A method and system for fault location in transformer substations based on power line carrier time synchronization

Abstract

This invention relates to the field of smart grid and distribution network automation technology, and discloses a method and system for fault location in distribution transformer areas based on power line carrier time synchronization. The key technical points are: addressing the problems of difficulty in deploying high-precision time synchronization networks in existing low-voltage distribution transformer areas, power line carrier communication interference traveling wave feature extraction, and poor robustness of single ranging algorithms, this invention adopts a power line carrier microsecond-level synchronization mechanism with hardware delay compensation, combined with distributed terminal local active cancellation of carrier interference based on preamble channel estimation and lightweight extraction of fault features, and through intelligent fusion terminal collaborative judgment based on traveling wave and power frequency impedance multi-source algorithm with dynamic confidence weight, high-precision fault location in complex low-voltage distribution transformer areas is achieved.

Description

Technical Field

[0001] This invention relates to the field of smart grid and distribution network automation technology, and more specifically, to a method and system for locating transformer area faults based on power line carrier time synchronization. Background Technology

[0002] Currently, 400V low-voltage distribution substations have complex structures and numerous branches. Due to cost and construction environment constraints, it is difficult to deploy high-precision time synchronization networks relying on fiber optics or independent satellites on a large scale. This results in distributed monitoring terminals lacking a unified microsecond-level absolute time reference, making it impossible to support traveling wave ranging systems. At the same time, low-voltage distribution lines not only suffer from severe power frequency waveform distortion due to frequent starts and stops of heavy loads, making traditional fault detection prone to false alarms, but also experience strong local background interference due to the high frequency overlap between broadband power line carrier (PLC) communication signals and fault traveling waves. This causes severe attenuation of high-frequency transient characteristics and makes it difficult to accurately extract the traveling wave front. In addition, the effective communication bandwidth of low-voltage PLC networks is extremely narrow and subject to concurrent conflicts, making it impossible to meet the centralized backhaul requirements of massive full waveform data. Existing single ranging algorithms are not robust enough and are prone to failure when dealing with such harsh substation conditions with strong noise, high distortion, and multiple branches. As a result, it has been difficult to achieve low-cost, high-precision fault location at the low-voltage substation level for a long time.

[0003] Therefore, the present invention provides a method and system for locating transformer area faults based on power line carrier time synchronization, which improves the above-mentioned technical problems. Summary of the Invention

[0004] This disclosure aims to address the shortcomings of existing technologies by providing a method and system for fault location in low-voltage distribution areas based on power line carrier time synchronization. The invention employs a power line carrier microsecond-level synchronization mechanism with hardware delay compensation, combined with active carrier interference cancellation and lightweight fault feature extraction from the local distributed terminal, and performs collaborative analysis through a traveling wave and impedance multi-source algorithm based on dynamic confidence weight by an intelligent fusion terminal, thereby achieving high-precision fault location in complex low-voltage distribution areas.

[0005] To achieve the above objectives, the present disclosure proposes the following technical solutions:

[0006] In a first aspect, embodiments of this disclosure propose a method and system for locating transformer area faults based on power line carrier time synchronization, comprising the following steps:

[0007] S1. Constructing a carrier synchronization network: The carrier master clock module on the transformer side sends synchronization messages to the distributed fault monitoring terminal through the power line carrier network, and uses physical layer hardware timestamp marking and round-trip time measurement and dynamic compensation mechanism to achieve microsecond-level synchronization between the slave clock and the carrier master clock module in the fault monitoring terminal, with a synchronization accuracy of no more than 5μs.

[0008] S2. Local Fault Trigger Detection: The fault monitoring terminal monitors the three-phase voltage and current electrical quantities of the line in real time to obtain the original discrete sampling sequence. The zero-sequence voltage feature components are extracted, and a multi-criteria adaptive threshold model combining power frequency differential energy mutation and high-frequency transient energy is used to determine the fault type and start high-frequency recording with a sampling rate of not less than 1MHz.

[0009] S3. Feature Extraction and Signal Cleaning: The fault monitoring terminal reconstructs the carrier interference signal using an active cancellation strategy based on the power line carrier preamble sequence channel estimation, and removes the carrier interference signal from the actual received mixed signal to obtain the cleaned signal. Then, the absolute time scale of the traveling wavefront and the confidence level of the wavefront extraction are extracted from the cleaned signal.

