Monitoring method for remotely and automatically searching faults of power transmission line

Abstract

The invention relates to the technical field of transient recording data processing, and discloses a monitoring method for remotely and automatically searching a power transmission line fault, which comprises the following steps: collecting fault transient recording data; inputting the fault transient recording data into a fault monitoring model, and outputting parameters for customizing a wavelet basis function by the fault monitoring model; a wavelet basis function is self-defined according to parameters output by the fault monitoring model; performing wavelet analysis on the waveform data of the B section according to a self-defined wavelet basis function to obtain the position of a first wave head; calculating a fault position by applying a double-end detection method according to the position of the first wave head; according to the method, mediation is carried out through a wavelet method, the difficulty of mode learning is reduced, the neural network can carry out training convergence in limited training samples, a fault type prediction branch is added to assist parameter updating of feature extraction of an attention layer, the accuracy of wavelet analysis is optimized, and positioning errors are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of transient wave recording data processing, and more specifically, to a remote automatic fault detection and monitoring method for power transmission line. Background Art

[0002] Then, it is necessary to determine the recording wave head according to the fault type, and then use the double-end detection method or the single-end detection method to locate the fault. Traveling wave ranging is divided into single-end ranging and double-end ranging. Double-end ranging is calculated based on the moment when the first wave head of the traveling wave reaches both ends of the line. It only needs to capture the first wave head of the traveling wave, without considering the reflection and refraction of the traveling wave. The amplitude point of the traveling wave head is also the singular point of the signal. It is easy to obtain the corresponding time of this point through wavelet transform. Therefore, double-end ranging has higher accuracy than single-end ranging.

[0003] The role of fault phase selection is mainly to analyze the transient signal of the power system and find or construct a wavelet basis similar to the signal, so that the wavelet transform has higher accuracy and effectiveness in fault detection and analysis.

[0004] Specifically, the fault phase selection must first determine the fault type before it can be carried out. The cause of the transmission line tripping may be lightning strike (including lightning strike or bypass strike) or non-lightning strike (such as tree barriers, wildfires, hanging objects, ice, wind deviation, construction equipment touching the line, etc.). There are differences in the traveling wave current waveforms generated by different fault causes. The existing technology generally determines the fault type by comparing the similarity between the recorded wave and the typical fault waveform spectrum of various types of faults.

[0005] Lightning Shielding

[0006] The monitoring terminal recorded a typical fault waveform as shown in the figure due to the insulator flashover fault caused by lightning bypassing the lightning protection line and hitting the conductor. The waveform characteristics are: the wave tail time is less than 20μs, and there is no reverse polarity pulse in front of the main wave.

[0007] Thunder strikes back

[0008] The typical fault waveform spectrum recorded by the monitoring terminal for insulator flashover fault caused by lightning striking the lightning conductor or tower is shown in the figure. Waveform characteristics: The wave tail time is less than 20μs, and there is a reverse polarity pulse in front of the main wave.

[0009] Tree Barrier

[0010] The typical fault waveform spectrum recorded by the monitoring terminal for the discharge fault caused by tree branches touching the conductor or less than the safe distance is shown in the figure. The waveform characteristics are: the wave head drops very slowly, and the wave head rises steeper than other high-resistance grounding faults; there is intermittent flashover before the main peak of the discharge; the traveling wave amplitude is small (less than 100A), which can be as low as the ampere level.

[0011] Wildfire

[0012] The discharge failure caused by factors such as wildfire causing air thermal ionization and smoke, the typical fault waveform spectrum recorded by the monitoring terminal is shown in the figure. The waveform characteristics are: the rising and falling edges of the main wave are relatively gentle, the wave head and tail time are long, and the traveling wave amplitude is small (less than 300A); the waveform is relatively smooth, there is no obvious pre-discharge feature on the rising edge of the main wave, but there is a weak pre-discharge in the first half of the main discharge.

[0013] Floating objects

[0014] Because the floating objects are hanging on the conductor, ground wire or tower, the conductor and ground potential are less than the safe distance, resulting in discharge fault. The typical fault waveform spectrum recorded by the monitoring terminal is shown in the figure. Waveform characteristics: long wave tail time; traveling wave amplitude ranges from tens of amperes to thousands of amperes.

