Lightning arrester on-line monitoring method and system

Abstract

The invention discloses a lightning arrester on-line monitoring method and system, and relates to the technical field of lightning arrester monitoring, the lightning arrester on-line monitoring system comprises an energy management unit, a monitoring unit, a master control and diagnosis unit and related peripherals, and the monitoring unit comprises a fault symptom sensing module and an energy event sensing module. The monitoring system is in a non-response state in a normal state and is triggered to enter a response state by event driving, so that the system only maintains the microampere-level ultra-low power consumption operation of the monitoring unit in most of time, and even under the extreme conditions of continuous cloudy and rainy days and weak leakage current, the online monitoring device can still work normally. The weak energy collected by the energy management unit can maintain the continuous monitoring of the monitoring unit, so that the monitoring process is ensured to be uninterrupted; and when the power input is abundant, the system does not consume energy uselessly, so that the limited energy is concentrated at the most critical monitoring moment, and the monitoring device can work more stably for a long time under the condition that no external auxiliary power supply exists.

Description

Technical Field

[0001] This invention relates to the field of surge arrester monitoring technology, and in particular to an online surge arrester monitoring method and system. Background Technology

[0002] Deploying online monitoring devices for surge arresters in outdoor or remote locations such as power transmission lines and distributed renewable energy power stations faces fundamental challenges: it is difficult to obtain a stable and continuous industrial power supply. Traditional wired power supply or battery power supply solutions are either too costly or unsustainable over the equipment's lifespan of several years or even ten years, making it impossible for most advanced monitoring technologies based on continuous or high-frequency sampling to be implemented in such scenarios.

[0003] Chinese patent application number 202511578522.4 discloses an online monitoring system for surge arresters based on self-powered power supply. This invention directly obtains electrical energy from the leakage current of the surge arrester through a self-powered design, initially eliminating dependence on external power sources. It reduces average power consumption through hardware circuit optimization and intermittent sleep mode, and wakes up at fixed intervals to collect all data.

[0004] However, since the leakage current of the surge arrester is unstable, while the energy consumption of periodic sampling is fixed, the monitoring system cannot guarantee basic monitoring functions during periods of energy scarcity, while during periods of abundant energy, it may waste energy due to the fixed sampling frequency.

[0005] Even if a solar power supply is added to the above solutions, the energy collection rate may be lower than the power consumption required for the system to complete one full data acquisition under conditions of continuous rain, no lightning current, and low leakage current, resulting in data interruption. Conversely, when the energy source is abundant, the fixed acquisition frequency cannot utilize the excess energy for more intensive or in-depth monitoring, resulting in energy waste.

[0006] To address these issues, this invention proposes an online monitoring method and system for surge arresters. Summary of the Invention

[0007] The purpose of this invention is to provide an online monitoring method and system for surge arresters to solve the technical problems mentioned in the background section.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an online monitoring method for surge arresters, comprising the following steps:

[0009] In the non-response state, at least one characteristic parameter related to the state of the surge arrester and the energy state of the energy storage module are continuously monitored by the monitoring unit.

[0010] The monitoring unit asynchronously generates different types of wake-up events based on the monitoring results. The types of wake-up events include at least a first type of event triggered by the feature parameters meeting preset conditions, and a second type of event triggered by the energy state of the energy storage module reaching a preset threshold.

[0011] In response to any of the aforementioned wake-up events, the main control and diagnostic unit selects and executes the corresponding monitoring and diagnostic task from a number of predefined response strategies based on the type of wake-up event and the current energy state.

[0012] After the selected monitoring and diagnostic task is completed, the main control and diagnostic unit and non-essential modules are powered down and returned to a non-responsive state.

[0013] Preferably, the characteristic parameter includes the leakage current signal of the surge arrester, and the triggering condition for the first type of event is that the time domain or frequency domain characteristics of the leakage current signal, relative to a dynamically updated baseline value, satisfy a preset deviation condition.