[0010] S4. Feature Data Upload: The fault monitoring terminal will upload the extracted traveling wave front absolute time scale, wave front extraction confidence level, and phasor containing the fault power frequency voltage. Phasor of fault power frequency current The electrical characteristics are encapsulated into a lightweight feature data packet with a data length of no more than 100 bytes and uploaded to the intelligent fusion terminal;

[0011] S5. Integrated Positioning Analysis: The intelligent integrated terminal extracts confidence scores and assigns weights based on the wavefronts reported by each fault monitoring terminal, performs weighted multi-terminal traveling wave positioning calculations to obtain the traveling wave method positioning results, and combines this with... and The power frequency impedance method location results obtained from the solution are subjected to dynamic confidence weighted fusion decision-making to output the precise location of the fault point.

[0012] As a preferred technical solution of the present invention, in step S1, the round-trip time measurement and dynamic compensation mechanism specifically includes: measuring the round-trip time of the synchronization message through multiple interactions between the carrier master clock module and the fault monitoring terminal, and dynamically compensating for the asymmetric delay on the power line path.

[0013] As a preferred embodiment of the present invention, in step S2, the multi-criteria adaptive threshold model includes a power frequency differential energy detection part:

[0014] Based on the original discrete sampling sequence Calculate the differential energy of adjacent power frequency cycles. :

[0015] ;

[0016] in, This is the discrete-time index for the current calculation; For the index of the variable in the summation operation; This represents the total number of sampling points in the system within one power frequency cycle. The original discrete sampling sequence In the index The sampling point value at that location; For the sequence Middle and sampling points The difference is between the historical sampling point values ​​of a complete power frequency cycle;

[0017] Set the power frequency differential energy adaptive threshold ;

[0018] in, For multiple consecutive power frequency cycles before the fault occurred The moving average; Within this historical cycle Standard deviation; This is the dynamic sensitivity adjustment coefficient; when At that time, the waveform change flag is triggered.

[0019] As a preferred embodiment of the present invention, in step S2, the multi-criteria adaptive threshold model further includes a high-frequency transient energy detection component:

[0020] From the original discrete sampling sequence Extracting high-frequency components And calculate the short-time high-frequency transient energy. :

[0021] ;

[0022] in, Calculate the current discrete-time index for the high-frequency components; To calculate the sliding data window length for short-time energy, For absolute value operations;

[0023] Set a high-frequency transient energy adaptive threshold ,in, Estimation of the mean of high-frequency background noise; For fault tolerance margin;

[0024] If the waveform change flag is triggered simultaneously and the short-time high-frequency transient energy exceeds [the threshold], then [the following conditions are met]. If the zero-sequence voltage change is satisfied, it is determined to be a phase-to-phase short circuit or a direct ground fault; If the preset zero-sequence threshold is exceeded, it is determined to be a high-resistance grounding fault. This is the differential amplitude calculated based on the zero-sequence voltage characteristic component.

[0025] As a preferred embodiment of the present invention, in step S3, the active cancellation strategy based on the power line carrier preamble sequence channel estimation specifically includes: obtaining the channel impulse response estimate using the least squares method. :

[0026] ;

[0027] in, The theoretical channel impulse response sequence to be optimized; To actually receive mixed signals; Given a known power line carrier synchronization preamble sequence; This is the convolution operator; To optimize variables when the objective function is minimized; This indicates the calculation of the sum of squares of the residuals.

[0028] As a preferred technical solution of the present invention, in step S3, the confidence level of wavehead extraction is performed. The calculation formula is:

[0029] ;

[0030] in, The peak value is the modulus maxima of the wavelet transform. The standard deviation of the noise before the wavefront arrives; The standard deviation of wavefront arrival time across multiple scales; This represents the average wavefront arrival time across multiple scales.

[0031] As a preferred embodiment of the present invention, in step S4, the data structure of the lightweight feature data packet includes terminal ID, absolute fault start time, absolute time stamp of traveling wave front, and fault power frequency voltage phasor. Fault frequency current phasor Fault type flag, terminal location topology identifier, and CRC checksum.

