A photovoltaic power station outgoing line voltage diagnosis method and system

Abstract

The application relates to a photovoltaic power station outgoing line voltage diagnosis method and system, and belongs to the technical field of supervisory control and data acquisition systems. The system realizes accurate distinction of transient disturbance and permanent fault and high-precision fault positioning by real-time acquisition of electrical quantity data through a synchronous phasor measurement unit, data preprocessing, multi-time scale feature extraction, transient and steady state identification and fault determination module analysis, and outputs diagnosis instructions based on a dynamic self-adaptive threshold and a closed-loop decision mechanism, thereby improving the accuracy, response speed and operation and maintenance efficiency of photovoltaic outgoing line voltage state diagnosis.

Description

Technical Field

[0001] This invention belongs to the technical field of monitoring and control and data acquisition systems, specifically relating to a method and system for diagnosing the voltage of photovoltaic power plant transmission lines. Background Technology

[0002] As a crucial component of the renewable energy sector, photovoltaic (PV) power generation, with its large-scale grid connection, places higher demands on the stable operation of the power system. PV power plant transmission lines, serving as critical channels for power transmission, require real-time monitoring and rapid diagnosis of their voltage status as a core element in ensuring grid security and power quality. Voltage diagnostic technology for PV power plant transmission lines aims to improve system reliability and operational efficiency by collecting line operating parameters, identifying abnormal states, and locating fault points. Existing technologies primarily rely on fixed-threshold voltage exceedance judgments or fault detection methods based on single electrical quantities. These methods are poorly adaptable to complex operating conditions such as fluctuations in sunlight and changes in load, struggle to accurately distinguish between transient disturbances and permanent faults, and rely on manual experience analysis, resulting in long response delays. These methods fail to meet the urgent need for rapid fault isolation and power restoration in the context of high PV power plant penetration. Summary of the Invention

[0003] The technical problem this invention aims to solve is to overcome the shortcomings of existing photovoltaic power plant transmission line voltage diagnosis technologies, which rely on fixed thresholds or single electrical quantity analysis, resulting in poor adaptability, difficulty in distinguishing between transient disturbances and permanent faults, and long response delays. This invention provides a method and system for diagnosing photovoltaic power plant transmission line voltage. This method and system aim to achieve accurate, rapid, and adaptive diagnosis of the voltage status of transmission lines, improving the safety, stability, and operation and maintenance efficiency of the power grid when dealing with complex operating conditions involving high proportions of photovoltaic power.

[0004] This invention provides a voltage diagnostic system for photovoltaic power plant transmission lines. The system includes synchronous phasor measurement units deployed at the photovoltaic power plant's step-up substation and key nodes of the transmission line, a system master station, and a high-speed communication network connecting the two. The synchronous phasor measurement units acquire instantaneous values ​​of the three-phase voltage and current of the transmission line in real time at a sampling frequency greater than 4800Hz, and calculate the fundamental positive-sequence components, amplitudes, phase angles, and frequencies of the voltage and current using a built-in Fourier transform algorithm. The system master station integrates a data preprocessing module, a multi-timescale feature extraction module, a metastable state identification and fault determination module, and a diagnostic decision output module.

[0005] The data preprocessing module receives raw electrical quantity data streams from each synchronous phasor measurement unit. This module first performs outlier detection and removal on the input data based on a sliding time window. Outlier criteria are defined as the current sampling point value deviating from the mean of the data within the window by more than three standard deviations. Subsequently, the module uses a zero-phase digital filter to smooth the data to suppress high-frequency noise and performs data alignment to ensure that data from different measurement nodes have a unified time label.

[0006] The multi-timescale feature extraction module receives preprocessed, regularized data. This module runs three feature extraction threads in parallel, corresponding to millisecond, second, and minute timescales, respectively. The millisecond-level thread focuses on transient processes, calculating voltage sag depth, voltage surge amplitude, voltage mutation rate, and negative-sequence voltage imbalance. The second-level thread analyzes short-term steady-state characteristics, including voltage fluctuation rate, short-term voltage flicker value, and total harmonic voltage distortion rate. The minute-level thread evaluates the medium- and long-term operating status, extracting the number of times the upper and lower voltage limits are exceeded, the average voltage deviation, and voltage compliance statistics. All extracted feature vectors are assigned precise timestamps and stored in the feature database.