[0014] Preferably, the action events identified based on the leakage current signal have a combination of spatiotemporal characteristics that conform to at least one preset failure mode associated with a specific failure mechanism of the surge arrester valve plate;

[0015] The spatiotemporal feature combination is used to distinguish between the cumulative aging mode caused by multiple operational overvoltages and the normal protection mode of a single lightning strike.

[0016] The distinction is achieved through the following steps: counting the cumulative occurrence frequency of action events within a preset first time window, and calculating the cumulative energy score of all action events within the same time window. When the cumulative occurrence frequency exceeds a first frequency threshold and the cumulative energy score is lower than a first energy threshold, it is determined to be a cumulative aging mode.

[0017] Preferably, the second type of event includes: an energy sufficiency event triggered when the energy level of the energy storage module is detected to be higher than an energy threshold;

[0018] The monitoring and diagnostic tasks performed in response to the energy-sufficient event specifically include: continuously acquiring the full current waveform and resistive current component for at least one power frequency cycle at a sampling rate of not less than a first sampling rate, simultaneously acquiring the arrester body temperature and ambient temperature, and calculating the current health status index of the arrester based on the full current waveform, resistive current component, and temperature data.

[0019] Preferably, in response to the first type of event, a response strategy is selected based on the current energy state, specifically including:

[0020] Obtain the current voltage value of the energy storage module. If the voltage value is lower than a preset voltage threshold, execute a simplified diagnostic strategy. The simplified diagnostic strategy includes at least local alerts and event logging.

[0021] If the voltage value is greater than or equal to the voltage threshold, a complete diagnostic strategy is executed. The complete diagnostic strategy includes: acquiring the leakage current waveform, performing spectrum or harmonic analysis on the waveform to determine the fault characteristics, and remotely transmitting a diagnostic report containing the fault characteristics and timestamps through a wireless communication module.

[0022] Preferably, the dynamically updated baseline value is adaptively adjusted based on historical data of the leakage current signal and ambient temperature parameters. Specifically, the adjustment method is as follows:

[0023] The moving average value of the effective value of the leakage current signal is calculated with a preset second time window as the period;

[0024] Based on the real-time collected ambient temperature, the pre-stored temperature-leakage current correction coefficient table is queried to obtain the correction coefficient at the current temperature. The moving average value is then multiplied by the correction coefficient to obtain the updated dynamic baseline value.

[0025] An online monitoring system for surge arresters includes:

[0026] An energy management unit is used to acquire and store electrical energy, and includes an energy storage module and an energy status monitoring circuit.

[0027] The monitoring unit, normally powered by the energy management unit and in a non-responsive state, includes:

[0028] The fault symptom sensing module is used to continuously monitor at least one characteristic parameter related to the state of the surge arrester, and generate a first type of wake-up signal when the parameter meets preset conditions.

[0029] An energy event sensing module, connected to the energy state monitoring circuit, is used to generate a second type of wake-up signal when the energy state of the energy storage module reaches a preset threshold.

[0030] The main control and diagnostic unit is connected to the monitoring unit and the energy management unit respectively. It is in a low-power state by default and is configured to: be woken up in response to the first or second type of wake-up signal, perform corresponding monitoring and diagnostic tasks according to the wake-up signal type and the current energy state, and control itself and associated modules to enter a low-power state after the task is completed.

[0031] Preferably, the energy event sensing module is configured to monitor the voltage of the energy storage module and generate the second type of wake-up signal when the voltage is higher than a preset energy threshold.

[0032] Preferably, the fault symptom perception module is implemented by an independent low-power microcontroller or dedicated logic circuit, which is configured to: run an algorithm capable of calculating the spatiotemporal feature combination of action events, and match and compare the combination with a pre-stored feature vector library representing different fault modes to determine whether to generate the first type of wake-up signal.

[0033] Preferably, the main control and diagnostic unit is further configured to: in response to the first type of wake-up signal, first read the real-time voltage value of the energy state monitoring circuit, and dynamically select the type of peripheral module to be activated, the data acquisition accuracy or the communication strength according to the preset voltage range to which the voltage value belongs;

[0034] The preset voltage range includes at least a low-energy range and a high-energy range, which are divided by the voltage threshold.