[0032] As a preferred embodiment of the present invention, step S5, the weighted multi-terminal traveling wave positioning calculation specifically includes: constructing a weighted nonlinear least squares objective function. :

[0033] ;

[0034] in, Let be the objective function to be minimized: the sum of squared errors. The location of the fault point to be solved; The absolute time of the fault occurrence; The equivalent propagation wave velocity to be solved jointly; Number the fault monitoring terminals participating in the calculation with an index; The total number of fault monitoring terminals participating in the calculation; The weighting coefficients are used to extract confidence scores based on the wavefront. The corresponding absolute timescale of the traveling wavefront; For the first Each fault monitoring terminal reaches the hypothetical fault point along the actual physical topology of the distribution network. The path length function;

[0035] The objective function is solved iteratively to obtain the traveling wave method positioning distance. and the overall reliability of the traveling wave method .

[0036] As a preferred embodiment of the present invention, step S5, the dynamic confidence-weighted fusion decision specifically includes:

[0037] Using fault power frequency voltage phasors and fault power frequency current phasor Based on the impedance method loop equation Calculating the distance using impedance method And quantify the reliability of the impedance method. :

[0038] ;

[0039] in, The complex impedance per unit length; This is the current of the transition resistance; For transition resistance; This is the change in impedance; The nominal impedance setting; This is the normalized voltage signal-to-noise ratio; For amplitude limiting calculation;

[0040] Calculate the difference between the positioning distance using the traveling wave method and the positioning distance using the impedance method. ,like 50m, then the precise location of the fault. Obtained by weighted fusion:

[0041] .

[0042] Secondly, this disclosure proposes a transformer area fault location system based on power line carrier time synchronization, the system comprising: a carrier master clock module, a distributed fault monitoring terminal, and an intelligent fusion terminal;

[0043] The carrier master clock module is deployed on the transformer side of the distribution area and is used to maintain a time synchronization reference in the power line carrier network through physical layer hardware stamping and round-trip time measurement.

[0044] The distributed fault monitoring terminal is deployed at each transformer substation node to perform local fault trigger detection, active cancellation of carrier interference signals, traveling wave feature extraction and wavefront extraction confidence calculation, and encapsulate and upload lightweight feature data packets.

[0045] The intelligent fusion terminal communicates with the distributed fault monitoring terminal and integrates a positioning calculation engine to perform multi-source feature data association, weighted multi-terminal traveling wave positioning calculation based on wavefront extraction confidence weights, and fusion analysis with the power frequency impedance method to output the precise location of the fault.

[0046] In summary, the present invention has the following beneficial effects:

[0047] Firstly, this invention utilizes a power line carrier (PLC) synchronization mechanism with physical layer hardware stamping and round-trip time (RTT) dynamic delay compensation to achieve microsecond-level absolute time synchronization among nodes in the entire distribution network without the need for additional deployment of fiber optic channels or BeiDou / GPS satellite receiving modules. The synchronization accuracy is stable within ±2μs, laying the foundation for the large-scale, low-cost application of high-precision distributed traveling wave ranging in complex 400V low-voltage distribution areas.

[0048] Secondly, addressing the technical bias and challenge of high frequency overlap between low-voltage PLC broadband communication signals and fault traveling waves, this invention proposes a "local interference active cancellation" strategy based on preamble channel impulse response estimation. This strategy completely eliminates carrier background noise while preserving the true high-frequency abrupt change characteristics of the traveling wave front to the greatest extent, avoiding the severe attenuation of the traveling wave signal by traditional fixed filtering methods. Under full-load interference of PLC communication, it significantly improves the accuracy of wave front arrival time calibration.

[0049] Thirdly, this invention adopts a dual detection mechanism that combines continuous waveform differential energy mutation and high-frequency short-time transient energy, replacing the traditional total harmonic distortion rate calculation, and introduces an adaptive threshold model based on historical power frequency fluctuation statistics. This scheme can accurately filter out normal waveform distortion caused by frequent start-stop of high-power nonlinear loads in low-voltage distribution areas, effectively preventing false triggering and missed detection of high-frequency fault recordings.

[0050] Fourth, this invention abandons the traditional full waveform upload master station analysis mode and adopts a system architecture of "edge-side local feature extraction + lightweight data packet backhaul". The distributed monitoring terminal only needs to encapsulate and concurrently upload approximately 86 bytes of extremely simple feature time stamp data, which completely avoids the risk of PLC channel congestion and packet loss caused by multiple nodes reporting concurrently at the moment of fault, ensuring the real-time access rate of the positioning system and the fault location response time is less than 100ms.