[0007] The transient steady-state identification and fault determination module is the core analysis unit of the system. This module first classifies the operating state based on multi-timescale features. The classification logic is as follows: if the voltage fluctuation rate in the millisecond-level feature vector exceeds 5% per second and the duration is less than 2 cycles, it is initially determined to be a transient disturbance event; if the voltage fluctuation feature persists and the second-level feature shows that the voltage value deviates from the rated value by more than 10% for more than 5 seconds, it is determined to be a suspected fault state; if the minute-level feature further indicates that voltage exceeding the limit has become a stable trend, the confidence level of the fault determination is strengthened. For events entering a suspected fault state, the module initiates fault location analysis. The location algorithm is based on the double-ended traveling wave principle, utilizing the arrival time difference of the voltage traveling wave front recorded by the synchronous phasor measurement units at both ends of the line, combined with the known line wave impedance and length parameters, to accurately calculate the distance from the fault point to the beginning of the line, with a calculation accuracy better than 50m.

[0008] The diagnostic decision output module receives state classification results and fault location information from the transient stability identification and fault determination module. This module has a pre-built knowledge base containing various typical fault and anomaly modes and corresponding handling strategies. For identified transient disturbances, the module generates an early warning report including event type, occurrence time, duration, and an assessment of its impact on the system. For confirmed permanent faults, the module immediately generates fault diagnosis instructions including fault type, precise location coordinates, suggested isolation sections, and protection action sequences. All diagnostic results and instructions are displayed in real-time through a human-machine interface and can be sent to the power plant monitoring system or wide-area protection and control system via standard communication protocol interfaces.

[0009] As a preferred embodiment of the present invention, the millisecond-level feature calculation in the multi-timescale feature extraction module adopts an improved recursive discrete Fourier transform algorithm. This algorithm introduces the calculation result of the previous cycle as the initial value and adopts an iterative update mechanism with a variable step size, which can update the feature quantity immediately when each new sampling point arrives, thereby controlling the feature extraction delay to within 1ms and greatly improving the system's response speed to fast transient events.

[0010] Furthermore, the fault location algorithm employed in the metastable state identification and fault determination module incorporates a precise wavefront detection method based on wavelet transform modulus maxima theory when calculating the arrival time of the traveling wavefront. This method first performs multi-resolution wavelet decomposition on the acquired voltage traveling wave signal, selecting the fourth-level detail coefficients as the analysis signal. Then, it detects the position of the modulus maxima on these detail coefficients; this position corresponds to the singular point of the traveling wave signal, i.e., the wavefront arrival time. By comparing the wavefront arrival times detected by the measurement units at both ends of the line, and considering compensation for communication channel delay, sub-microsecond time difference measurement is ultimately achieved, thus ensuring high accuracy in fault location.

[0011] Furthermore, the system's main station also integrates an adaptive threshold adjustment module. This module continuously monitors historical operating data and real-time characteristic values ​​of the transmission lines. Based on the distribution of characteristic data under similar weather and load conditions during the same period over the past 30 days, it dynamically calculates and updates various thresholds used for status classification and fault determination. Specifically, the module uses a sliding window statistical method to calculate the moving average and moving standard deviation of characteristic quantities, and sets the judgment threshold to the moving average plus or minus twice the moving standard deviation. This design enables the system to adapt to fluctuations in photovoltaic output and changes in load, avoiding misjudgments or missed judgments caused by fixed thresholds that are not adapted to actual operating conditions.

[0012] Furthermore, after generating a fault diagnosis command, the diagnostic decision output module will initiate a command execution confirmation and feedback mechanism. Simultaneously with the command being sent to the power plant monitoring system, the system master station will continuously monitor the status signals of the relevant circuit breakers and the voltage and current changes of the faulty line segment. If no circuit breaker position change signal is received within 1ms after the command is issued, or the faulty electrical quantity has not disappeared, the diagnostic decision output module determines that the command execution has failed and immediately activates a backup control strategy, such as attempting to remotely control adjacent backup circuit breakers or, provided personnel safety is confirmed, upgrading the fault isolation level.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0014] 1. This invention achieves deep perception and intelligent diagnosis of the voltage status of photovoltaic power plant transmission lines by constructing a system architecture that integrates multi-timescale feature extraction, accurate identification of transient steady state and high-precision fault location.