[0035] The beneficial effects of this invention are:

[0036] This invention enables the monitoring system to remain in a non-responsive state under normal conditions and then be triggered into a responsive state by events. This allows the system to maintain ultra-low power consumption (microamps) for the listening unit most of the time, while completely shutting down the high-power main system. This significantly reduces average power consumption compared to traditional periodic wake-up schemes. Even in extreme conditions such as continuous rain or weak leakage current, the meager energy collected by the energy management unit can maintain continuous monitoring by the listening unit, ensuring uninterrupted monitoring. When the power input is sufficient, the system will not waste energy unnecessarily, thus concentrating limited energy at the most critical monitoring moments. This ensures that the monitoring device can operate more stably for extended periods without any external auxiliary power supply.

[0037] Furthermore, the system can distinguish between a single lightning discharge from a surge arrester and the cumulative impact of multiple harmful operational overvoltages, ensuring that subsequent diagnosis is triggered only when the monitoring signal matches the preset fault mode characteristics. This avoids frequent false wake-ups and energy waste caused by misjudging the normal protection action of the surge arrester as a fault. Moreover, since the monitoring unit is always online, it has a continuous sensing capability for real early, slowly changing fault characteristics (such as the slow increase of leakage current), overcoming the risk of missed detections due to missed sampling points in periodic sampling, making fault warnings more timely and reliable. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of the control flow of an online monitoring method for surge arresters according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] In some remote outdoor locations, most existing online surge arrester monitoring devices struggle to obtain a stable and continuous industrial power supply. While some devices directly draw power from the surge arrester's leakage current, thus initially reducing reliance on external power, their method of waking up and collecting all data at fixed intervals has significant shortcomings. This is because the surge arrester's leakage current is unstable, while the energy consumption for periodic sampling is fixed. This results in the monitoring system being unable to guarantee basic monitoring functions during periods of energy scarcity, while potentially wasting energy during periods of abundant energy due to the fixed sampling frequency.

[0041] Furthermore, even if solar power is added to the aforementioned self-powered scheme to improve power supply stability, the energy collection rate may remain lower than the power consumption required for the system to complete one full data acquisition under conditions of continuous rain, no lightning current, and low leakage current, resulting in data interruption. Conversely, when the energy source is abundant, the fixed acquisition frequency cannot utilize the excess energy for more intensive or in-depth monitoring, thus causing energy waste.

[0042] This embodiment was invented to solve the above problems.

[0043] Please see Figure 1 As shown, an embodiment of the present invention provides an online monitoring system for surge arresters, which includes an energy management unit, a monitoring unit, a main control and diagnostic unit, and related peripherals. The monitoring unit includes a fault symptom sensing module and an energy event sensing module.

[0044] The aforementioned energy management unit is used to acquire and store electrical energy. It includes an energy storage module and an energy state monitoring circuit. In this embodiment, the energy storage module uses a large-capacity, low-self-discharge supercapacitor to store irregularly acquired electrical energy and provide support when the system needs to operate with a large current. The energy state monitoring circuit uses a precision resistor voltage divider network to divide the capacitor voltage to a range suitable for the microcontroller input.

[0045] In this embodiment, the power acquisition of the energy management unit mainly relies on the leakage current of the surge arrester. Specifically, it is achieved by a current transformer coupled to the grounding lead of the surge arrester. The core of the current transformer is made of high-permeability nanocrystalline or permalloy material, and the number of turns in the secondary winding is optimized to achieve good energy coupling efficiency for power frequency leakage currents in the mA to several A range and short-term impulse currents. The output of the current transformer is an AC signal, which is first converted into pulsating DC by a full-bridge rectifier circuit composed of low forward voltage drop Schottky diodes. Then, the rectified voltage is initially smoothed by a π-type filter network. After that, a power management chip or a circuit composed of an ultra-low quiescent current DC-DC boost converter and a linear regulator is connected to the subsequent stage to adapt to a wide range of input currents and achieve efficient energy storage. The above schemes all adopt existing technologies and will not be described in detail below. In other embodiments, power can also be obtained from the environment of the surge arrester, which is not limited here.

