A circuit breaker arc extinguishing performance online detection method, device and system

Abstract

This invention provides a method, apparatus, and system for online testing of the arc-extinguishing performance of circuit breakers, belonging to the field of online testing of electrical equipment in power systems. The method includes: acquiring the circuit breaker closing current. i ( t Select the one that satisfies i ( t The sampling point corresponding to the instant when the current is greater than 0.01kA is used as the initial value for sampling the closing current, and sampling is performed thereafter to form the closing current sequence. Empirical mode decomposition is performed on each term in the sequence to obtain the trend term sequence of the closing current sequence. The terms are then accumulated to obtain the accumulated result. k ( N ) and add to the sequence k ( n = N In; when satisfied N When ≥15, adopt K - means Algorithm for sequence k ( n = N Clustering is performed, and the number of cluster centers is determined based on the threshold condition of the sum of squared clustering errors. K Is it greater than 1? If the result is no, it is determined that the current arc extinction performance of the circuit breaker is good; if the result is yes, it is determined that the current arc extinction performance of the circuit breaker is abnormal; if the arc extinction performance of the circuit breaker is abnormal, an early warning signal is generated to provide an early warning.

Description

Technical Field

[0001] This invention belongs to the field of online testing of electrical equipment in power systems, and particularly relates to a method, device and system for online testing of the arc extinction performance of circuit breakers. Background Technology

[0002] In a power system, when power facilities malfunction due to environmental factors or external forces, the relay protection device in the power system sends a trip instruction to the circuit breaker. Upon receiving the trip instruction, the circuit breaker performs the tripping operation. The tripping operation is complete when the circuit breaker contacts are completely separated and the electric arc between the contacts is completely extinguished.

[0003] During this process, the contacts and nozzles inside the circuit breaker's arc-extinguishing chamber are subjected to both arc erosion and mechanical fatigue over a long period, which can easily lead to a significant degradation in their arc-extinguishing efficiency. In subsequent breaking operations, the weakened arc-extinguishing capability can easily trigger reignition, breaking failure, or even equipment explosion, posing a serious threat to the safe and stable operation of the power grid. Therefore, it is necessary to monitor the arc-extinguishing performance of the circuit breaker regularly. Summary of the Invention

[0004] In view of this, the present invention provides a method, device and system for online detection of the arc extinction performance of circuit breakers. By analyzing the potential correlation between instantaneous current data during switching operations and the arc extinction performance of circuit breakers, the present invention enables real-time and rapid evaluation of the arc extinction performance of circuit breakers, thereby improving the overall operational stability and safety of the power grid.

[0005] The technical solution adopted by the embodiments of the present invention to solve its technical problem is as follows:

[0006] A method for online testing of the arc-extinguishing performance of a circuit breaker, comprising:

[0007] Step S1: Obtain the circuit breaker closing current i(t);

[0008] Step S2: Select the sampling point corresponding to the instant when i(t) > 0.01kA as the initial value for sampling the closing current and sample backward to form the closing current sequence {i(m)}.

[0009] Step S2: Perform empirical mode decomposition on each term in the sequence {i(m)} to obtain the trend term sequence {i} of the closing current sequence. r (m)};

[0010] Step S3, accumulate sequence {i r The cumulative result k(N) is obtained from each item in (m)}, and k(N) is added to the sequence k(n=N), where N represents the total number of records, and k(n=N)={k(n)};

[0011] Step S4: Determine whether N≥15 is satisfied currently. If not, let N = N + 1 and jump to Step S1 to wait for the next closing of the circuit breaker; if satisfied, continue to execute Step S5;

[0012] Step S5: Use the K-means algorithm to cluster the sequence k(n = N), and confirm whether the number of cluster centers is greater than 1 based on the condition of the sum of squared clustering errors threshold; among them, when the judgment result is no, it is determined that the current arc extinguishing performance of the circuit breaker is good; when the judgment result is yes, it is determined that the current arc extinguishing performance of the circuit breaker is abnormal;

[0013] Step S6: When the arc extinguishing performance state of the circuit breaker is abnormal, generate a warning signal for warning.

[0014] Preferably, the method for collecting the closing current i(t) is: measure the closing current through the current transformer already installed on the circuit breaker or by mounting a Rogowski coil on the circuit breaker, the measurement sampling rate f is greater than 10 kHz, and the total number of sampling points m is greater than 0.003*f.