[0051] Fifth, this invention constructs a weighted nonlinear least squares multi-terminal traveling wave ranging model based on wavefront extraction and dynamic confidence weights, and creatively introduces a power frequency impedance method based on signal-to-noise ratio normalization for multi-source confidence arbitration and weighted fusion. This mechanism can not only effectively suppress pseudo-wavefront errors caused by interference to a single terminal, but also smoothly transition to the impedance method under extreme operating conditions with weak traveling wave characteristics, fundamentally solving the problem that single ranging algorithms are prone to failure under harsh transformer substation conditions. The average error of phase-to-phase short-circuit fault location is 8.2 meters, with a maximum error not exceeding 15 meters, and the average error of high-resistance grounding fault location (transition resistance 1kΩ) is 12.6 meters. Attached Figure Description

[0052] Figure 1 A flowchart of a transformer area fault location method based on power line carrier time synchronization provided for an embodiment of the present invention;

[0053] Figure 2 This is a framework diagram of a transformer area fault location system based on power line carrier time synchronization, provided for an embodiment of the present invention. Detailed Implementation

[0054] The present application will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0055] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0056] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The term "and / or" as used in this specification includes any and all combinations of one or more of the associated listed items.

[0057] Furthermore, the technical features involved in the various embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0058] This disclosure aims to address the challenges of large-scale application of existing power distribution network fault location technologies in 400V low-voltage distribution areas, as well as the poor stability and low location accuracy of wavefront feature extraction under strong power line carrier (PLC) interference. Therefore, this disclosure proposes a power line carrier time synchronization-based method and system for distribution area fault location. This method utilizes power line carrier synchronization technology with physical layer hardware stamping and path delay compensation to achieve network-wide time synchronization. Through active cancellation of local PLC interference and fusion analysis of multi-terminal traveling wave and power frequency impedance methods based on confidence weights, it achieves low-cost, high-precision fault location in complex, high-noise low-voltage distribution area environments.

[0059] Please refer to Figure 1 , Figure 1 A flowchart of a transformer area fault location method based on power line carrier time synchronization, as described in an embodiment of this disclosure, is shown. The overall process mainly includes the following five steps:

[0060] S1. Construction and maintenance of time synchronization network based on power line carrier.

[0061] A carrier master clock module (T-GM) is installed on the transformer side of the distribution area to serve as the time reference source for the entire distribution area. Synchronization messages are sent to the fault monitoring terminals (FMT) distributed at each key node through the power line carrier communication network (PLC-TSN).

[0062] To overcome the impact of asymmetric delay in low-voltage power line channels, this embodiment employs hardware timestamp technology at the physical / MAC layer, combined with a round-trip time (RTT) measurement mechanism. The T-GM and FMT measure the round-trip time of messages through multiple interactions and dynamically compensate for asymmetric delays on the path. The slave clock within the FMT maintains microsecond-level synchronization with the master clock accordingly, with a synchronization accuracy of no more than 5μs, laying a unified and accurate absolute time stamp foundation for the subsequent extraction of fault features.

[0063] S2, Distributed terminal local fault detection and triggering.

[0064] Each FMT continuously monitors the three-phase voltage and current electrical quantities of the local line and calculates and extracts characteristic components such as zero-sequence voltage in real time. In order to effectively distinguish between normal load distortion and real faults in low-voltage distribution areas with frequent load fluctuations, a "multi-criteria adaptive threshold comprehensive discrimination" model is adopted.

[0065] S2.1 Waveform Distortion Detection: Continuous waveform differential energy mutation detection is adopted to replace the traditional total harmonic distortion rate calculation, so as to capture the moment of fault onset more sensitively and quickly; the differential energy of adjacent cycles is calculated. The formula is:

[0066] ;

[0067] in, This is the discrete-time index for the current calculation; For the discrete-time variable index of the summation operation; This represents the total number of sampling points in the system within one power frequency cycle. The original discrete sampling sequence to be obtained; For the original discrete sampling sequence at the index The sampling point value at that location; For the sequence and the sampling point The difference is exactly one complete power frequency cycle of historical sampling point values.

[0068] Set the power frequency differential energy adaptive threshold In order to dynamically adapt to the power frequency fluctuations caused by the random start-up and shutdown of high loads in low-voltage distribution areas, The 10 consecutive power frequency cycles prior to the fault occurred. The moving average; Within this historical cycle Standard deviation; This is the dynamic sensitivity adjustment coefficient (in this embodiment, the value ranges from 3.0 to 5.0, set based on the statistical 3σ criterion). When At that time, the waveform change flag is triggered.