[0046] The aforementioned monitoring unit is normally powered by the energy management unit and is in a non-responsive state. It mainly includes a fault symptom sensing module and an energy event sensing module.

[0047] The aforementioned fault symptom sensing module is used to continuously monitor at least one characteristic parameter related to the state of the surge arrester. In this embodiment, the fault symptom sensing module uses a high-precision sampling resistor connected in series in the monitoring circuit of the surge arrester's grounding wire. The voltage drop across the sampling resistor is proportional to the leakage current. The small signal detected during the monitoring process is initially amplified and buffered by an instrumentation amplifier composed of operational amplifiers, and then sent to its internal microcontroller. The microcontroller calculates the effective value of the signal in real time, detects pulse events, counts pulse frequencies, estimates pulse energy, and compares it with the dynamic baseline value and fault mode feature library stored in the internal memory. The fault mode feature library pre-stores at least one characteristic parameter related to the specific state of the surge arrester valve plate. The feature vector or judgment rule associated with the failure mechanism, for example, for the cumulative aging mode caused by multiple operational overvoltages, its feature vector can be defined as follows: within a preset first time window (e.g., 60 seconds), the cumulative occurrence frequency of the action event exceeds a first frequency threshold (e.g., 5 times), and the cumulative energy integral is lower than a first energy threshold (this threshold can be set based on historical data as a small percentage of a single typical lightning current impact energy, e.g., 1%). When the feature combination calculated by the microcontroller satisfies this rule, it is judged as a cumulative aging mode. The rules in the fault mode feature library can be derived by analyzing a large amount of historical fault data, laboratory accelerated aging test data, or by summarizing the physical model of electrothermal aging based on the surge arrester valve material. Once the algorithm determines that the current signal characteristics meet the preset fault mode, the microcontroller will output a low-to-high transition signal, i.e., the first type of wake-up signal.

[0048] The aforementioned energy event sensing module is connected to the voltage divider point of the energy status monitoring circuit. Its core is a window comparator chip, which internally sets an energy threshold. This threshold is set based on the rated voltage of the energy storage module and the minimum energy required for the system to complete a full health check task. For example, for a supercapacitor with a rated voltage of 5.5 volts, considering circuit efficiency and task power consumption, the energy threshold can be set to 4.2 volts to ensure sufficient energy to support a complete data acquisition, processing, and transmission process. When the voltage at the voltage divider point is higher than this energy threshold, the output of the window comparator flips, directly generating a digital level signal as a second type of wake-up signal.

[0049] The aforementioned main control and diagnostic units are connected to the monitoring unit and the power management unit, respectively. In terms of hardware, this embodiment uses an ultra-low power microcontroller based on the ARM Cortex-M core as the core, such as the STM32L4 series. This microcontroller is in a deep low power mode by default under normal system conditions, at which time most of its clock sources are turned off, and only a few wake-up sources are retained.

[0050] The specific hardware connection topology is as follows: The microcontroller has at least two external interrupt pins connected to the first type of wake-up signal line and the second type of wake-up signal line from the monitoring unit, and these two signal lines are configured to rise-edge or fall-edge triggering mode; a high-precision analog-to-digital converter channel of the microcontroller is connected to the voltage divider point of the energy state monitoring circuit in the energy management unit, used to read the precise voltage value of the energy storage module (such as a supercapacitor) after wake-up; a high-speed serial peripheral interface is used to connect an independent high-precision analog-to-digital converter chip, such as a 24-bit Σ-Δ analog-to-digital converter, which is responsible for high-quality waveform sampling of the leakage current signal when needed; a general-purpose input / output pin controls an analog switch to switch the flow of the leakage current signal: whether to the low-power sampling front end of the monitoring unit or to the sampling loop of the independent high-precision analog-to-digital converter; a serial communication interface is connected to a low-power wireless communication module; in addition, other general-purpose input / output pins are connected to local audio-visual indicators (such as LEDs and buzzers) and non-volatile memory, respectively.

