterns T1' and T2', thereby acquiring the distribution (standardized partial discharge occurrence number difference absolute value distribution) <NUM> of the partial discharge occurrence number difference absolute value between the previous and current standardized phase-resolved partial discharge patterns T1' and T2'.

Then, the differential data generation unit <NUM> performs sum calculation of adding all the partial discharge occurrence number difference absolute values of the respective cells <NUM> (all the cells <NUM> of the row) of the standardized charge quantity for each of the same standardized charge quantities (that is, for each of the same rows) of the partial discharge occurrence number difference absolute value distribution <NUM> (block of "sum calculation by charge quantity" in <FIG>), and outputs the calculation result for each standardized charge quantity thus obtained to the neural network held by the AI unit <NUM> as a sum SUM1 of partial discharge occurrence number difference absolute values for each charge quantity.

In addition, the differential data generation unit <NUM> performs sum calculation of adding all the partial discharge occurrence number difference absolute values of the respective cells <NUM> (all the cells <NUM> of the column) of the standardized phase angle for each of the same standardized phase angles (that is, for each of the same columns) of the partial discharge occurrence number difference absolute value distribution <NUM> (block of "sum calculation by phase angle" in <FIG>), and outputs the calculation result for each standardized phase angle thus obtained to the neural network held by the AI unit <NUM> as a sum SUM2 of partial discharge occurrence number difference absolute values by phase angle.

On the other hand, <FIG> illustrates a configuration example of the neural network <NUM> of the present embodiment. <FIG> is an example of a case where the neural network <NUM> includes a perceptron including an input layer, a hidden layer, and an output layer.

In the neural network <NUM>, the first unit 82A corresponding to each cell <NUM> (<FIG>) of a current phase-resolved partial discharge <NUM> (<FIG>) is provided in the input layer, and the count value sqc [sq] [sd] of the partial discharge pulse counter of the corresponding cell <NUM> in the current standardized phase-resolved partial discharge pattern T2' is input to each of the first units 82A.

In the input layer of the neural network <NUM>, first units 82B each corresponding to the standardized charge quantity are also provided in the input layer, and the sum SUM1 of partial discharge occurrence number difference absolute values for each charge quantity of the corresponding standardized charge quantity is input to these first units 82B. In addition, in the input layer of the neural network <NUM>, first units 82C each corresponding to the standardized phase angle are also provided in the input layer, and the sum SUM2 of partial discharge occurrence number difference absolute values for each phase angle of the corresponding standardized phase angle is input to these first units 82C.

The hidden layer is provided with a smaller number of second units <NUM> than the total number of first units 82A and 82C in the input layer. The value input to each of the first units 82A to 82C of the input layer is weighted by a preset weight on a line connecting each of the first units 82A to 82C and the corresponding second unit <NUM> of the hidden layer, and is output to the second unit <NUM>. Each second unit <NUM> calculates a sum of input values from each first units 82A to 82C.

The output layer is provided with a smaller number of third units <NUM> than the total number of second units <NUM>. The sum of the input values to that second unit <NUM> respectively calculated in each second unit <NUM> of the hidden layer is weighted by a preset weight on a line connecting that second unit <NUM> and the corresponding third unit <NUM> of the output layer, and is output to the third unit <NUM>. Each of the third units <NUM> calculates a sum of input values from each of the second units <NUM>, and outputs a calculation result.

Note that, in the present embodiment, three third units <NUM> of the output layer are provided, and thereby inputs to the input layer are classified into three categories and output from the neural network <NUM>. Then, the output of the neural network <NUM> is transmitted as a partial discharge determination signal to the cable degradation monitoring apparatus <NUM> (<FIG>) via the transmission frame generation unit <NUM> (<FIG>) and the transmitter <NUM> (<FIG>) as a determination result of the progress of the partial discharge.