[0015] Preferably, for the closing current i N (m) collected for the Nth time, the expression for empirical mode decomposition is:

[0016]

[0017] In the formula, i Nqj (m) is the q-order eigenterm sequence of the Nth closing current sequence i N (m); i Nr (m) is the trend term sequence of the Nth closing current sequence.

[0018] Preferably, the expression for the cumulative result k(N) corresponding to the Nth closing is:

[0019]

[0020] Preferably, the step of analyzing whether the number of cluster centers of the sequence k(n = N) in Step S5 is greater than 1 includes:

[0021] Step S51: Randomly select P data points from the sequence k(n = N) as the initial cluster centers C p of P cluster clusters I p , p∈[1, P], P < N, and let the initial value of P be taken as 1;

[0022] Step S52: Calculate the distances d(k(n), C p ) between each item in the sequence k(n = N) and each cluster center C p :

[0023]

[0024] Then, each data point in sequence k (n=N) is assigned to the nearest cluster center, forming P clusters I. p :

[0025] {k(n)∈I p |min[d(k(n),C p )]}

[0026] Calculate each of the cluster I p The mean of all data points in the cluster is used as the cluster I. p The new cluster center;

[0027] Step S53: Calculate the sum of squared errors SSE for the clustering of sequence k (n=N). P :

[0028]

[0029] Step S54: Let P = P+1, repeat steps S51-S53, use P+1 as the total number of cluster centers to perform clustering and calculate SSE. P+1 ;

[0030] Step S55, calculate the ratio D, D = SSE P / SSE P+1 When D>1.5, the number of cluster centers of sequence k (n=N) is 1; when D>1.5 is not satisfied, the number of cluster centers of sequence k (n=N) is greater than 1.

[0031] An online testing device for the arc extinction performance of a circuit breaker includes: at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0032] More preferably, it is also used to: reflect the arc-extinguishing capacity of the circuit breaker's arc-extinguishing chamber by the trend of the closing current change, based on the principle of arc gap energy balance.

[0033] An online detection system for the arc extinction performance of a circuit breaker includes the aforementioned online detection device for the arc extinction performance of a circuit breaker and a current acquisition device, wherein the current acquisition device is used to acquire the closing current of the circuit breaker.

[0034] Preferably, the current acquisition device is a current transformer or a Rogowski coil.

[0035] Preferably, it also includes a cloud server and a receiving terminal, wherein the online detection device for the arc extinction performance of the circuit breaker sends a warning signal to the cloud server; and the online detection device for the arc extinction performance of the circuit breaker sends the warning signal to the receiving terminal.

[0036] As can be seen from the above technical solution, the embodiments of the present invention provide an online detection method, device and system for the arc extinction performance of a circuit breaker. First, the closing current i(t) of the circuit breaker is obtained; the sampling point corresponding to the instant when i(t) > 0.01kA is selected as the initial value for sampling the closing current and then sampled backward to form a closing current sequence; empirical mode decomposition is performed on each item in the sequence to obtain the trend term sequence of the closing current sequence, and the items are accumulated to obtain the accumulated result k(N) and added to the sequence k(n=N); when N≥15, the K-means algorithm is used to cluster the sequence k(n=N), and the number of cluster centers is determined to be greater than 1 based on the threshold condition of the sum of squared clustering errors; if the judgment result is negative, it is determined that the current arc extinction performance of the circuit breaker is good; if the judgment result is positive, it is determined that the current arc extinction performance of the circuit breaker is abnormal; when the arc extinction performance of the circuit breaker is abnormal, an early warning signal is generated to provide an early warning. This invention analyzes the potential correlation between transient current data during switching operations and the arc-extinguishing performance of circuit breakers, enabling real-time and rapid assessment of the arc-extinguishing performance of circuit breakers, thereby effectively improving the overall operational stability and safety of the power grid. Attached Figure Description

[0037] Figure 1 This is a flowchart of the online detection method for the arc extinction performance of circuit breakers according to the present invention.

[0038] Figure 2 This is the equivalent circuit diagram for circuit breaker switching.

[0039] Figure 3 This is a diagram showing the empirical mode decomposition of the circuit breaker closing current.