[0015] Ice Cover

[0016] Ice on the insulator surface causes insulation performance degradation and flashover failure. The typical fault waveform recorded by the monitoring terminal is shown in the figure. Waveform characteristics: The main wave has a steep rising edge, a short wave head time, and a long wave tail time; the traveling wave amplitude is hundreds of amperes.

[0017] Wind deviation

[0018] Due to strong wind, the distance between the conductor and the tower or lightning conductor is short, resulting in a discharge fault. The typical fault waveform spectrum recorded by the monitoring terminal is shown in the figure. Waveform characteristics: The main wave has a steep rising edge, a short wave head time, and a long wave tail time; multiple faults are often sent in a short period of time. Due to the same discharge channel, the main wave has a high similarity.

[0019] Construction machinery hits the line

[0020] The crane was operating close to the conductor, resulting in insufficient safety distance and a fault. The typical fault waveform spectrum recorded by the monitoring terminal is shown in the figure. Waveform characteristics: The rising edge of the main wave is steeper, and the lower edge of the wave tail is steeper than other non-lightning faults; the amplitude of the bear wave is from hundreds of kiloamperes to thousands of kiloamperes.

[0021] In summary, in the existing double-terminal fault location method based on wavelet analysis, it is difficult to construct a wavelet basis similar to the signal, and the fault type identification also directly affects the construction or selection of the wavelet basis, which has an adverse effect on the positioning accuracy of this method. A millimeter-level wave head recognition error will lead to a hundred-meter-level positioning error. Summary of the invention

[0022] The present invention provides a remote automatic power transmission line fault monitoring method to solve the technical problems in related technologies.

[0023] The present invention provides a remote automatic fault detection and monitoring method for power transmission line, comprising:

[0024] Collect fault transient recording data;

[0025] The fault transient recording data includes the waveform data of segments A, B, C, and D;

[0026] The fault transient recording data is input into the fault monitoring model, and the fault monitoring model outputs the parameters used to customize the wavelet basis function;

[0027] Customize the wavelet basis function according to the parameters output by the fault monitoring model;

[0028] Perform wavelet analysis on the waveform data of segment B according to the custom wavelet basis function to obtain the position of the first wave head;

[0029] The fault location is calculated using the double-end detection method based on the location of the first wave head.

[0030] Furthermore, the method also includes the step of sending the fault location and fault type to operation and maintenance personnel.

[0031] Furthermore, the fault monitoring model includes:

[0032] The attention layer is used to input the waveform data of segments A, B, C, and D of the fault transient recording data, and then output the weighted features of segment A, segment B, segment C, and segment D;

[0033] The first convolution layer is used to input the weighted features of segment B and then output the first convolution features;

[0034] The second convolution layer is used to input the weighted features of segment B and then output the second convolution features;

[0035] The first fully connected layer is used to input the first convolution feature and then output a classification result representing the fault type.

[0036] The second fully connected layer is used to input the second convolution feature and then output a result representing the parameters of the wavelet basis function of the wavelet analysis.

[0037] Furthermore, each waveform segment A, B, C, D is itself a time series, containing waveform sampling values ​​of multiple time steps. They are regarded as four independent input sequences, denoted as X A ,X B ,X C ,X D ;

[0038] For each sequence, add the corresponding segment type embedding;

[0039] The four enhanced input sequences X′ A , X′ B , X′ C , X′D Splice them into a long sequence X′=[X′ A , X′ B , X′ C , X′ D ];

[0040] Finally, X′ is input into the attention layer.

[0041] Furthermore, the calculation formula of the attention layer is as follows:

[0042] Perform a linear transformation on X′ to obtain the Query matrix Q, Key matrix K and Value matrix V.

[0043] Q=X′W Q

[0044] K=X′W K

[0045] V=X′W V

[0046] in is the learnable weight matrix, d k is the dimension of the attention head.

[0047] Then calculate the attention weight matrix

[0048]

[0049] Finally, the attention weight matrix is ​​multiplied by the Value matrix and linearly transformed to get the output

[0050] Z=AVW O

[0051] in is the output layer weight matrix;

[0052] Z has the same number of rows as X, and is then reversely split into weighted features of segment A, weighted features of segment B, weighted features of segment C, and weighted features of segment D.

[0053] Furthermore, one-hot encoding is used to define an embedding vector for each waveform segment type.