[0069] S2.2 High-frequency transient signal detection: Extract high-frequency components (e.g., 10kHz~100kHz) from the original sampled sequence using a digital bandpass filter. Calculate short-time high-frequency transient energy :

[0070] ;

[0071] in, Calculate the current discrete-time index for the high-frequency components; To calculate the length of the sliding data window for short-time energy (in this embodiment, 20 to 50 sampling points are used); This is for absolute value operations.

[0072] Set a high-frequency transient energy adaptive threshold Among them, considering that the high-frequency band is mainly stable background white noise under normal operating conditions, The average energy estimate of background high-frequency noise during normal system operation is set. A fixed tolerance margin constant to mask hardware measurement errors. When > When this occurs, it means that a high-frequency traveling wave signal far exceeding the normal noise floor has been detected, triggering the high-frequency transient flag.

[0073] S2.3 Comprehensive Judgment Logic:

[0074] Phase-to-phase short circuit / open ground fault: If the following conditions are met (current surge > And (high-frequency transient energy > If the signal is not cleared, it is considered a fault, and high-frequency recording (sampling rate not less than 1MHz) is initiated.

[0075] High-resistance grounding fault: If it meets the following conditions (zero-sequence voltage change) If either (a sudden change in waveform differential energy occurs and the duration of high-frequency energy exceeds a preset duration) occurs, high-frequency recording is initiated; where, This is the zero-sequence voltage differential amplitude calculated from the current cycle and historical cycles; The preset zero-sequence voltage mutation threshold is dynamically updated based on the zero-sequence voltage fluctuation level during the historical normal operation of the transformer area (for example, it is set to 5%-10% of the rated voltage of the bus).

[0076] S3, Fault Feature Extraction and Local Timescale Generation.

[0077] To address the issue of high overlap and temporal mixing between PLC communication signals and fault traveling waves in the frequency domain, traditional filtering would severely attenuate the traveling wave signal. This embodiment employs an "active cancellation" strategy based on power line carrier preamble sequence channel estimation to purify the signal.

[0078] S3.1 Channel Impulse Response Estimation: FMT utilizes the known synchronization preamble sequence in the PLC data frame header. Actual received mixed signal acquired synchronously with high sampling rate ADC The channel impulse response estimate is obtained by solving the least squares problem. :

[0079] ;

[0080] Furthermore, under OFDM modulation, this estimation can also be performed in the frequency domain by extracting pilot subcarriers: ;in, The theoretical channel impulse response sequence to be optimized; Represents the convolution operator; To optimize variables when the objective function is minimized; To find the L2 norm square of the orientation quantity (i.e., the sum of squared residuals); This is an estimate of the frequency domain channel impulse response; For subcarrier indexing; These are measured frequency domain values; The value is the ideal pilot frequency domain value.

[0081] S3.2 Interference Cancellation and Signal Cleansing: Utilizing the reconstructed current interference signal Actively subtracting from the original received signal yields a purified signal that retains the traveling wave characteristics to the greatest extent possible. .

[0082] S3.3 Wavehead Extraction and Confidence Quantification: For The wave was filtered using an elliptic filter with a passband of 100kHz-2MHz, and then subjected to a sym8 wavelet transform (scale 3-5) to extract the wavelet transform modulus maxima. The absolute arrival time scale of the first wavefront of the traveling wave was then extracted. The formula is:

[0083] ;

[0084] in, For sliding time variables; The scaling parameter of the wavelet transform; In order to scale Wavelet transform coefficients; The absolute time of fault initiation (i.e., the triggering time when the fault criterion is met in step S2.3); The preset observation window duration; The dynamic detection threshold for wavelet transform coefficients is calculated using the following formula: ,in For noise standard deviation estimation, This is a configurable gain factor (typically between 4.0 and 6.0).

[0085] After extraction, the traveling wave confidence level is calculated for each terminal. :

[0086] ;

[0087] in, The peak value is the modulus maxima of the wavelet transform. The standard deviation of the noise before the wavefront arrives; The standard deviation of wavefront arrival time across multiple scales; This represents the average wavefront arrival time across multiple scales.

[0088] S4. Lightweight upload of fault characteristic data.