[0040] Figure 4a The closing current waveform of a circuit breaker with good arc extinguishing performance.

[0041] Figure 4b The closing current waveform of a circuit breaker with deteriorated arc-extinguishing performance.

[0042] Figure 5a A schematic diagram of the clustering results for circuit breakers with good arc-extinguishing performance.

[0043] Figure 5b A schematic diagram of the clustering results for circuit breakers with deteriorated arc-extinguishing performance. Detailed Implementation

[0044] The technical solution and effects of the present invention will be further described in detail below with reference to the accompanying drawings.

[0045] During the closing process of a circuit breaker contact, an electric arc is generated. The combustion and extinguishing of the arc depend on the magnitude of the energy generated and dissipated. When the energy dissipated from the arc gap exceeds the energy generated, the arc gradually tends to extinguish. Heat dissipation per unit time and unit length includes three forms: conductive heat dissipation P T Radiative heat dissipation P S Convection heat dissipation P K The energy balance equation for an electric arc is expressed as follows:

[0046] P = P T +P S +P K (1)

[0047] In the formula: P is the energy generated by the electric arc per unit length, P T P S P K The power output is positively correlated with the arc-extinguishing capacity of the arc-extinguishing chamber. When the circuit breaker's arc-extinguishing chamber is operating normally, P... T P S P K The sum of these three factors is greater than that of the circuit breaker P, thus reducing the arc resistance value. However, arc resistance cannot be directly measured. To find the same characteristic value, an equivalent model of circuit breaker switching is constructed, such as... Figure 2 As shown. Where; U a The bus power supply; R, L, and C are the equivalent resistance, inductance, and capacitance of the circuit, respectively; R a It is a nonlinear arc resistance.

[0048] According to Kirchhoff's laws, the loop expression is as follows:

[0049]

[0050] Where u R (t), u L (t), u C (t), u Ra (t) represent the equivalent resistance voltage, equivalent inductance voltage, equivalent capacitance voltage, and arc resistance voltage, respectively.

[0051] When the electric arc enters the stable combustion stage, the closing current expression can be solved using equation (2):

[0052]

[0053] Where I a Let ω0 be the amplitude of the initial stable combustion current of the electric arc, α be the oscillation frequency, and α be the attenuation coefficient, which is defined as:

[0054]

[0055] As can be seen from equations (3) and (5), the attenuation characteristics of the closing current i(t) mainly depend on (R+R) a (t)) / (2L), under the same circuit breaker operation, R and L are fixed, and i(t) decays due to the arc resistance value R. a Direct impact, and the previous analysis already knew R. a The change in t is related to the arc-extinguishing capacity of the arc-extinguishing chamber, therefore the i(t) decay trend term sequence can indirectly describe the arc-extinguishing capacity of the arc-extinguishing chamber.

[0056] This invention uses the closing current i(t) generated during the closing operation of the circuit breaker as the basis for evaluating the arc-extinguishing performance of the circuit breaker. The collected closing current is decomposed to obtain the intrinsic term sequence and trend term sequence. After obtaining the closing current trend term sequence, the sequence points are accumulated and calculated, and the result is denoted as k(1). The data results k(1), k(2), ... k(n) from multiple measurements are denoted as sequence k (n = N). K-means clustering algorithm is used to perform cluster analysis on sequence k. K-means clustering of sequence k enables the detection and analysis of multiple sets of current trend term sequences to extract feature parameters. The extracted feature parameters are used as the standard for judging the arc-extinguishing performance status of the circuit breaker. If at least one cluster center exists, the arc-extinguishing performance of the arc-extinguishing chamber is considered abnormal, requiring power outage maintenance. (Refer to...) Figure 1 This invention provides an online testing method for the arc-extinguishing performance of a circuit breaker, the specific steps of which include:

[0057] Step S1: Obtain the circuit breaker closing current i(t); Specifically, the closing current can be measured by the current transformer installed on the circuit breaker or by mounting a Rogowski coil on the circuit breaker. The measurement sampling rate f is greater than 10kHz, and the total number of sampling points m is greater than 0.003*f.

[0058] Step S2: Select the sampling point corresponding to the instant when i(t) > 0.01kA as the initial value for sampling the closing current and sample backward to form the closing current sequence {i(m)}.