[0054] Furthermore, the output parameter θ contains the following dimensions:

[0055] Mother wavelet type: ID real number encoding or one-hot encoding of the mother wavelet function;

[0056] Scaling parameter a(θ): controls the scale of the wavelet;

[0057] Translation parameter b(θ): controls the time position of the wavelet;

[0058] Modulation parameter ω(θ): controls the frequency of the wavelet.

[0059] Apply the scaling parameter, translation parameter, and modulation parameter to the mother wavelet function to obtain the parameterized adaptive wavelet basis function:

[0060] ψ(θ,a,b,t)=a(-0.5)*ψ((tb) / a)*exp(jωt)

[0061] Where j is the imaginary unit.

[0062] The present invention provides a remote automatic fault detection and monitoring system for power transmission line, comprising:

[0063] Recording equipment used for recording transient fault conditions;

[0064] A data transmission module for transmitting data from the recording equipment to the monitoring center;

[0065] A monitoring center for executing the aforementioned remote automatic power transmission line fault monitoring method;

[0066] The monitoring center includes a computer capable of executing computer readable instructions.

[0067] The present invention provides a storage medium storing computer-readable instructions, which, when read, can execute the aforementioned method for remotely and automatically finding power transmission line faults and monitoring.

[0068] The beneficial effects of the present invention are as follows: the present invention uses the wavelet method as an intermediary to reduce the difficulty of pattern learning, enables the neural network to be trained and converged in a limited training sample, and adds a fault type prediction branch to assist the parameter update of the feature extraction of the attention layer, optimizes the accuracy of wavelet analysis, and reduces the positioning error. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of a remote automatic power transmission line fault monitoring method of the present invention;

[0070] Figure 2 Dual-end detection method based on GPS / Beidou;

[0071] Figure 3 Single-end detection method based on GPS / Beidou;

[0072] Figure 4 Module diagram of the fault monitoring model. DETAILED DESCRIPTION

[0073] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and implement the subject matter described herein, and the functions and arrangements of the elements discussed may be changed without departing from the scope of protection of the present specification. Various examples may omit, replace, or add various processes or components as needed. In addition, the features described in some examples may also be combined in other examples.

[0074] At least one embodiment of the present invention discloses a remote automatic fault detection and monitoring method for power transmission line. Figure 1 As shown, including:

[0075] Step 100, collecting fault transient recording data;

[0076] The fault transient recording data includes the waveform data of segments A, B, C, and D;

[0077] The following are the definitions and general explanations of these four waveform data in general:

[0078] Section A (before fault)

[0079] Definition: Section A is the voltage and current waveforms under normal operating conditions for a period of time before a fault occurs.

[0080] effect:

[0081] Provides a baseline of the normal operating status of the system before the failure.

[0082] Used to compare the changes in electrical parameters before and after a fault, helping to identify the fault type and fault point.

[0083] As a reference for system stability and power quality assessment.

[0084] Section B (fault start section)

[0085] Definition: Segment B is the moment when the fault occurs and records the transient signal at the initial stage of the fault.

[0086] effect:

[0087] Capturing the first wave when a fault occurs is the key data for fault location.

[0088] It reflects the initial characteristics of the fault, including sudden changes in voltage and current.

[0089] Helps analyze the initial characteristics of the fault type (such as short circuit, ground fault, etc.).

[0090] Section C (fault period)

[0091] Definition: Segment C records the voltage and current waveforms during the fault period, including transient and steady-state signals.

[0092] effect:

[0093] It reflects the system response after a fault occurs, including the action process of the protection device.

[0094] Helps analyze the duration and impact of a fault.

[0095] Provides waveform data during fault period for detailed fault process analysis.

[0096] Section D (fault-post section)

[0097] Definition: Segment D is the voltage and current waveform after the fault is cleared, recording the system status after the fault is restored.

[0098] effect:

[0099] Reflects the system recovery process and stable status after the fault is cleared.

[0100] Helps assess the long-term impact and recovery of a failure on the system.

[0101] Provides system stability and power quality data after a fault.

[0102] Step 200, inputting the fault transient recording data into the fault monitoring model, and the fault monitoring model outputs parameters for customizing the wavelet basis function;

[0103] Step 300, customizing a wavelet basis function according to the parameters output by the fault monitoring model;

[0104] Step 400, performing wavelet analysis on the waveform data of segment B according to the custom wavelet basis function to obtain the position of the first wave head;

[0105] Step 500, calculating the fault location by applying a double-end detection method according to the location of the first wave head.

