A relay automatic testing system and method based on parallel processing

Abstract

The present invention provides a relay automation test system and method based on parallel processing, which relates to the field of relay testing technology. The method comprises the following steps: recording the reference signal waveform and timing data, baseline status indicators and initial reclosing times increment under the normal operating state of the equipment; real-time monitoring and acquisition of equipment signal differential analysis signal changes during the simulated power failure-reclosing process, and sending them to the next module; quantifying system state changes to obtain the equipment safety state value; creating a reclosing times adaptive algorithm, judging whether to continue testing and determining the next simulated reclosing times based on the equipment safety state value and the initial reclosing times increment; determining the maximum tolerable reclosing times based on the test results, and obtaining a recovery robustness index. The present invention solves the problem that fixed step-size testing cannot fit the actual state of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of relay testing, and in particular to a relay automatic testing system and method based on parallel processing. Background Art

[0002] Relay protection devices are core components for the safe and stable operation of power systems. Their reliability is directly related to the effectiveness of grid fault isolation and equipment protection. As power systems expand and new types of power equipment are added, relay protection devices face more complex operating conditions and higher reliability requirements. After a device disconnects and reconnects, sensors or instruments may lose status or need to reinitialize, impacting system stability.

[0003] Traditional testing involves sequentially performing static and dynamic tests on relays using handheld test instruments or single-channel automated systems to verify their operating characteristics and durability. However, these methods often rely on manual operation or simple scripts, resulting in low efficiency, susceptibility to human error, and poor repeatability. In field environments, maintenance personnel often need to connect each relay and perform multiple rounds of testing, which is time-consuming, labor-intensive, and requires high-skilled personnel. Test data is often recorded in a dispersed manner, making subsequent analysis cumbersome and difficult to compare and statistically analyze across batches of samples.

[0004] Currently, most relay tests focus on static operating characteristics and predefined endurance cycles, lack health awareness, and are unable to timely adjust subsequent test intensity based on the relay's response in previous reclosing cycles. This can easily lead to two extremes: one is that the increment is too small, resulting in too many test rounds and a long test time; the other is that the increment is too large, which may lead to sudden failure once it approaches the criticality, making it impossible to accurately locate the maximum tolerable number of times and increasing the risk of equipment damage. Summary of the Invention

[0005] Purpose of the invention: To propose a relay automatic testing system and method based on parallel processing to solve the existing problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a relay automatic testing system based on parallel processing, comprising:

[0007] Initial state acquisition module, used to record the baseline signal and initial reclosing times increment under normal equipment operation;

[0008] A real-time signal monitoring module is used to monitor and collect device signals in real time during the simulated power outage-reclosing process, analyze signal changes in combination with the baseline signal, and send the signals to the status indicator extraction module;

[0009] Status indicator extraction module, used to calculate the device safety status value;

[0010] A reclosing times adaptive scheduling module is used to create a reclosing times adaptive algorithm, wherein the reclosing times adaptive algorithm is used to output the reclosing times increment of each relay in the next round according to the initial reclosing times increment and the equipment safety status value, and determine whether to continue the test. If so, it returns to the real-time signal monitoring module; otherwise, it runs the recovery robustness assessment module;

[0011] The recovery robustness evaluation module is used to determine the maximum tolerable reclosing times according to the real-time equipment safety status value and obtain the recovery robustness index.

[0012] A further improvement of the present invention is that the initial state acquisition module collects the synchronous trigger time, sampling clock, and waveform signal of each device through a synchronous sampling device, and obtains the baseline characteristics of all data and sends them to the real-time signal monitoring module;

[0013] Before the test begins, the initial state acquisition module records and initializes an initial reclosing times increment , and sent to the adaptive scheduling module for the number of reclosing times.

[0014] A further improvement of the present invention is that the real-time signal monitoring module triggers a reclosing event through the control terminal before each simulation event, closes the reclosing switch after a set time after disconnection, and continues to record the device output until the signal stabilizes again after the power is re-closed; records each sampling timestamp, each phase voltage peak and waveform before and after the power outage; and performs differential analysis on the baseline characteristics corresponding to all data after each simulation and passes them to the next module.