[0059] Step S2: Perform empirical mode decomposition on each term in the sequence {i(m)} to obtain the trend term sequence {i} of the closing current sequence. r (m)};For the closing current i collected in the Nth time N The expression for empirical mode decomposition (m) is:

[0060]

[0061] In the formula, i Nqj (m) represents the Nth closing current sequence i N (m) is a sequence of q-order eigenterms; i Nr(m) is the sequence trend item sequence of the Nth closing current; by performing empirical mode decomposition on the closing current data when a certain branch circuit breaker trips, the trend item sequence of the original current signal can be obtained, as shown in Figure 3 shown;

[0062] Step S3, accumulate each item in the sequence {i r (m)} to obtain the accumulation result k(N), and add k(N) to the sequence k(n = N), where N represents the total number of records, and k(n = N) = {k(n)}; the expression for the accumulation result k(N) corresponding to the Nth closing is:

[0063]

[0064] Step S4, determine whether N≥15 is satisfied currently, that is, whether the number of recorded closing currents is less than 15 times. If not satisfied, let N = N + 1 and jump to Step S1 to wait for the next closing of the circuit breaker; if satisfied, continue to execute Step S5;

[0065] Step S5, use the K - means algorithm to cluster the sequence k(n = N), and confirm whether the number of cluster centers is greater than 1 based on the clustering error sum of squares threshold condition; among them, when the judgment result is no, it is determined that the current arc extinction performance of the circuit breaker is good; when the judgment result is yes, it is determined that there is an abnormality in the current arc extinction performance of the circuit breaker; [[ID= seventeen]]

[0066] Step S51, randomly select P data points from the sequence k(n = N) as the initial cluster centers C p of P cluster clusters I p , p ∈ [1, P], P < N, and let the initial value of P be taken as 1;

[0067] Step S52, calculate the distance d(k(n), C p ) between each item in the sequence k(n = N) and each cluster center C p :

[0068]

[0069] and assign each data point in the sequence k(n = N) to the cluster center with the closest distance to form P cluster clusters I p :

[0070] {k(n) ∈ I p |min[d(k(n), C p )]} (9)

[0071] Calculate the mean value of all data points in each cluster cluster I p as the new cluster center of the cluster cluster I p ;

[0072] Step S53: Since the sum of squared errors (SSE) of clustering reflects the degree of clustering of data points, the sum of squared errors (SSE) of the clustering of sequence k (n=N) is calculated. P :

[0073]

[0074] Step S54: Let P = P+1, repeat steps S51-S53, use P+1 as the total number of cluster centers to perform clustering and calculate SSE. P+1 ;

[0075] Step S55, calculate the ratio D, D = SSE P / SSE P+1 When D>1.5, the number of cluster centers of sequence k (n=N) is 1; when D>1.5 is not satisfied, the number of cluster centers of sequence k (n=N) is greater than 1.

[0076] Step S6: When the arc-extinguishing performance of the circuit breaker is abnormal, an early warning signal is generated to provide an early warning.

[0077] The detection method is initiated by checking if the absolute value of the circuit breaker current amplitude obtained is greater than the monitoring trigger value of 0.01kA. If it is greater than the monitoring trigger value, the current value in the line is recorded; otherwise, the process returns to continue measuring the current value.

[0078] In step S5, the aforementioned implementation method uses one loop, that is, only the sum of squared errors for the cases of P=1 and P+1=2 are calculated, and the number of cluster centers is confirmed to be P=1 based on whether the ratio D=SSE1 / SSE2 satisfies D>1.5. As an optional implementation method, step S5 can be adjusted. After calculating the ratio D in step S55, if D>1.5 is not satisfied, the process jumps back to step S54, increases the total number of cluster centers by 1, and continues to calculate SSE and the ratio D until D>1.5 is satisfied, then the calculation stops, and it is determined whether the P value that satisfies D>1.5 is greater than 1.

[0079] Furthermore, the present invention provides an online detection device for the arc extinction performance of a circuit breaker, comprising: at least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method.

[0080] Based on the principle of arc gap energy balance, the arc extinguishing capacity of the circuit breaker's arc extinguishing chamber is reflected by the trend of closing current variation.