[0051] The main control and diagnostic unit is entirely event-driven. When it is awakened from deep sleep by a wake-up signal, it first executes the startup code to initialize the necessary peripherals. Then, it determines the wake-up source by querying the interrupt flag. If the wake-up source is a second type of wake-up signal, it immediately executes the preset comprehensive health check task process. Specifically, it controls the analog switch to switch the signal to the high-precision sampling circuit. The high-precision analog-to-digital converter continuously acquires the full waveform of the leakage current for at least one power frequency cycle at a high sampling rate. At the same time, it wakes up and reads the temperature sensor installed on the surge arrester body and the ambient temperature sensor on the monitoring device through the internal integrated circuit bus. After acquiring all the data, the microcontroller calls its built-in signal processing and status evaluation program to perform full waveform analysis, resistive current component separation, temperature data processing, and comprehensively calculate the current health status index of the surge arrester. Finally, it turns on the power of the wireless communication module and sends the packaged health status data packet to the remote monitoring center. After the entire task is completed, the system orderly shuts down the power of all high-power peripherals such as the high-precision analog-to-digital converter and the wireless module, and puts the microcontroller itself back into deep sleep mode.

[0052] If the wake-up source is a Type I signal, the main control and diagnostic unit enters a different decision-making process: First, it activates its own analog-to-digital converter to sample the voltage divider point of the energy state detection circuit to obtain the current voltage value of the energy storage module. Then, it compares this voltage value with a preset voltage threshold stored in non-volatile memory. This voltage threshold is set based on the minimum voltage required for the system to maintain the minimum operation of the monitoring unit and execute simplified diagnostic strategies. For example, for a system with an operating voltage of 3.3 volts, considering the minimum operating voltage and margin of each chip, this threshold can be set to 2.8 volts. If the current voltage value is lower than the voltage threshold, it indicates that the system is under energy pressure, and a simplified diagnostic strategy is executed. This includes: issuing a local alarm signal through a general-purpose input / output pin, such as controlling an LED to flash a specific number of times, and recording the event of this event. Type encoding and timestamps are written to non-volatile memory via a serial peripheral interface for storage. If the current voltage value is higher than or equal to the voltage threshold, indicating that the energy allows for complex operations, a complete diagnostic strategy is executed. This includes: switching signal paths and starting a high-precision analog-to-digital converter, capturing the leakage current waveform at the moment of the fault, and simultaneously reading the ambient temperature. Subsequently, detailed spectrum analysis or harmonic analysis is performed on the waveform to extract features related to the fault type (such as the content of specific harmonics). Finally, a diagnostic report containing the fault feature analysis results, timestamp, and ambient temperature is sent out via a wireless communication module. Regardless of whether the simplified or complete strategy is executed, the system will clear the hardware state and return the microcontroller to deep sleep mode after the task is completed, waiting for the next event to wake it up.

[0053] Based on the above-mentioned online monitoring system, this embodiment also proposes an online monitoring method for surge arresters, which specifically includes the following steps:

[0054] First, the monitoring system is in a non-responsive state under normal conditions when no events occur. In this state, the main control and diagnostic units and their high-power peripheral modules such as high-precision sampling and wireless communication are all powered off or in deep sleep. The current consumed by the system is reduced to the microampere level. The monitoring unit is powered by the energy management unit and maintains a low-power monitoring state to continuously monitor at least one characteristic parameter related to the health status of the surge arrester and the energy status of the energy storage module. In actual operation, the leakage current signal of the surge arrester is preferred as the characteristic parameter. The fault symptom sensing module obtains the current signal through the sampling resistor connected in series in the grounding loop. After amplification and conditioning, it is sampled and calculated by an independent low-power microcontroller at a low fixed frequency. At the same time, the energy event sensing module continuously compares the voltage of the energy storage capacitor voltage divider point with the preset energy threshold through a window comparator.