[0015] A further improvement of the present invention is that the reclosing times adaptive scheduling module includes a state trend judgment unit and an adaptive increment adjustment unit;

[0016] After each test, the state trend judgment unit obtains the health state drift value based on the change in the real-time device safety state value. The latest health status drift is combined with the most recent M historical health status drifts to form a health status drift sequence. The number of positive values ​​N+ and negative values ​​N- in the health status drift sequence is counted. If the number of positive values ​​N+ is greater than 70% of all data in the sequence, the health status is judged to be in a downward trend. If the number of negative values ​​N- is greater than 70% of all data in the sequence, the health status is judged to be in an upward trend. Otherwise, the health status is judged to be fluctuating.

[0017] A further improvement of the present invention is that the adaptive incremental adjustment unit is used to run a reclosing times adaptive algorithm, and the reclosing times adaptive algorithm includes:

[0018] Define the optimization goal and define the particle dimension as two components Composition, including the current reclosing times increment And the steady-state time of this round of testing ;

[0019] Set the number of particles P. Initially, each particle is evenly distributed within the domain of definition. The domain of definition is expressed as and ,in, 、 、 、 Respectively represent the minimum reclosing times increment, maximum reclosing times increment, minimum test steady-state time, and maximum test steady-state time set by the system;

[0020] When the current reclosing times increment And the steady-state time of this round of testing After adjusting the number of equipment reclosing times, the particle speed and particle position are updated according to the particle update strategy. The individual optimal position and the global optimal position are updated after the fitness of each particle is evaluated according to the objective function. When the fitness of the individual optimal position of a particle is greater than the fitness of the initial global optimal position, the individual optimal position of the particle is updated as the new global optimal position, and the global optimal position is output directly as the increment of the number of reclosing times of each relay in the next round.

[0021] A further improvement of the present invention is that the objective function is expressed as ,in, Indicates the negative impact of health status drift. When the health status is in a downward trend, the health status drift corresponding to the maximum positive value in the health status drift sequence is taken as the negative impact of health status. When the health status is in an upward trend, the negative impact of health status drift is 0. When the health status is oscillating, the average health status drift is taken as the negative impact of health status. Expressed as test duration, expressed as ,in Indicates the average time of one reclosing; represents the maintenance cost, taking , Indicates the set wear coefficient, 、 and Represents the weight coefficient.

[0022] A further improvement of the present invention is that the particle update strategy includes:

[0023] Update particle velocity and particle position according to health state drift and health state, and set particle initial position and speed , defining the optimal position of an individual , the global optimal position g is the current The position corresponding to the minimum value of the objective function;

[0024] For each particle i=1,2,...,P In the kth iteration, the following rules are executed: the health state drift at the current moment and the health state drift at the previous moment in the health state drift sequence are extracted to achieve inertia weight adaptation and update the inertia weight; for the current particle, the new round of trial speed is first calculated based on the current particle's speed in the previous round, the historical optimal position, and the global current optimal position, combined with the adaptive inertia weight and the learning factor; after obtaining the new speed, the current position of the particle can be updated.

[0025] A further improvement of the present invention is that the inertia weight adaptation specifically includes setting the inertia weight interval , then the inertia weight adaptive adjustment formula is:

[0026] ;

[0027] in, Indicates the maximum value of health status drift.

[0028] A further improvement of the present invention is that after each test, the status indicator extraction module calculates the device safety status value based on the status indicator measured in real time, specifically including:

[0029] The synchronization error is obtained by averaging the absolute values ​​of the differences between the synchronization trigger time recorded by the device and its baseline characteristics;

[0030] The sampling clock drift is obtained by comparing the cumulative offset of the device sampling clock with the baseline before and after reclosing.

[0031] The proportion of normal waveform signals observed after reclosing is restored is used to obtain the state retention rate;

[0032] The synchronization error, sampling clock drift and state retention rate are normalized and then weighted summed to obtain the device safety state value SSI.

[0033] A further improvement of the present invention is that the recovery robustness evaluation module continuously performs a fault-reclosing cycle on each relay sample during the test process, and compares the current equipment safety state value with the set equipment safety threshold in each round. When the current equipment safety state value is greater than the equipment safety threshold, the cumulative number of reclosing completed in the previous round is recorded as the maximum tolerable reclosing number of the sample. ,Will It is normalized and mapped to the [0,1] interval and recorded as the recovery robustness index.