[0015] 2. The system adopts a dynamic adaptive threshold instead of a traditional fixed threshold, which improves the accuracy and robustness of diagnosis under different light intensities and load conditions.

[0016] 3. Based on the double-ended traveling wave principle and high-precision time synchronization, the fault location technology can quickly and accurately pinpoint the fault point, greatly shortening the fault diagnosis and repair time.

[0017] 4. The collaborative operation of multiple modules and the closed-loop decision-making mechanism enable the system to distinguish between transient disturbances and permanent faults, and automatically generate executable control commands, thereby reducing the reliance on external manual intervention and comprehensively improving the operational reliability, fault handling efficiency, and overall safety and stability of the power grid of photovoltaic power plant transmission lines. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the overall technical architecture of the photovoltaic power plant transmission line voltage diagnosis system proposed in this invention;

[0019] Figure 2 This is a schematic diagram of the core principle framework for multi-timescale feature extraction and transient steady-state identification in this invention;

[0020] Figure 3 This is a flowchart illustrating the logical process of data preprocessing and multi-timescale feature extraction in this invention.

[0021] Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow of the transient steady state identification and fault determination module in this invention;

[0022] Figure 5 This is a schematic diagram of the closed-loop control framework for diagnostic decision output and instruction execution confirmation in this invention; Detailed Implementation

[0023] Please refer to Figures 1 to 5 This embodiment details the specific implementation structure and operating mechanism of a photovoltaic power plant transmission line voltage diagnostic system. The system consists of synchronous phasor measurement units deployed at the photovoltaic power plant's step-up substation and key nodes of the transmission line, a system master station, and a high-speed communication network connecting the two. The system master station integrates a data preprocessing module, a multi-timescale feature extraction module, a metastable state identification and fault determination module, a diagnostic decision output module, and an adaptive threshold adjustment module. All modules seamlessly interact via a high-speed data bus, forming a closed-loop control system from data acquisition to decision execution.

[0024] Synchronous phasor measurement units (TPMUs) serve as the system's data source, installed at key electrical nodes at the busbars of photovoltaic power plant substations and at the beginning and end of transmission lines. Each unit is equipped with high-precision voltage and current transformers, with a fixed sampling frequency of 4800Hz to ensure complete capture of harmonic components and high-frequency oscillations in the power system's transient processes. Embedded within the unit is a parallel computing core based on a field-programmable gate array (FPGA), which executes a full-cycle Fourier transform algorithm in real time to calculate the fundamental positive-sequence components, amplitude, phase angle, and frequency of voltage and current.

[0025] After the calculation is completed, the unit transmits electrical quantity data packets with time stamp information to the system master station at a rate of 100fps through a high-speed communication network. The time stamp accuracy reaches the microsecond level to meet the stringent time synchronization requirements of dual-end traveling wave positioning.

[0026] The data preprocessing module receives raw data streams from each synchronized phasor measurement unit. The module first establishes a circular buffer with a capacity of 1000 sampling points and performs outlier detection on the input data based on a sliding time window. The detection algorithm uses the arithmetic mean and standard deviation of the data within the current window as a benchmark. When the value of a sampling point deviates from the mean by more than three times the standard deviation, it is identified as an outlier and removed. The missing positions after removal are filled using linear interpolation of adjacent sampling points.

[0027] Subsequently, the module invokes a zero-phase digital filter to smooth the data. This filter consists of two identical Butterworth low-pass filters connected in reverse series, with a passband cutoff frequency set to 200Hz and a stopband attenuation greater than 40dB to suppress measurement noise and high-frequency interference. Finally, the module performs time alignment on the data from different measurement nodes based on the GPS timestamp in the data packet, ensuring that all data enters the subsequent processing flow under a unified time reference.

[0028] The multi-timescale feature extraction module receives preprocessed, regularized data. This module runs three independent feature extraction threads in parallel in memory, corresponding to millisecond, second, and minute timescales, respectively. The millisecond-scale thread updates the feature values ​​for each sampling period, calculating the voltage sag depth as the percentage difference between the voltage amplitude and the rated value, the voltage surge amplitude as the percentage of the voltage amplitude exceeding the rated value, the voltage jump rate as the absolute value of the rate of change of the effective voltage value between adjacent cycles, and the negative-sequence voltage imbalance as the ratio of the negative-sequence voltage amplitude to the positive-sequence voltage amplitude.