[0055] Then, based on the monitoring results, the monitoring unit asynchronously generates different types of wake-up events. The triggering of these events depends entirely on the actual state changes of the monitored object. The types of wake-up events include at least the first type triggered by the satisfaction of preset conditions for characteristic parameters, and the second type triggered by the energy storage module reaching a preset threshold. Specifically, the microcontroller in the monitoring unit performs real-time analysis of the leakage current signal, not only calculating its effective value but also focusing on identifying pulse-like action events. The algorithm combines the spatiotemporal characteristics of the monitored action events (such as frequency, energy, and interval) with a pre-stored fault mode feature library associated with specific failure mechanisms of surge arrester valve plates. In this embodiment, the objects distinguished after comparison are "cumulative aging mode caused by multiple operational overvoltages" and "normal protection mode of a single lightning strike." For example, the algorithm will count events within a preset 60-second timeframe. Within the window, the cumulative frequency of action events is counted, and the cumulative energy integral of these events is estimated. If the characteristic combination of "high frequency (e.g., more than 5 times) but low cumulative energy (e.g., much lower than the energy of a single lightning strike)" is determined, it matches the suspected fault mode of cumulative overvoltage impact. At this time, a first type of wake-up signal is generated. After the main control and diagnostic units are woken up, they will review and deeply analyze the relevant data to finally confirm the fault status. This pattern recognition mechanism ensures the accuracy of wake-up and avoids invalid or even harmful frequent wake-ups caused by the normal discharge of lightning current by the surge arrester. As for the second type of event, its generation logic is relatively direct. That is, when the voltage of the energy storage capacitor rises due to continuous energy extraction and exceeds the preset energy threshold (e.g., 4.2V), the output of the window comparator flips, directly generating a second type of wake-up signal, indicating that the system has entered an energy surplus state and can use the surplus resources for active health checks.

[0056] When any wake-up event occurs, the system transitions from a non-response state to a response state. After initialization, the woken-up main control and diagnostic unit makes a decision based on two key pieces of information: the type of wake-up event and the current energy state (voltage value) read through its own analog-to-digital converter. For example, if the wake-up source is a type II event, the current specific voltage (known to be higher than the voltage threshold) does not need to be considered, and the preset comprehensive health check task is executed directly. The execution method of this task is as described above and will not be repeated here.

[0057] If the wake-up source is a Type I event, the main control and diagnostic unit will immediately sample the energy storage capacitor voltage and compare it with a preset voltage threshold (e.g., 3.3V). If the current voltage value is lower than the threshold, it indicates that the system is in a period of energy shortage. At this time, the system will execute a simplified diagnostic strategy. The goal of this strategy is to complete the confirmation and recording of the fault event with minimal energy cost, ensuring that the core early warning function does not fail under any energy conditions. If the current voltage value is higher than or equal to the threshold, it indicates that the energy reserve allows for more in-depth operation. The system will execute a complete diagnostic strategy, which is similar to a health check but more targeted: after collecting high-precision waveform and temperature data, it focuses on performing spectrum or harmonic analysis on the waveform during the fault characteristic period, extracting specific features such as the third harmonic growth ratio to determine whether it is dampness or aging, and forming a detailed report containing specific diagnostic conclusions for remote reporting. This hierarchical response mechanism perfectly achieves the goal of maximizing monitoring effectiveness under limited energy constraints.

[0058] More importantly, in order to make the entire monitoring system environmentally adaptable and long-term stable, the surge arrester monitoring method in this embodiment also includes a dynamic self-learning mechanism for key parameters. Specifically, it is an adaptive adjustment of the dynamic baseline value of leakage current. The monitoring system does not use a fixed threshold to determine whether the current is abnormal. The main control unit records the calculated steady-state value of leakage current and the corresponding ambient temperature each time a complete diagnostic or health check task is performed. These historical data are used to periodically (e.g., every 24 hours) update the baseline value.

[0059] The update algorithm calculates the new dynamic baseline value using the following formula. :

[0060]

[0061] in, The first record within the second time window There are 10 RMS leakage current sampling data points, where N is the total number of samples within this window. This is the moving average (MA) of historical data.