[0034] On the other hand, the present invention provides a relay automation testing method based on parallel processing, comprising the following steps:

[0035] S1. Record the baseline signal and initial reclosing times increment under normal equipment operation;

[0036] S2, real-time monitoring and acquisition of device signals during the simulated power outage-reclosing process, combined with the baseline signal, analyzing signal changes, and sending the signals to S3;

[0037] S3. Calculate the device security status value;

[0038] S4. Create a reclosing number adaptive algorithm, which is used to output the reclosing number increment of each relay in the next round according to the initial reclosing number increment and the device safety state value, and determine whether to continue testing. If so, return to S2, otherwise run S5;

[0039] S5. Determine the maximum tolerable reclosing times based on the real-time equipment safety status value and obtain a recovery robustness index.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] (1) This invention uses the health state drift to drive the objective function, and combines particle swarm optimization to dynamically adjust the reclosing increment and steady-state waiting time. This allows the test step size to be rapidly expanded and the approach to the limit to be accelerated when the health is good; and the step size to be tightened in time to avoid excessive stress when the health deteriorates. The segmented processing of the negative impact of the falling, rising, and oscillating trends makes the scheduling decision both agile and robust, balancing the test speed and equipment life protection.

[0042] (2) In each round of testing, the equipment safety status value is compared with the threshold in real time. Once the SSI falls below the safety threshold or an abnormal opening / closing occurs, the system can be stopped immediately to avoid excessive wear or burning of the relay contacts. Compared with fixed large-number tests, this system can terminate intelligently in advance, significantly extending the equipment life and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 2 is a framework diagram of a relay automatic testing system based on parallel processing in an embodiment.

[0044] Figure 2 This is the framework diagram of the adaptive scheduling module for reclosing times in the relay automation test system.

[0045] Figure 3 Flowchart of the relay automatic testing method based on parallel processing in an embodiment. DETAILED DESCRIPTION

[0046] In the following description, numerous specific details are provided to provide a more thorough understanding of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced without one or more of these details. In other instances, certain technical features well known in the art have not been described to avoid confusion with the present invention.

[0047] Example 1

[0048] Figure 1 The following is a framework diagram of a relay automated testing system based on parallel processing disclosed in this embodiment, including:

[0049] The initial state acquisition module is used to record the baseline signal and initial reclosing times increment of the equipment under normal operating conditions; the baseline signal includes the reference voltage and current waveform and the baseline clock period under normal operating conditions of the equipment;

[0050] A real-time signal monitoring module is used to monitor and collect device signals in real time during the simulated power outage-reclosing process, analyze signal changes based on the baseline signal, and send the signals to the status indicator extraction module;

[0051] The status indicator extraction module is used to quantify the system status changes and obtain the equipment safety status value;

[0052] A reclosing times adaptive scheduling module is used to create a reclosing times adaptive algorithm, which is used to output the reclosing times increment of each relay in the next round based on the initial reclosing times increment and the equipment safety status value, and determine whether to continue the test. If so, it returns to the real-time signal monitoring module; if not, it runs the recovery robustness assessment module;

[0053] The recovery robustness evaluation module is used to determine the maximum tolerable reclosing times according to the real-time equipment safety status value and obtain the recovery robustness index.

[0054] The initial state acquisition module collects the synchronous trigger frame of each device through the synchronous sampling device. The trigger moment is marked by the transmitter, the data sampling timestamp sequence, the voltage and current waveforms, and the digital state;

[0055] And obtain the baseline characteristics of all data and send them to the real-time signal monitoring module; before the test starts, the initial state acquisition module records and initializes an initial reclosing number increment , can be set according to the equipment rated parameters, the first test results or the experience value, and sent to the reclosing number adaptive scheduling module.

[0056] The real-time signal monitoring module includes triggering a reclosing event through the control terminal before each simulation event, closing the reclosing switch after a set time after disconnection, and continuing to record the device output until the signal stabilizes again after the power is re-closed. The process is represented as the test steady-state time; recording each sampling timestamp before and after the power outage, the peak voltage and waveform of each phase; and performing differential analysis on the baseline characteristics corresponding to all data after each simulation and passing them to the next module.