[0106] In one embodiment of the present invention, the method further includes sending the fault location and fault type to the operation and maintenance personnel, so that the operation and maintenance personnel can quickly find the fault point and carry out targeted repairs more quickly.

[0107] The fault monitoring model includes:

[0108] The attention layer is used to input the waveform data of segments A, B, C, and D of the fault transient recording data, and then output the weighted features of segment A, segment B, segment C, and segment D;

[0109] Each waveform segment A, B, C, D is itself a time series, containing waveform sampling values ​​of multiple time steps. They can be regarded as four independent input sequences, denoted by X A , X B , X C , X D .

[0110] For each sequence, add the corresponding segment type embedding. For example, for X A , copy and expand the embedding vector of segment A to be the same as X A Matrices of equal length, denoted by E A Then X A and E A Concatenate on the feature dimension to get X′ A .

[0111] The four enhanced input sequences X′ A , X′ B , X′ C , X′ D Splice them into a long sequence X′=[X′ A , X′ B , X′ C , X′ D ].

[0112] Finally, X′ is input into the attention layer;

[0113] The calculation formula of the attention layer is as follows:

[0114] Perform a linear transformation on X′ to obtain the Query matrix Q, Key matrix K and Value matrix V.

[0115] Q=X′W Q

[0116] K=X′W K

[0117] V=X′W V

[0118] in is the learnable weight matrix, d k is the dimension of the attention head.

[0119] Then calculate the attention weight matrix

[0120]

[0121] Finally, the attention weight matrix is ​​multiplied by the Value matrix and linearly transformed to get the output

[0122] Z=AVW O

[0123] in is the output layer weight matrix;

[0124] Z has the same number of rows as X, and is then reversely split into weighted features of segment A, weighted features of segment B, weighted features of segment C, and weighted features of segment D.

[0125] For example, X in X A If it is the 1st to 100th rows, then the 1st to 100th rows of Z are separated as the weighted features of segment A.

[0126] In one embodiment of the present invention, an embedding vector is defined for each waveform segment type. For example, if one-hot encoding is used, segment A corresponds to [1, 0, 0, 0], segment B corresponds to [0, 1, 0, 0], segment C corresponds to [0, 0, 1, 0], and segment D corresponds to [0, 0, 0, 1].

[0127] In one embodiment of the present invention, the waveform sampling value of a sampling may be a vector including dimensions such as voltage and current.

[0128] The first convolution layer is used to input the weighted features of segment B and then output the first convolution features;

[0129] The second convolution layer is used to input the weighted features of segment B and then output the second convolution features;

[0130] The first fully connected layer is used to input the first convolution feature and then output a classification result representing the fault type.

[0131] A second fully connected layer, which is used to input the second convolution feature and then output a result representing the parameters of the wavelet basis function of the wavelet analysis;

[0132] In one embodiment of the present invention, the convolution layer adopts 1D convolution, and in this case, the weighted feature tensor needs to be input as a vector.

[0133] In one embodiment of the present invention, the convolution layer adopts 2D convolution.

[0134] The two convolutional layers can use the same convolutional structure or different convolutional structures, and can use existing convolutional architectures, such as AlexNet, VGG, etc.

[0135] Let the output of the second fully connected layer be a set of parameters θ, which define the adaptive wavelet basis function ψ(θ,a,b,t). Given an input signal x(t), its wavelet transform coefficients can be calculated:

[0136] W(θ,a,b)=∫x(t)ψ(θ,a,b,t)dt

[0137] Then, it is necessary to perform wave head detection on the wavelet coefficients W(θ,a,b). Design a wave head detection function f to map the wavelet coefficients to the predicted wave head position:

[0138]

[0139] in, is the predicted wave head position.

[0140] Assume that there is a set of training samples (xi(t), pi) (i=1→N), where xi(t) is the i-th input signal and pi is the corresponding true wave head position. The following loss function can be designed:

[0141] L(θ)=1 / N*∑(i=1→N)d(pi,f(W(θ,a,b)))

[0142] Where d(·) is a function that measures the error between the predicted wave head position and the true wave head position, such as square error, absolute value error, etc.