[0089] To accommodate the narrow bandwidth and contention issues inherent in low-voltage PLC network communication, the FMT does not upload full waveform data. Instead, it encapsulates a lightweight feature data packet with a length not exceeding 100 bytes. This data packet structure includes: a frame header (2 bytes), a terminal ID (4 bytes), an absolute fault start time (6 bytes, simplified transmission), and an absolute time stamp of the traveling wave header. (6 bytes), initial electrical characteristics of the fault (12 bytes, including the fault power frequency voltage phasor used for subsequent impedance method calculation) Fault frequency current phasor The data packet includes zero-sequence characteristics, a fault type flag (1 byte, with the result combined using a Boolean type), a terminal location topology identifier (4 bytes), a CRC checksum (2 bytes), and a frame tail (2 bytes). This data packet is concurrently uploaded to the intelligent fusion terminal (IFU) on the transformer side.

[0090] S5, intelligent fusion terminal multi-source data fusion and fault location analysis.

[0091] After collecting the characteristic data packets from each terminal, IFU performs the following collaborative analysis:

[0092] S5.1 Fault Association: IFU unifies all data to the same time coordinate system. If the difference in absolute time of fault initiation reported by multiple terminals is less than the set time window (such as the maximum traveling wave propagation delay of the transformer area, which is usually on the order of tens of microseconds), and the fault type flag is consistent, then they are associated as the same fault event.

[0093] S5.2 Weighted Multi-Terminal Traveling Wave Positioning: Utilizing the confidence levels reported by each terminal Assign weights to the observation equation Construct a weighted nonlinear least squares objective function to form an overdetermined system of equations:

[0094] ;

[0095] in, Let be the objective function to be minimized: the sum of squared errors. Number index of the terminals participating in the computation ; The total number of terminals participating in the calculation; The location of the fault point to be solved; The absolute time of the fault to be solved; The equivalent propagation wave velocity to be solved jointly; For the first Each terminal travels along the actual physical topology of the distribution network to the hypothetical fault point. The path length function.

[0096] The fault distance located solely by the traveling wave method was obtained by iteratively solving the Levenberg-Marquardt optimization algorithm. and the overall reliability of the traveling wave method ( Take the weighted average of the confidence levels of each terminal involved in the calculation.

[0097] S5.3 Multi-source fusion decision: As an effective supplement when traveling wave failure or weakness occurs, the system simultaneously uses the impedance method loop equation to calculate the fault distance. The impedance method equation is: ,in , The fault power frequency voltage and current phasors uploaded in step S4; The complex impedance per unit length of the line; To determine the distance using the impedance method; This is the current of the transition resistance; The required transition resistance is denoted as .

[0098] Quantitative calculation of the reliability of impedance method :

[0099] ;

[0100] in, This represents the impedance change calculated before and after the fault. The preset nominal impedance value; This is the normalized value of the voltage signal-to-noise ratio; To limit the calculation results to a maximum value of 1.

[0101] Calculate the distance difference between the two ,like If the distance is 50m (preset consistency tolerance), the final positioning result will be output using a weighted fusion method.

[0102] ;

[0103] If the difference is too large ( 50m), then according to and The reliability of the results is arbitrated based on the level of confidence, and the result of the one with higher confidence is given priority and a reference prompt is marked. This fusion model based on dynamic confidence effectively solves the problem that a single method is prone to failure under complex interference in the transformer area.

[0104] Please refer to Figure 2 , Figure 2 A framework diagram of a transformer area fault location system based on power line carrier time synchronization according to an embodiment of this disclosure is shown; including: a carrier master clock module, a distributed fault monitoring terminal (FMT), and an intelligent fusion terminal (IFU).

[0105] Carrier master clock module: Deployed on the transformer side of the distribution area, it is used to maintain microsecond-level time synchronization across the entire PLC network through physical layer hardware stamping and RTT measurement.

[0106] Distributed Fault Monitoring Terminal (FMT): Widely deployed in various branch boxes and key nodes of the distribution area, it integrates a high-speed waveform recording unit, a channel impulse response estimation module, and a feature extraction module. It is used to perform PLC interference active cancellation, traveling wave front extraction, and confidence calculation locally, and encapsulates lightweight feature data packets.