[0057] Comparing the number of synchronous trigger frames and their time alignment is important because power outages that cause frame loss or offsets can reveal misaligned trigger times. Comparing sampling timestamp sequences is important because large clock recalibration errors or frequency drift can cause variations in timestamp intervals. Comparing voltage waveforms is important to analyze overshoot, attenuation, or distortion during voltage recovery after a transient interruption. The real-time monitoring module can directly transmit raw data to downstream indicator modules.

[0058] The state index extraction module includes calculating the device safety state value based on the real-time measured state index after each test, specifically including: obtaining the synchronization error by averaging the absolute value of the difference between the synchronization trigger time recorded by the device and its baseline characteristics; for example, if the baseline trigger time sequence is , the real-time triggering sequence of the device is , then the synchronization error , where H represents the number of timestamps; this metric reflects the time alignment accuracy of the device and the reference, and an increase indicates that synchronization is impaired.

[0059] The sampling clock drift is obtained by comparing the cumulative offset of the device sampling clock with the baseline before and after reclosing. For example, if the baseline clock period is Ts, after the first round of testing, the difference between the application intervals of the p-th frame and the q-th frame is the sampling clock drift, which is expressed as This indicator measures the degree of deviation of the device's internal clock or sampling period. Increased drift will accumulate and cause timing misalignment.

[0060] The state retention rate is obtained by measuring the proportion of normal signals observed after the reclosing is restored. For example, the collected analog voltage waveform is first band-pass filtered, and the amplitude of the filtered signal is mapped to the 0-1 interval to facilitate subsequent peak determination. The average amplitude of all peaks is calculated from the baseline phase signal. , set the peak judgment threshold to , Indicates the peak threshold coefficient, which can be 0.7-0.9 in this embodiment; in each sampling window, traverse the normalized signal sequence; if the amplitude of a sampling point is greater than the X points before and after it and exceeds , it is determined to be a valid peak. The selection of X should be greater than or equal to the number of sampling points corresponding to half a cycle to prevent false detection. Peak detection is performed in the baseline window and test window of the same length, and the result is and , directly substitute the peak number obtained above into the formula If R is close to 100%, it means that the loss of signal frames during the simulated power outage-reclosing process is very small and the state is maintained well. If R is significantly lower than 100%, it indicates that there are many frame losses or jitters after reclosing, and the state retention ability is reduced.

[0061] The synchronization error, sampling clock drift and state retention rate are normalized and then weighted summed to obtain the equipment safety status value SSI; the equipment safety status value calculation formula is: ,in, 、 、 Represents weight.

[0062] Example 2

[0063] Based on the inventive concept of Example 1, this embodiment proposes a specific implementation of a reclosing times adaptive scheduling module in a relay automation test system based on parallel processing; Figure 2 The framework diagram of the adaptive reclosing number scheduling module of this embodiment is shown, which specifically includes a state trend judgment unit and an adaptive increment adjustment unit;

[0064] The state trend judgment unit includes obtaining the health state drift value according to the change of the real-time device safety state value after each test. ; Subtract the device safety status value at the previous moment from the next moment; and combine the latest health status drift with the most recent M historical health status drifts to form a health status drift sequence; count the number of positive values ​​N+ and negative values ​​N- in the health status drift sequence. If the number of positive values ​​N+ is greater than 70% of all data in the sequence, the health status is judged to be in a downward trend. If the number of negative values ​​N- is greater than 70% of all data in the sequence, the health status is judged to be in an upward trend. Otherwise, the health status is judged to be fluctuating.

[0065] The adaptive increment adjustment unit is used to run the adaptive algorithm for the number of reclosing times, including defining the optimization goal as quickly seeking the optimal ΔN increment under the premise of minimizing the test cost, test time and the risk of relay state deterioration, so that the test can approach the maximum tolerable number of reclosing times in the least number of rounds, and defining the particle dimension as consisting of two components Composition, including the current reclosing times increment And the steady-state time of this round of testing , which is used to control the time required for equipment cooling or signal stabilization after each round of reclosing; set the number of particles P, and initially each particle is evenly distributed within the definition domain, which is expressed as and ,in, 、 、 、 Respectively represent the minimum reclosing times increment, maximum reclosing times increment, minimum test steady-state time, and maximum test steady-state time set by the system; when the current reclosing times increment is And the steady-state time of this round of testing After adjusting the number of equipment reclosing times, the particle speed and particle position are updated according to the particle update strategy. The individual optimal position and the global optimal position are updated after the fitness of each particle is evaluated according to the objective function. When the fitness of the individual optimal position of a particle is greater than the fitness of the initial global optimal position, the individual optimal position of the particle is updated as the new global optimal position, and the global optimal position is output directly as the increment of the number of reclosing times of each relay in the next round.