According to the partial discharge determination apparatus of the present embodiment using the differential data generation unit <NUM> and the neural network <NUM> described above, the amount of computation of the differential data generation unit <NUM> can be reduced as compared with the partial discharge determination apparatus <NUM> according to the first embodiment. Therefore, in addition to the effect obtained by the first embodiment, it is possible to obtain an effect that the processing time can be shortened.

In <FIG> in which a part corresponding to that in <FIG> is denoted by the same reference numeral, reference numeral <NUM> denotes a differential data generation unit according to the fourth embodiment applied to the partial discharge determination apparatus <NUM> instead of the differential data generation unit <NUM> in <FIG>.

The differential data generation unit <NUM> is different from the differential data generation unit <NUM> of the second embodiment in that the sum of the partial discharge occurrence number for each standardized charge quantity (standardized charge quantity) and for each standardized phase angle (standardized phase angle) of the partial discharge occurrence number difference absolute value distribution <NUM> obtained based on three of the next-to-last, previous, and current standardized phase-resolved partial discharge patterns T0' to T2' is calculated.

In practice, the differential data generation unit <NUM> of the present embodiment calculates the partial discharge occurrence number difference absolute value for each of the cells <NUM> in the next-to-last and previous standardized phase-resolved partial discharge patterns T0' and T1', thereby acquiring the distribution (first standardized partial discharge occurrence number difference absolute value distribution) 71A of the partial discharge occurrence number difference absolute value between the next-to-last and previous standardized phase-resolved partial discharge patterns T0' and T1'.

In addition, the differential data generation unit <NUM> of the present embodiment calculates the partial discharge occurrence number difference absolute value for each of the cells <NUM> in the previous and current standardized phase-resolved partial discharge patterns T1' and T2', thereby acquiring the distribution (second standardized partial discharge occurrence number difference absolute value distribution) 71B of the partial discharge occurrence number difference absolute value between the previous and current standardized phase-resolved partial discharge patterns T1' and T2'.

Then, the differential data generation unit <NUM> adds the partial discharge occurrence number difference absolute value of each cell 72A in the first partial discharge occurrence number difference absolute value distribution 71A acquired as described above and the partial discharge occurrence number difference absolute value of each cell 72B in the second partial discharge occurrence number difference absolute value distribution 71B for each of the corresponding cells 72A and 72B to generate one partial discharge occurrence number difference absolute value distribution <NUM>.

In addition, the differential data generation unit <NUM> performs sum calculation of adding all the partial discharge occurrence number difference absolute values of the respective cells <NUM> (all the cells <NUM> of the row) of the standardized charge quantity for each of the same standardized charge quantities (that is, for each of the same rows) of the partial discharge occurrence number difference absolute value distribution <NUM> (block of "sum calculation by charge quantity" in <FIG>), and outputs the calculation result for each standardized charge quantity thus obtained to the neural network held by the AI unit <NUM> as a sum SUM10 of partial discharge occurrence number difference absolute values for each charge quantity.

Further, the differential data generation unit <NUM> performs sum calculation of adding all the partial discharge occurrence number difference absolute values of the respective cells <NUM> (all the cells <NUM> of the column) of the standardized phase angle for each of the same standardized phase angles (that is, for each of the same columns) of the partial discharge occurrence number difference absolute value distribution <NUM> (block of "sum calculation by phase angle" in <FIG>), and outputs the calculation result for each standardized phase angle thus obtained to the neural network held by the AI unit <NUM> as a sum SUM11 of partial discharge occurrence number difference absolute values by phase angle.

Note that the configuration of the neural network according to the present embodiment is similar to that of the neural network <NUM> according to the third embodiment described above with reference to <FIG>, and thus the description thereof will be omitted here.

According to the partial discharge determination apparatus of the present embodiment using the differential data generation unit <NUM> and the neural network <NUM> described above, in addition to the effects obtained by the first and second embodiments, it is possible to obtain an effect of shortening the processing time as in the third embodiment.