[0062] T represents the real-time ambient temperature. The correction coefficient is obtained from the pre-stored "Temperature-Leakage Current Correction Coefficient Table". This coefficient reflects the physical characteristics of current change with temperature and can be obtained by fitting experimental data. For example, with 25 degrees Celsius as the reference, test data shows that for every 10 degrees Celsius increase in temperature, the leakage current increases by an average of about 8%, so the correction coefficient for the corresponding temperature range can be set to 1.08; for every 10 degrees Celsius decrease in temperature, the current decreases by an average of about 8%, so the correction coefficient is 0.92. Subsequently, this new baseline value is sent to the fault symptom perception module of the monitoring unit. When the surge arrester is in the high temperature of summer, the system will automatically increase the expected value of the normal current to prevent false alarms; in the low temperature of winter, the expected value of the current will be reduced to increase the sensitivity to abnormal increases in small currents.

[0063] In summary, this invention enables the monitoring system to remain in a non-responsive state under normal conditions and then be triggered into a responsive state by events. This allows the system to maintain ultra-low power consumption at the microampere level for the listening unit most of the time, while completely shutting down the high-power main system. This significantly reduces the average power consumption compared to traditional periodic wake-up schemes. Even in extreme conditions such as continuous rain or weak leakage current, the meager energy collected by the system is sufficient to maintain the continuous operation of the listening unit, ensuring uninterrupted monitoring. Furthermore, when energy input is abundant, the system does not waste energy unnecessarily, thus concentrating limited energy at the most critical monitoring moments. This ensures that the monitoring device can operate more continuously and stably for extended periods without any external auxiliary power supply.

[0064] Meanwhile, the system can distinguish between a single lightning discharge from a surge arrester and the cumulative impact of multiple harmful operational overvoltages, ensuring that subsequent diagnosis is triggered only when the monitoring signal matches the preset fault mode characteristics. This avoids frequent false wake-ups and energy waste caused by misjudging the normal protection action of the surge arrester as a fault. Furthermore, since the monitoring unit is always online, it has a continuous sensing capability for real early, slowly changing fault characteristics (such as the slow increase of leakage current), overcoming the risk of missed detection due to missed sampling points in periodic sampling, making fault early warning more timely and reliable.

[0065] Finally, this invention adaptively adjusts the dynamic baseline value, compensating for the baseline value in real time based on historical leakage current data and real-time ambient temperature, thereby eliminating the interference of ambient temperature changes on the normal range of leakage current. When the surge arrester body is in a high-temperature environment in summer, the system can automatically increase the expected normal current to avoid false alarms caused by thermally increased current; while in a low-temperature environment in winter, the expected current can be lowered accordingly, making the system more sensitive to slight abnormal changes in leakage current.

[0066] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for online monitoring of surge arresters, characterized in that, Includes the following steps: In the non-response state, at least one characteristic parameter related to the state of the surge arrester and the energy state of the energy storage module are continuously monitored by the monitoring unit. The monitoring unit asynchronously generates different types of wake-up events based on the monitoring results. The types of wake-up events include at least a first type of event triggered by the feature parameters meeting preset conditions, and a second type of event triggered by the energy state of the energy storage module reaching a preset threshold. In response to any of the aforementioned wake-up events, the main control and diagnostic unit selects and executes the corresponding monitoring and diagnostic task from a number of predefined response strategies based on the type of wake-up event and the current energy state. After the selected monitoring and diagnostic task is completed, the main control and diagnostic unit and non-essential modules are powered down and returned to a non-responsive state.

2. The online monitoring method for surge arresters according to claim 1, characterized in that, The characteristic parameters include the leakage current signal of the surge arrester. The triggering condition for the first type of event is that the time domain or frequency domain characteristics of the leakage current signal, relative to a dynamically updated baseline value, satisfy a preset deviation condition.

3. The online monitoring method for surge arresters according to claim 2, characterized in that, The preset deviation condition is: the action event identified based on the leakage current signal has a spatiotemporal characteristic combination that conforms to at least one preset failure mode associated with a specific failure mechanism of the surge arrester valve plate. The spatiotemporal feature combination is used to distinguish between the cumulative aging mode caused by multiple operational overvoltages and the normal protection mode of a single lightning strike. The distinction is achieved through the following steps: counting the cumulative occurrence frequency of action events within a preset first time window, and calculating the cumulative energy score of all action events within the same time window. When the cumulative occurrence frequency exceeds a first frequency threshold and the cumulative energy score is lower than a first energy threshold, it is determined to be a cumulative aging mode.