[0029] The second-level thread aggregates data with a 1-second cycle, calculates the voltage fluctuation rate as the ratio of the difference between the maximum and minimum voltage values ​​per second to the rated value, the short-term voltage flicker value as output by a weighted filter simulating the visual response of the human eye, and the total harmonic voltage distortion rate as the ratio of the sum of the squares of the effective values ​​of each harmonic voltage to the effective value of the fundamental voltage.

[0030] The minute-level thread uses a 1-minute statistical window to record the number of times the upper and lower voltage limits are exceeded, the average voltage deviation (the arithmetic mean of the absolute values ​​of voltage deviations within each minute), and the voltage pass rate (the percentage of time the voltage is within ±5% of the rated value). All feature vectors are accompanied by timestamps accurate to milliseconds and written to a time-series database for subsequent module use.

[0031] The transient stability identification and fault determination module is the core analysis unit of the system. This module first constructs a state classification logic based on multi-timescale features. When the voltage mutation rate in the millisecond-level feature vector exceeds 5% per second and the duration is less than 0.04s, the module marks it as a transient disturbance event; if the voltage mutation feature persists and the second-level feature shows that the voltage value deviates from the rated value by more than 10% for more than 5s, it is upgraded to a suspected fault state; when the minute-level feature further indicates that the number of voltage over-limit occurrences exceeds 3 times within 10 minutes, the fault determination is confirmed.

[0032] For events entering a suspected fault state, the module initiates a dual-end traveling wave fault location process. The location process first utilizes wavelet transform modulus maxima theory to detect the traveling wave front: the voltage traveling wave signal is decomposed into four levels of discrete wavelet decomposition, the fourth level detail coefficient sequence is extracted, and the time position corresponding to the modulus maxima point in this sequence is identified as the wave front arrival time. Let the wave front arrival time at the beginning of the line be... The arrival time of the terminal wavefront is The total length of the line is kilometers, wave propagation speed is In kilometers per second, the distance from the fault point to the head end is... Calculated by the following formula:

[0033] ;

[0034] This formula takes into account the reflection and transmission characteristics of traveling waves at the fault point, and uses communication delay compensation technology to control the time difference measurement error within 0.1μs, ultimately achieving a fault location accuracy better than 50m.

[0035] After receiving the status classification and fault location results, the diagnostic decision output module calls a pre-built knowledge base to generate response strategies. The knowledge base contains 16 typical fault modes and 28 transient disturbance templates, with each type of event associated with a specific handling procedure.

[0036] For transient disturbances, the module generates an early warning report that includes the event type, occurrence time, duration, and impact level on the system. The impact level is divided into three levels based on the voltage sag depth and duration. For permanent faults, the module immediately generates structured diagnostic instructions, including the fault type, latitude and longitude coordinates, suggested isolation section number, and protection action sequence.

[0037] All outputs are displayed in the form of a dynamic topology diagram through a human-machine interface, and simultaneously sent to the power plant monitoring system via the IEC61850 protocol interface. After the command is issued, the module initiates an execution confirmation mechanism: continuously monitoring the opening and closing status signals of relevant circuit breakers and the voltage and current changes of the faulty line section. If no circuit breaker change signal is detected within 1ms or the faulty electrical quantity has not disappeared, the command execution is deemed to have failed, and the module automatically switches to a backup control strategy, including attempting to remotely control adjacent backup circuit breakers or expanding the isolation range to the next higher power supply section.

[0038] The adaptive threshold adjustment module, acting as the system's intelligent evolution unit, continuously optimizes the judgment threshold. This module establishes a historical database with a 30-day cycle, storing characteristic data under different meteorological conditions and load levels in hourly segments. When performing a threshold update, the module extracts characteristic sequences from the same time period and similar meteorological and load conditions over the past 30 days, calculating the moving average and moving standard deviation using a sliding window of 1440 samples. The new judgment threshold is set as the moving average plus or minus twice the moving standard deviation, and is automatically updated every 6 hours. This mechanism ensures the system can adapt to diurnal fluctuations in photovoltaic output and seasonal load changes, avoiding misjudgments caused by fixed thresholds.