[0107] Intelligent Fusion Terminal (IFU): It communicates with the distributed fault monitoring terminal and integrates a positioning calculation engine to perform multi-source feature data association, weighted multi-terminal traveling wave positioning calculation based on wavefront extraction confidence weight, and fusion analysis with the power frequency impedance method to output the precise location of the fault.

[0108] This disclosure also provides a computer-readable storage medium, such as a memory including program code that can be executed by a processor to perform the transformer fault location method based on power line carrier time synchronization in the above embodiments. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0109] This disclosure also provides a computer program product comprising one or more lines of program code stored in a computer-readable storage medium. A processor of an electronic device reads the program code from the computer-readable storage medium and executes the program code to complete the implementation steps of the transformer substation fault location method based on power line carrier time synchronization provided in the above embodiments.

[0110] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A power line carrier time synchronization based substation fault location method, characterized in that, The method includes the following steps: S1. Constructing a carrier synchronization network: The carrier master clock module on the transformer side sends synchronization messages to the distributed fault monitoring terminal through the power line carrier network, and uses physical layer hardware timestamp marking and round-trip time measurement and dynamic compensation mechanism to achieve microsecond-level synchronization between the slave clock and the carrier master clock module in the fault monitoring terminal, with a synchronization accuracy of no more than 5μs. S2, Local fault trigger detection: The fault monitoring terminal monitors the line three-phase voltage and current electrical quantities in real time to obtain the original discrete sampling sequence and extracts the zero sequence voltage feature component, adopts a multi-criterion adaptive threshold model combining power frequency differential energy jump and high frequency transient energy to determine the fault type and start high frequency recording with a sampling rate not less than 1 MHz. S3. Feature Extraction and Signal Cleaning: The fault monitoring terminal reconstructs the carrier interference signal using an active cancellation strategy based on the power line carrier preamble sequence channel estimation, and removes the carrier interference signal from the actual received mixed signal to obtain the cleaned signal. Then, the absolute time scale of the traveling wavefront and the confidence level of the wavefront extraction are extracted from the cleaned signal. S4, feature data upload: the fault monitoring terminal uploads the extracted wave head absolute time tag, wave head extraction confidence and electrical characteristics including fault power frequency voltage phase and fault power frequency current phase to the intelligent fusion terminal in the form of lightweight feature data packets with a data length of not more than 100 bytes. S5, fusion positioning analysis: the intelligent fusion terminal assigns weights based on the wave head extraction confidence reported by each fault monitoring terminal, performs weighted multi-terminal traveling wave positioning calculation to obtain the traveling wave method positioning result, and combines the obtained power frequency impedance method positioning result to perform dynamic confidence weighted fusion decision, and outputs the accurate positioning position of the fault point. With the calculated power frequency impedance method positioning result to perform dynamic confidence weighted fusion decision, and outputs the accurate positioning position of the fault point.

2. The power line carrier time synchronization based substation fault location method according to claim 1, characterized in that, In step S1, the round-trip time measurement and dynamic compensation mechanism specifically includes: measuring the round-trip time of synchronization messages through multiple interactions between the carrier master clock module and the fault monitoring terminal, and dynamically compensating for asymmetric delays on the power line path.

3. The power line carrier time synchronization based substation fault location method according to claim 1, characterized in that, In step S2, the multi-criteria adaptive threshold model includes a power frequency differential energy detection component: Based on the original discrete sampling sequence Calculate the differential energy of adjacent power frequency cycles. : ； in, This is the discrete-time index for the current calculation; For the index of the variable in the summation operation; This represents the total number of sampling points in the system within one power frequency cycle. Original discrete sampling sequence In the index The sampling point value at that location; For sequence Middle and sampling points The difference is between the historical sampling point values ​​of a complete power frequency cycle; Set the power frequency differential energy adaptive threshold ; in, For multiple consecutive power frequency cycles before the fault occurred The moving average; Within this historical cycle Standard deviation; This is the dynamic sensitivity adjustment coefficient; when At that time, the waveform change flag is triggered.