[0066] The objective function is expressed as ,in, Indicates the negative impact of health status drift. When the health status is in a downward trend, the health status drift corresponding to the maximum positive value in the health status drift sequence is taken as the negative impact of health status. When the health status is in an upward trend, the negative impact of health status drift is 0. When the health status is oscillating, the average health status drift is taken as the negative impact of health status. Indicates the test duration, ,in Indicates the average time of one reclosing; represents the maintenance cost, taking , Indicates the set wear coefficient. The more times, the higher the wear cost. 、 and Represents the weight coefficient.

[0067] The objective function of this implementation offers the following benefits: Maximizing protection against downward trends: When the health state shows a downward trend, the maximum positive value in the health state drift sequence is directly selected as the "negative health state drift impact," ensuring that the objective function is highly sensitive to the most severe drift fluctuations. This strategy can quickly detect the most adverse deviations of the relay during continuous reclosing, promptly amplifying the algorithm's response to unstable conditions, and effectively preventing excessive stress on the relay or sudden failure.

[0068] To ensure safety tolerance for rising trends, the "Negative Impact of Health Drift" is set to zero when the health state is trending upward, indicating that no negative penalty should be imposed on the reclosing increment. This approach fully utilizes the window of good relay recovery performance, allowing the algorithm to moderately increase the test intensity while maintaining safety, accelerating the approach to the maximum tolerable number of times while avoiding premature convergence due to small drifts, thereby improving overall test efficiency.

[0069] Smoothing out oscillations: When the health status fluctuates, the "negative impact of health status drift" is calculated by averaging the health status drift sequence. This balance strategy neither over-amplifies occasional fluctuations nor completely ignores the drift effect. This balance strategy smoothly reflects short-term jitter in relay status, avoids frequent step size adjustments caused by short-term noise in adaptive scheduling, and achieves more stable step size convergence.

[0070] Improve the robustness and stability of the algorithm. Through the above-mentioned segmented processing, the "negative impact of health state drift" is directly linked to the health trend, so that the objective function can respond quickly when obvious deterioration is quickly identified, and remain stable when the device state is stable or slightly jittering, ensuring that the adaptive scheduling module can show good noise resistance and stability under various test environments and signal quality conditions.

[0071] Optimizing the balance between test efficiency and equipment safety, the objective function design fully considers test speed and equipment lifespan: actively expanding the test scope when the equipment is in good condition to shorten the test cycle; and rapidly tightening the increments when the equipment is nearing its limits to reduce unnecessary reclosing cycles and avoid excessive wear. The entire process achieves dual optimization of efficiency and safety in evaluating relay "system recovery robustness."

[0072] The particle update strategy includes updating the particle velocity and particle position according to the health state drift and health state, setting the particle initial position and speed , defining the optimal position of an individual , the global optimal position g is the current The position corresponding to the minimum value of the objective function; for each particle i=1,2,...,P In the kth iteration, the following rules are executed: the health state drift at the current moment and the health state drift at the previous moment in the health state drift sequence are extracted to achieve inertia weight adaptation and update the inertia weight; for the current particle, the new round of trial speed is first calculated based on the current particle's speed in the previous round, the historical optimal position, and the global current optimal position, combined with the adaptive inertia weight and the learning factor; after obtaining the new speed, the current position of the particle can be updated.

[0073] The inertia weight adaptation specifically includes setting the inertia weight interval , then the inertia weight adaptive adjustment formula is:

[0074] ;

[0075] in, Indicates the maximum value of health status drift.

[0076] This strategy allows particles to be When the model is healthy and stable, the weight is lower and the exploration is enhanced; when the model is healthy and stable, the weight is increased and the convergence speed is faster.