[0143] During the training process, the loss function L(θ) is minimized through optimization algorithms such as gradient descent to find the optimal neural network parameters θ*:

[0144] θ*=argmin(θ)L(θ)

[0145] The wavelet basis function ψ(θ, a, b, t) defined by θ can generate wavelet coefficient representation in the wavelet transform domain that is conducive to wave head identification, and then the wave head position is accurately predicted through the wave head detection function f. The whole process is an end-to-end optimization, directly targeting the wave head identification error without predetermining the desired wavelet basis function shape.

[0146] The cost of defining the most optimized wavelet basis function to mark the training samples is too high. Therefore, in the embodiment provided by the present invention, the error of identifying the wave head through the wavelet basis function by the wavelet method is used as the loss basis of the loss function. When marking the wave head, the position of the wave head can be obtained by inferring the fault position. This greatly reduces the difficulty of marking. Combined with simulated faults, training samples can be generated more accurately and simply, and the training samples can be marked.

[0147] In addition, in view of the high sampling frequency of the fault transient recording, the large amount of data, and the different sampling frequencies of the four segments A, B, C, and D, pattern learning is very difficult. It is very difficult to converge the fault location result directly output by the neural network, especially when the error of fault location is reduced to below the level of 100 meters. It is basically impossible to converge. Therefore, the present invention uses the wavelet method as an intermediary to reduce the difficulty of pattern learning, so that the neural network can be trained and converged in a limited training sample, and a fault type prediction branch is added to assist the parameter update of the feature extraction of the attention layer.

[0148] Therefore, in the embodiment of the present invention, the loss function of the fault monitoring model is a weighted sum of the classification loss of the fault type and the error of the wave head.

[0149] The classification loss function of the fault type adopts the general cross entropy loss function.

[0150] The parameters θ of the neural network have the following dimensions:

[0151] Mother wavelet type: ID real number encoding or one-hot encoding of mother wavelet functions such as Morlet wavelet and Mexican Hat wavelet;

[0152] Scaling parameter a(θ): controls the scale of the wavelet, that is, the width of the wavelet. The larger a is, the wider the wavelet is, the lower the frequency resolution is, and the higher the time resolution is; vice versa. One of the parameters θ can be used a Generate the scaling parameter through exponential mapping: a = exp(θ a ).

[0153] Translation parameter b(θ): controls the time position of the wavelet. The larger the b, the more the wavelet is translated to the right. You can directly use a parameter θ in θ b Indicates the amount of translation: b = θ b .

[0154] Modulation parameter ω(θ): controls the frequency of the wavelet. The larger the ω, the higher the frequency of the wavelet and the faster the oscillation. ω The modulation parameters are generated by exponential mapping: ω = exp(θ ω ).

[0155] Other shape parameters: Depending on the definition of the chosen mother wavelet, there may be other parameters that control the wavelet shape, such as the bandwidth parameter σ of the Morlet wavelet. These parameters can also be generated using the corresponding elements in θ.

[0156] Applying these transformations to the mother wavelet function yields parameterized adaptive wavelet basis functions:

[0157] ψ(θ,a,b,t)=a(-0.5)*ψ((tb) / a)*exp(jωt)

[0158] Where j is an imaginary unit. Here, the mother wavelet function is scaled, translated and modulated.

[0159] The neural network learns the parameters θ = θ a ,θ b ,θ ω ,..., generate the optimal wavelet basis function cluster ψ(θ,a i ,b i ,t)(i=1→n), so that it can generate wavelet coefficient representation W(θ,a,b) in the wavelet transform domain which is beneficial to wave head identification.

[0160] It should be noted that the method of obtaining the first wave head by wavelet analysis is a prior art and will not be described in detail here.

[0161] If there is no special explanation, the aforementioned neural network refers to the fault monitoring model.

[0162] The present invention provides a remote automatic fault detection and monitoring system for power transmission line, comprising:

[0163] Recording equipment used for recording transient fault conditions;

[0164] A data transmission module for transmitting data from the recording equipment to the monitoring center;

[0165] A monitoring center for executing the aforementioned remote automatic power transmission line fault monitoring method;

[0166] The monitoring center includes a computer capable of executing computer readable instructions.

[0167] The present invention provides a storage medium storing computer-readable instructions, which, when read, can execute the aforementioned method for remotely and automatically finding power transmission line faults and monitoring.