4. The online monitoring method for surge arresters according to claim 1, characterized in that, The second type of event includes: an energy sufficiency event triggered when the energy level of the energy storage module is detected to be higher than the energy threshold; The monitoring and diagnostic tasks performed in response to the energy-sufficient event specifically include: continuously acquiring the full current waveform and resistive current component for at least one power frequency cycle at a sampling rate of not less than a first sampling rate, simultaneously acquiring the arrester body temperature and ambient temperature, and calculating the current health status index of the arrester based on the full current waveform, resistive current component, and temperature data.

5. The online monitoring method for surge arresters according to claim 1, characterized in that, When responding to the first type of event, a response strategy is selected based on the current energy state, specifically including: Obtain the current voltage value of the energy storage module. If the voltage value is lower than a preset voltage threshold, execute a simplified diagnostic strategy. The simplified diagnostic strategy includes at least local alerts and event logging. If the voltage value is greater than or equal to the voltage threshold, a complete diagnostic strategy is executed. The complete diagnostic strategy includes: acquiring the leakage current waveform, performing spectrum or harmonic analysis on the waveform to determine the fault characteristics, and remotely transmitting a diagnostic report containing the fault characteristics and timestamps through a wireless communication module.

6. The online monitoring method for surge arresters according to claim 2, characterized in that, The dynamically updated baseline value is adaptively adjusted based on historical data of the leakage current signal and ambient temperature parameters. Specifically, the adjustment method is as follows: The moving average value of the effective value of the leakage current signal is calculated with a preset second time window as the period; Based on the real-time collected ambient temperature, the pre-stored temperature-leakage current correction coefficient table is queried to obtain the correction coefficient at the current temperature. The moving average value is then multiplied by the correction coefficient to obtain the updated dynamic baseline value.

7. An online monitoring system for surge arresters, used to implement the online monitoring method for surge arresters according to any one of claims 1-6, characterized in that, include: An energy management unit is used to acquire and store electrical energy, and includes an energy storage module and an energy status monitoring circuit. The monitoring unit, normally powered by the energy management unit and in a non-responsive state, includes: The fault symptom sensing module is used to continuously monitor at least one characteristic parameter related to the state of the surge arrester, and generate a first type of wake-up signal when the parameter meets preset conditions. An energy event sensing module, connected to the energy state monitoring circuit, is used to generate a second type of wake-up signal when the energy state of the energy storage module reaches a preset threshold. The main control and diagnostic unit is connected to the monitoring unit and the energy management unit respectively. It is in a low-power state by default and is configured to: be woken up in response to the first or second type of wake-up signal, perform corresponding monitoring and diagnostic tasks according to the wake-up signal type and the current energy state, and control itself and associated modules to enter a low-power state after the task is completed.

8. The online monitoring system for surge arresters according to claim 7, characterized in that, The energy event sensing module is configured to monitor the voltage of the energy storage module and generate the second type of wake-up signal when the voltage is higher than a preset energy threshold.

9. The online monitoring system for surge arresters according to claim 8, characterized in that, The fault symptom perception module is implemented by an independent low-power microcontroller or dedicated logic circuit, which is configured to: run an algorithm that can calculate the spatiotemporal feature combination of action events, and match and compare the combination with a pre-stored feature vector library representing different fault modes to determine whether to generate the first type of wake-up signal.

10. The online monitoring system for surge arresters according to claim 9, characterized in that, The main control and diagnostic unit is also configured to: in response to the first type of wake-up signal, first read the real-time voltage value of the energy state monitoring circuit, and dynamically select the type of peripheral module to be activated, the data acquisition accuracy or the communication strength according to the preset voltage range to which the voltage value belongs; The preset voltage range includes at least a low-energy range and a high-energy range, which are divided by the voltage threshold.

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