In the first to fourth embodiments, the case where the present invention is applied to the partial discharge determination apparatus <NUM> in which the determination target of the degree of progress of the partial discharge is the underground power transmission cable <NUM> has been described; however, the present invention is not limited thereto, and can be widely applied to various partial determination apparatuses that determine the degree of progress of the partial discharge of the power transmission cable other than the underground power transmission cable <NUM>.

In the first to fourth embodiments described above, the case where the data registration unit <NUM> is configured by the FPGA has been described; however, the present invention is not limited thereto, and the data registration unit <NUM> may be configured as a functional unit of a software configuration embodied by the CPU <NUM> executing a corresponding program.

Furthermore, in the first to fourth embodiments described above, the case where the charge quantity and the occurrence phase angle are standardized using the data of the partial discharge pulse PL occurring in the period of <NUM> cycles of the applied voltage of the target underground power transmission cable <NUM> as one lump has been described; however, the present invention is not limited thereto, and the charge quantity and the occurrence phase angle may be standardized using the data of the partial discharge pulse PL occurring in one or a plurality of cycle periods other than the period of <NUM> cycles as one lump.

In the first to fourth embodiments, the case where the partial discharge occurrence number difference absolute value distributions <NUM> and <NUM> are generated based on the latest two or three standardized phase-resolved partial discharge patterns T' has been described; however, the present invention is not limited thereto, and the partial discharge occurrence number difference absolute value distributions <NUM> and <NUM> may be generated based on the latest four or more standardized phase-resolved partial discharge patterns T'.

Furthermore, in the first to fourth embodiments described above, the case where the AI unit <NUM> performs machine learning as to whether or not the current degree of progress of the partial discharge of the target underground power transmission cable <NUM> belongs to a category at the start of the partial discharge, the middle stage of the partial discharge, or immediately before the dielectric breakdown, and determines the degree of progress of the partial discharge in the target underground power transmission cable <NUM> using the neural networks <NUM> and <NUM> obtained by the learning has been described; however, the present invention is not limited thereto, and the neural networks <NUM> and <NUM> already created by the machine learning may be provided to the AI unit <NUM>, and the AI unit <NUM> may determine the degree of progress of the partial discharge in the target underground power transmission cable <NUM> using the neural networks <NUM> and <NUM>.

Claim 1:
A partial discharge determination apparatus (<NUM>) that determines a degree of progress of a partial discharge occurring in a power transmission cable (<NUM>), the apparatus (<NUM>) comprising:
a distribution pattern generation unit (<NUM>) that generates a distribution pattern of a combination of a charge quantity and an occurrence phase angle of each of the partial discharges occurring in one or a plurality of cycle periods of an applied voltage of the power transmission cable (<NUM>);
a differential data generation unit (<NUM>) that generates differential data including a difference between the numbers of occurrences of the partial discharges for each combination of the charge quantity and the occurrence phase angle in two or more latest distribution patterns generated by the distribution pattern generation unit (<NUM>), respectively; and
a determination unit (<NUM>) that determines the degree of progress of the partial discharge based on data of the latest distribution patterns and the differential data, wherein
the distribution pattern generation unit (<NUM>) standardizes the charge quantity and the occurrence phase angle of each of the partial discharges to generate the distribution pattern in which the numbers of occurrences of the partial discharge for each combination of the standardized charge quantity and occurrence phase angle are aggregated, and
the determination unit (<NUM>) determines the degree of progress of the partial discharge by using data of the latest distribution pattern and the differential data as inputs, and wherein
the differential data generation unit (<NUM>) generates, as the differential data, a result of a sum calculation by charge quantity obtained by adding a difference in the numbers of occurrences of the partial discharges for each combination of the standardized charge quantity and the occurrence phase angle in the two or more latest distribution patterns for each of the standardized charge quantities, wherein the partial discharge determination apparatus (<NUM>) is characterized in that:
a result of a sum calculation by phase angle is obtained by adding a difference in the numbers of occurrences of the partial discharge for each of the combinations for each of the standardized occurrence phase angles.