[0077] The recovery robustness evaluation module includes continuously executing fault-reclosing cycles for each relay sample during the test process, and comparing the current device safety status value with the set device safety threshold in each round. When the current device safety status value is greater than the device safety threshold, the cumulative number of reclosing completed in the previous round is recorded as the maximum tolerable reclosing number of the sample. , in order to eliminate the differences in different test conditions or equipment specifications, It is normalized and mapped to the [0,1] interval and recorded as the recovery robustness index.

[0078] The thresholds, weights and other setting values ​​may be set by default according to the present invention, or may be set by those skilled in the art.

[0079] Example 3

[0080] Figure 3 The present invention shows a flow chart of a relay automatic testing method based on parallel processing. Based on the same inventive concept as Example 1 and Example 2, the present invention provides a relay automatic testing method based on parallel processing, comprising the following steps:

[0081] S1. Record the baseline signal and initial reclosing times increment under normal equipment operation;

[0082] S2, real-time monitoring and acquisition of device signals during the simulated power outage-reclosing process, combined with the baseline signal, analyzing signal changes, and sending the signals to S3;

[0083] S3. Quantify the system state change and obtain the equipment safety state value;

[0084] S4. Create a reclosing number adaptive algorithm, which is used to output the reclosing number increment of each relay in the next round according to the initial reclosing number increment and the device safety status value, and determine whether to continue testing. If so, return to S2; if not, run S5;

[0085] S5. Determine the maximum tolerable reclosing times based on the real-time equipment safety status value and obtain a recovery robustness index.

[0086] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0087] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0088] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0089] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0090] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A relay automatic test system based on parallel processing, characterized in that: include: Initial state acquisition module, used to record the baseline signal and initial reclosing times increment under normal equipment operation; A real-time signal monitoring module is used to monitor and collect device signals in real time during the simulated power outage-reclosing process, analyze signal changes in combination with the baseline signal, and send the signals to the status indicator extraction module; Status indicator extraction module, used to calculate the device safety status value; After each test, the status indicator extraction module calculates the device safety status value based on the status indicators measured in real time, specifically including: The synchronization error is obtained by averaging the absolute values ​​of the differences between the synchronization trigger time recorded by the device and its baseline characteristics; The sampling clock drift is obtained by comparing the cumulative offset of the device sampling clock with the baseline before and after reclosing. The proportion of normal waveform signals observed after reclosing is restored is used to obtain the state retention rate; Normalizing the synchronization error, sampling clock drift, and state retention rate, and performing weighted summation to obtain a device safety state value SSI; A reclosing times adaptive scheduling module is used to create a reclosing times adaptive algorithm, wherein the reclosing times adaptive algorithm is used to output the reclosing times increment of each relay in the next round according to the initial reclosing times increment and the equipment safety status value, and determine whether to continue the test. If so, it returns to the real-time signal monitoring module; otherwise, it runs the recovery robustness assessment module; The reclosing times adaptive algorithm includes: Define the optimization goal and define the particle dimension as two components Composition, including the current reclosing times increment And the steady-state time of this round of testing ; Set the number of particles P. Initially, each particle is evenly distributed within the domain of definition. The domain of definition is expressed as and ,in, 、 、 、 Respectively represent the minimum reclosing times increment, maximum reclosing times increment, minimum test steady-state time, and maximum test steady-state time set by the system; When the current reclosing times increment And the steady-state time of this round of testing After adjusting the number of device reclosing times, the particle speed and particle position are updated according to the particle update strategy. The individual optimal position and the global optimal position are updated after evaluating the fitness of each particle according to the objective function. When the fitness of the individual optimal position of a particle is greater than the fitness of the initial global optimal position, the individual optimal position of the particle is updated as the new global optimal position, and the global optimal position is directly output as the scheduling parameter of each relay in the next round. The recovery robustness evaluation module is used to determine the maximum tolerable number of reclosing times based on the real-time equipment safety status value and obtain the recovery robustness index; the recovery robustness evaluation module continuously performs a fault-reclosing cycle on each relay sample during the test process, and compares the current equipment safety status value with the set equipment safety threshold in each round. When the current equipment safety status value is greater than the equipment safety threshold, the cumulative number of reclosing times completed in the previous round is recorded as the maximum tolerable number of reclosing times for the sample ,Will It is normalized and mapped to the [0,1] interval and recorded as the recovery robustness index.