[0168] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation mode. The above-mentioned specific implementation mode is merely illustrative and not restrictive. Under the guidance of this embodiment, ordinary technicians in this field can also make more forms of equivalent embodiments, all of which are within the protection of this embodiment.

Claims

1. A remote automatic fault detection and monitoring method for power transmission line, characterized in that: include: Collect fault transient recording data; The fault transient recording data includes the waveform data of segments A, B, C, and D; The fault transient recording data is input into the fault monitoring model, and the fault monitoring model outputs the parameters used to customize the wavelet basis function; Customize the wavelet basis function according to the parameters output by the fault monitoring model; Perform wavelet analysis on the waveform data of segment B according to the custom wavelet basis function to obtain the position of the first wave head; The fault location is calculated using the double-end detection method based on the location of the first wave head.

2. A remote automatic fault detection and monitoring method for power transmission line according to claim 1, characterized in that: The step of sending the fault location and fault type to the operation and maintenance personnel is also included.

3. A remote automatic fault detection and monitoring method for power transmission line according to claim 1, characterized in that: The fault monitoring model includes: The attention layer is used to input the waveform data of segments A, B, C, and D of the fault transient recording data, and then output the weighted features of segment A, segment B, segment C, and segment D; The first convolution layer is used to input the weighted features of segment B and then output the first convolution features; The second convolution layer is used to input the weighted features of segment B and then output the second convolution features; A first fully connected layer, which is used to input the first convolution feature and then output a classification result representing the fault type; The second fully connected layer is used to input the second convolution feature and then output a result representing the parameters of the wavelet basis function of the wavelet analysis.

4. A remote automatic fault detection and monitoring method for power transmission line according to claim 3, characterized in that: Each waveform segment A, B, C, D is a time series, which contains waveform sampling values ​​of multiple time steps. They are regarded as four independent input sequences, which are recorded as X A , X B , X C , X D ; For each sequence, add the corresponding segment type embedding; The four enhanced input sequences X′ A , X′ B , X′ C , X′ D Splice them into a long sequence X′=[X′ A , X′ B , X′ C , X′ D ]; Finally, X′ is input into the attention layer.

5. A remote automatic fault detection and monitoring method for power transmission line according to claim 4, characterized in that: The calculation formula of the attention layer is as follows: Perform linear transformation on X′ to obtain the Query matrix Q, Key matrix K and Value matrix V; Q=X′W Q K=X′W K V=X′W V in is the learnable weight matrix, d k is the dimension of the attention head; Then calculate the attention weight matrix Finally, the attention weight matrix is ​​multiplied by the Value matrix and linearly transformed to get the output Z=AVW O in is the output layer weight matrix; Z has the same number of rows as X, and is then reversely split into weighted features of segment A, weighted features of segment B, weighted features of segment C, and weighted features of segment D.

6. A remote automatic fault detection and monitoring method for power transmission line according to claim 4, characterized in that: An embedding vector is defined for each waveform segment type using one-hot encoding.

7. A remote automatic fault detection and monitoring method for power transmission line according to claim 1, characterized in that: The output parameters θ contain the following dimensions: Mother wavelet type: ID real number encoding or one-hot encoding of the mother wavelet function; Scaling parameter a(θ): controls the scale of the wavelet; Translation parameter b(θ): controls the time position of the wavelet; Modulation parameter ω(θ): controls the frequency of the wavelet.

8. A remote automatic fault detection and monitoring method for power transmission line according to claim 1, characterized in that: Apply the scaling parameter, translation parameter, and modulation parameter to the mother wavelet function to obtain the parameterized adaptive wavelet basis function: ψ(θ,a,b,t)=a(-0.5)*ψ((tb) / a)*exp(jωt) Where j is the imaginary unit.

9. A remote automatic fault detection and monitoring system for power transmission lines, characterized in that: It includes: Recording equipment used for recording transient fault conditions; A data transmission module for transmitting data from the recording equipment to the monitoring center; A monitoring center for executing a remote automatic power transmission line fault monitoring method as described in any one of claims 1 to 8; The monitoring center includes a computer capable of executing computer readable instructions.

10. A storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are read, the method for remotely and automatically finding power transmission line faults and monitoring the power transmission line faults as described in any one of claims 1 to 8 can be executed.

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