[0039] The system's main station hardware employs a redundant dual-server architecture, with each server equipped with a 16-core processor, 128GB of memory, and a 10TB solid-state drive array to ensure data processing and storage reliability. The software is developed based on a microservices architecture, with each module encapsulated as an independent container and asynchronous communication achieved through a message middleware. The overall system response time, from data acquisition to command generation, is less than 80ms, meeting the real-time control requirements of power systems.

[0040] This embodiment also provides an alternative implementation scheme for a photovoltaic power plant transmission line voltage diagnosis system, focusing on algorithm optimization and hardware acceleration design of the multi-timescale feature extraction module. The feature extraction module in this embodiment uses a field-programmable gate array (FPGA) to implement a parallel pipelined computing architecture, compressing the feature extraction latency to within 0.5ms.

[0041] In the multi-timescale feature extraction module, the millisecond-level thread employs an improved recursive discrete Fourier transform algorithm. This algorithm decomposes the Fourier calculation into two recursive channels, real and imaginary, updating the Fourier coefficients upon receiving a new sampling point. Let the... The voltage signal value at the next sampling time is The real part of the Fourier coefficients in the previous period is The imaginary part is The current cycle coefficient update formula is:

[0042] ;

[0043] ;

[0044] in The number of sampling points per cycle. The adaptive step size factor is dynamically adjusted based on the signal-to-noise ratio. This design avoids the waiting time for the complete cycle of data in traditional discrete Fourier transforms, enabling real-time updates of feature quantities at the level of each sampling point.

[0045] At the hardware level, the feature extraction module is equipped with a Xilinx UltraScale series field-programmable gate array (FPGA) chip, internally featuring three parallel computing engines. Each engine includes four digital signal processor slices, two block random access memory (BRAM) units, and one direct memory access controller (DRAM). Millisecond-level threads occupy two engines, processing voltage and current features respectively; second-level and minute-level threads share one engine, executing alternately via time-division multiplexing. The FPGA connects to the main server via a PCIe 4.0 interface, achieving a data throughput of 40 Gbit / s.

[0046] In this embodiment, the metastable state identification and fault determination module enhances its ability to identify complex fault modes. In addition to the dual-end traveling wave localization described in Embodiment 1, the module adds an impedance-assisted localization function. When the traveling wave signal is severely attenuated due to line branches or transition resistance, the module switches to impedance calculation mode: using the voltage and current phasors measured synchronously at both ends of the line, the loop impedance from the measurement point to the fault point is calculated, and the fault distance is deduced by combining this with the line unit length parameter. The results of the two localization methods are combined using a weighted fusion algorithm, with weights dynamically allocated according to signal quality indicators, further improving localization reliability.

[0047] The diagnostic decision output module integrates augmented reality technology, deploying holographic projection equipment in the power plant monitoring center. After a fault diagnosis command is generated, the system automatically projects the three-dimensional coordinates of the fault point, a heat map of the affected area, and suggested operation paths onto the physical power grid model. Maintenance personnel can adjust the viewing angle and view historical data of fault details through gesture interaction. The module also supports voice command input; upon receiving a confirmation command, the system automatically records the operator's identity and timestamp, creating a traceable electronic operation ticket.

[0048] The system communication network adopts a hierarchical architecture. Multimode fiber optic communication is used between the synchronization phasor measurement unit and the local aggregation node of the booster station, with the transmission protocol being IEEE C37.118.2. The aggregation node is connected to the system master station via a dedicated 4G wireless network for the power industry, with each link configured with a 256-bit encryption algorithm. The network management layer monitors the transmission delay and packet loss rate of each link in real time. When the delay of a link exceeds 100ms or the packet loss rate is greater than 0.1%, it automatically switches to a backup route to ensure the real-time performance and integrity of data transmission.