4. The method for fault location in transformer substations based on power line carrier time synchronization according to claim 3, characterized in that, In step S2, the multi-criteria adaptive threshold model also includes a high-frequency transient energy detection part: From the original discrete sampling sequence Extracting high-frequency components And calculate the short-time high-frequency transient energy. : ； in, Calculate the current discrete-time index for the high-frequency components; To calculate the sliding data window length for short-time energy, For absolute value operations; Set a high-frequency transient energy adaptive threshold ,in, Estimation of the mean of high-frequency background noise; For fault tolerance margin; If the waveform change flag is triggered simultaneously and the short-time high-frequency transient energy exceeds [the threshold], then [the following conditions are met]. If the zero-sequence voltage change is satisfied, it is determined to be a phase-to-phase short circuit or a direct ground fault; If the preset zero-sequence threshold is exceeded, it is determined to be a high-resistance grounding fault. This is the differential amplitude calculated based on the zero-sequence voltage characteristic component.

5. The method for fault location in transformer substations based on power line carrier time synchronization according to claim 1, characterized in that, In step S3, the active cancellation strategy based on the power line carrier preamble sequence channel estimation specifically includes: obtaining the channel impulse response estimate using the least squares method. : ； in, The theoretical channel impulse response sequence to be optimized; To actually receive mixed signals; Given a known power line carrier synchronization preamble sequence; This is the convolution operator; To optimize variables when the objective function is minimized; This indicates the calculation of the sum of squares of the residuals.

6. The method for fault location in transformer substations based on power line carrier time synchronization according to claim 1, characterized in that, In step S3, the confidence level of the wave head is extracted. The calculation formula is: ； in, The peak value is the modulus maxima of the wavelet transform. The standard deviation of the noise before the wavefront arrives; The standard deviation of wavefront arrival time across multiple scales; This represents the average wavefront arrival time across multiple scales.

7. The method for fault location in transformer substations based on power line carrier time synchronization according to claim 1, characterized in that, In step S4, the data structure of the lightweight feature data packet includes the terminal ID, the absolute time of fault initiation, the absolute time stamp of the traveling wave front, and the fault power frequency voltage phasor. Fault frequency current phasor Fault type flag, terminal location topology identifier, and CRC checksum.

8. The method for transformer area fault location based on power line carrier time synchronization according to claim 1, characterized in that, In step S5, the weighted multi-terminal traveling wave localization calculation specifically includes: constructing a weighted nonlinear least squares objective function. : ； in, Let be the objective function to be minimized: the sum of squared errors. The location of the fault point to be solved; The absolute time of the fault occurrence; The equivalent propagation wave velocity to be solved jointly; Number the fault monitoring terminals participating in the calculation with an index; The total number of fault monitoring terminals participating in the calculation; The weighting coefficients for confidence assignment based on wavehead extraction; The corresponding absolute timescale of the traveling wavefront; For the first Each fault monitoring terminal reaches the hypothetical fault point along the actual physical topology of the distribution network. The path length function; The objective function is solved iteratively to obtain the traveling wave method positioning distance. and the overall reliability of the traveling wave method .

9. A method for fault location in transformer substations based on power line carrier time synchronization according to claim 8, characterized in that, In step S5, the dynamic confidence-weighted fusion decision specifically includes: Using fault power frequency voltage phasors and fault power frequency current phasor Based on the impedance method loop equation Calculating the distance using impedance method And quantify the reliability of the impedance method. : ； in, The complex impedance per unit length; This is the current of the transition resistance; For transition resistance; This is the change in impedance; The nominal impedance setting; This is the normalized voltage signal-to-noise ratio; For amplitude limiting calculation; Calculate the difference between the positioning distance using the traveling wave method and the positioning distance using the impedance method. ,like 50m, then the precise location of the fault. Obtained by weighted fusion: 。 10. A transformer area fault location system based on power line carrier time synchronization, characterized in that, The system is used to implement the transformer area fault location method based on power line carrier time synchronization as described in any one of claims 1 to 9. The system includes: a carrier master clock module, a distributed fault monitoring terminal, and an intelligent fusion terminal. The carrier master clock module is deployed on the transformer side of the distribution area and is used to maintain a time synchronization reference in the power line carrier network through physical layer hardware stamping and round-trip time measurement. The distributed fault monitoring terminal is deployed at each transformer substation node to perform local fault trigger detection, active cancellation of carrier interference signals, traveling wave feature extraction and wavefront extraction confidence calculation, and encapsulate and upload lightweight feature data packets. The intelligent fusion terminal communicates with the distributed fault monitoring terminal and integrates a positioning calculation engine to perform multi-source feature data association, weighted multi-terminal traveling wave positioning calculation based on wavefront extraction confidence weights, and fusion analysis with the power frequency impedance method to output the precise location of the fault.

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