2. The relay automatic test system based on parallel processing according to claim 1, characterized in that: The initial state acquisition module collects the synchronous trigger time, sampling clock, and waveform signal of each device through the synchronous sampling device, and obtains the baseline characteristics of all data and sends them to the real-time signal monitoring module; Before the test begins, the initial state acquisition module records and initializes an initial reclosing times increment , and sent to the adaptive scheduling module for the number of reclosing times.

3. The relay automatic test system based on parallel processing according to claim 1, characterized in that: Before each simulation event, the real-time signal monitoring module triggers a reclosing event through the control terminal, closes the reclosing switch after a set time after disconnection, and continues to record the device output until the signal stabilizes again after the power is re-applied; records each sampling timestamp before and after the power outage, the peak voltage and waveform of each phase; and performs differential analysis on the baseline characteristics corresponding to all data after each simulation and passes them to the next module.

4. The relay automatic test system based on parallel processing according to claim 1, characterized in that: The reclosing times adaptive scheduling module includes a state trend judgment unit and an adaptive increment adjustment unit; After each test, the state trend judgment unit obtains the health state drift value based on the change in the real-time device safety state value. The latest health status drift is combined with the most recent M historical health status drifts to form a health status drift sequence; The number of positive values ​​N+ and the number of negative values ​​N- in the health status drift sequence are counted. If the number of positive values ​​N+ is greater than 70% of all data in the sequence, the health status is judged to be in a downward trend. If the number of negative values ​​N- is greater than 70% of all data in the sequence, the health status is judged to be in an upward trend. Otherwise, the health status is judged to be oscillating. The adaptive incremental adjustment unit is used to run an adaptive algorithm for the number of reclosing times.

5. The relay automatic test system based on parallel processing according to claim 1, characterized in that: The objective function is expressed as ,in, Indicates the negative impact of health status drift. When the health status is in a downward trend, the health status drift corresponding to the maximum positive value in the health status drift sequence is taken as the negative impact of health status. When the health status is in an upward trend, the negative impact of health status drift is 0. When the health status is oscillating, the average health status drift is taken as the negative impact of health status. Expressed as test duration, expressed as ,in Indicates the average time of one reclosing; represents the maintenance cost, taking , Indicates the set wear coefficient, 、 and Represents the weight coefficient.

6. The relay automatic test system based on parallel processing according to claim 1, characterized in that: The particle update strategy includes: Update particle velocity and particle position according to health state drift and health state, and set particle initial position and speed , defining the optimal position of an individual , the global optimal position g is the current The position corresponding to the minimum value of the objective function; For each particle i=1,2,...,P In the kth iteration, the following rules are executed: the health state drift at the current moment and the health state drift at the previous moment in the health state drift sequence are extracted to achieve inertia weight adaptation and update the inertia weight; for the current particle, the new round of trial speed is first calculated based on the current particle's speed in the previous round, the historical optimal position, and the global current optimal position, combined with the adaptive inertia weight and the learning factor; after obtaining the new speed, the current position of the particle can be updated.

7. The relay automatic test system based on parallel processing according to claim 6, characterized in that: The inertia weight adaptation specifically includes setting the inertia weight interval , then the inertia weight adaptive adjustment formula is: ； in, Indicates the maximum value of health status drift.

8. A relay automated testing method based on parallel processing, characterized in that: A relay automatic test system based on parallel processing as claimed in any one of claims 1 to 7, characterized in that it includes the following steps: S1. Record the baseline signal and initial reclosing times increment under normal equipment operation; S2, real-time monitoring and acquisition of device signals during the simulated power outage-reclosing process, combined with the baseline signal, analyzing signal changes, and sending the signals to S3; S3. Calculate the device security status value; S4. Create a reclosing number adaptive algorithm, which is used to output the reclosing number increment of each relay in the next round according to the initial reclosing number increment and the device safety state value, and determine whether to continue testing. If so, return to S2, otherwise run S5; S5. Determine the maximum tolerable reclosing times based on the real-time equipment safety status value and obtain a recovery robustness index.

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