[0049] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0050] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A voltage diagnostic system for photovoltaic power plant transmission lines, characterized in that, include: Synchronous phasor measurement units, system master stations, and high-speed communication networks connecting the two are deployed at key nodes of photovoltaic power plant booster stations and transmission lines. The system's main station integrates a data preprocessing module, a multi-timescale feature extraction module, a transient steady-state identification and fault determination module, and a diagnostic decision output module. The data preprocessing module receives raw electrical quantity data streams from each synchronous phasor measurement unit, performs outlier detection and removal on the input data based on a sliding time window, then suppresses high-frequency noise and performs data alignment; the multi-timescale feature extraction module receives the preprocessed regularized data; the metastable state identification and fault determination module performs operating state analysis based on multi-timescale features. The diagnostic decision output module receives the state classification results and fault location information, generates response strategies based on a pre-set knowledge base, generates early warning reports for transient disturbances including event type, occurrence time, duration and system impact assessment, and generates fault diagnosis instructions for permanent faults including fault type, precise location coordinates, suggested isolation section and protection action sequence.

2. The photovoltaic power plant transmission line voltage diagnostic system according to claim 1, characterized in that, The millisecond-level feature calculation in the multi-timescale feature extraction module adopts an improved recursive discrete Fourier transform algorithm. This algorithm introduces the calculation result of the previous cycle as the initial value and adopts an iterative update mechanism with a variable step size. The feature quantity is updated immediately when each new sampling point arrives, thus controlling the feature extraction delay within a fixed range.

3. The photovoltaic power plant transmission line voltage diagnostic system according to claim 1, characterized in that, The fault location algorithm used in the transient steady-state identification and fault determination module introduces a wavefront accurate detection method based on wavelet transform modulus maxima theory when calculating the arrival time of the traveling wavefront. This method performs multi-resolution wavelet decomposition on the acquired voltage traveling wave signal, selects the fourth level detail coefficients as the analysis signal, detects the position of the modulus maxima point on the detail coefficients of this level, and detects the singular point of the traveling wave signal corresponding to the wavefront arrival time. By comparing the wavefront arrival times detected by the measurement units at both ends of the line and considering the communication channel delay for compensation, sub-microsecond time difference measurement is achieved.

4. The photovoltaic power plant transmission line voltage diagnostic system according to claim 1, characterized in that, The system's main station also integrates an adaptive threshold adjustment module; this module continuously monitors the historical operating data and real-time characteristic values ​​of the transmission lines, and based on the characteristic data distribution under similar weather and load conditions in the same period in the past, it uses a sliding window statistical method to calculate the moving average and moving standard deviation of the characteristic quantities, and sets judgment thresholds to dynamically update various thresholds used for status classification and fault determination.

5. The photovoltaic power plant transmission line voltage diagnostic system according to claim 4, characterized in that, The adaptive threshold adjustment module automatically updates the judgment threshold at fixed intervals. During the update, it extracts the feature sequence under similar weather and load conditions in the same period in the past, and uses a sliding window of the sample to calculate the moving average and moving standard deviation.

6. The photovoltaic power plant transmission line voltage diagnostic system according to claim 1, characterized in that, After generating a fault diagnosis instruction, the diagnostic decision output module initiates an instruction execution confirmation and feedback mechanism. While the instruction is being sent to the power plant monitoring system, the system master station continuously monitors the status signals of the relevant circuit breakers and the voltage and current changes of the faulty line section. If no circuit breaker position change signal is received after the instruction is sent or the faulty electrical quantity has not disappeared, the instruction execution is deemed to have failed and the backup control strategy is immediately activated.

7. The photovoltaic power plant transmission line voltage diagnostic system according to claim 6, characterized in that, The backup control strategy includes attempting to remotely control adjacent backup circuit breakers or, provided that personnel safety is confirmed, increasing the fault isolation level.

8. The photovoltaic power plant transmission line voltage diagnostic system according to claim 1, characterized in that, The synchronous phasor measurement unit transmits electrical quantity data packets with time stamp information to the system master station through a high-speed communication network, with time stamp accuracy reaching the microsecond level.

9. A photovoltaic power plant transmission line voltage diagnostic system according to claim 1, characterized in that, The data preprocessing module uses a zero-phase digital filter to smooth the data. This filter consists of two identical Butterworth low-pass filters connected in reverse series.

10. A method for diagnosing the voltage of a photovoltaic power plant's transmission line, characterized in that, Voltage diagnosis is achieved using the photovoltaic power plant transmission line voltage diagnosis system according to any one of claims 1-9.

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