[0081] The online monitoring process for the arc extinguishing performance of the arc extinguishing chamber is as follows: Figure 1As shown, when actually testing the circuit breaker, the cumulative result of the sequence of the circuit breaker closing current trend term points can be calculated by K-means clustering. If the first N measurements do not exceed one cluster center, an early warning signal is issued. If the circuit breaker status is not monitored, the cumulative result of the next current trend term sequence is recorded as k(N+1) during real-time monitoring. A new sequence is formed from k(2) to k(N+1) to determine whether the K-means clustering circuit breaker arc-extinguishing chamber status is normal. If the status is abnormal, an early warning signal is issued. If the status is normal, the above steps are repeated for status detection.

[0082] Furthermore, this invention provides an online detection system for the arc extinction performance of a circuit breaker, including the aforementioned online detection device for the arc extinction performance of a circuit breaker and a current acquisition device. The current acquisition device is used to acquire the closing current of the circuit breaker. In the system, the current acquisition device uses a current transformer or a Rogowski coil to monitor the instantaneous current value i(t) before the circuit breaker is fully closed in real time. The sampling rate f of the circuit breaker current should be greater than 10kHz. The arc extinction performance detection method starts the detection method by checking whether the absolute value of the acquired circuit breaker current is greater than the monitoring trigger. The monitoring trigger value is 0.01kA. If it is greater than the monitoring trigger, the current value in the line is recorded; otherwise, it returns and continues to measure the current value. The entire arcing time during the closing process is about 3-5ms. The sampling point corresponding to the instant when i(t) is detected to be greater than 0.01kA is the initial value of the closing current sampling. In the subsequent time window, m sampling points are taken to form the closing current sequence i(m), where m is recommended to be greater than 0.003*f.

[0083] The online arc extinction performance monitoring system for circuit breakers also includes a cloud server and a receiving terminal. The online arc extinction performance monitoring device sends the arc extinction performance judgment results and early warning signals of the circuit breaker to the cloud server; the online arc extinction performance monitoring device sends the arc extinction performance judgment results and early warning signals of the circuit breaker to the receiving terminal.

[0084] The online detection device for the arc-extinguishing performance of circuit breakers is also used for: establishing an equivalent model of circuit breaker switching; reflecting the arc-extinguishing capacity of the circuit breaker's arc-extinguishing chamber by the trend of closing current changes based on the principle of arc gap energy balance; acquiring current measurement data during actual operation of the circuit breaker; determining whether the closing current has entered the steady-state combustion stage by setting a relational formula; decomposing the closing current signal into intrinsic terms and trend terms using empirical mode decomposition to extract the closing current trend term sequence; integrating the closing current trend term sequence, performing shortest distance iteration calculations on several calculated integral values ​​to achieve clustering and extract feature parameters; and issuing a warning for abnormal arc-extinguishing performance of the arc-extinguishing chamber based on the number of cluster centers after the clustering process is completed.

[0085] The method of the present invention does not limit the arc extinguishing method of the circuit breaker to be tested. The arc extinguishing performance of circuit breakers such as vacuum circuit breakers and SF6 circuit breakers, which realize the connection and disconnection of the line by extinguishing the arc, can be tested online using this method.

[0086] To clarify the method, let's take the monitoring currents of two different circuit breakers as examples: one with good arc-extinguishing performance and the other with deteriorated arc-extinguishing performance leading to a reignition accident. The closing currents of the circuit breakers with good arc-extinguishing performance and those with deteriorated arc-extinguishing performance are respectively as follows: Figure 4a and Figure 4b As shown.

[0087] The integral values ​​of the closing current trend term sequence for the two circuit breakers are shown in Table 1.

[0088] Table 1. Cumulative values ​​of closing current trends for circuit breakers with good and poor arc-extinguishing performance.

[0089]

[0090] Next, clustering calculations were performed on the data of phases A and B respectively, where the horizontal axis represents the number of data points and the vertical axis represents the cumulative value. The results are as follows: Figure 5a and Figure 5b As shown.

[0091] The closing current of the circuit breaker with deteriorated arc extinction performance showed two cluster centers, and a reignition accident also occurred, which verified the effectiveness of the proposed detection method.

[0092] This invention provides a method, device, and system for online detection of circuit breaker arc extinction performance, comprising: acquiring the closing current signal generated during the circuit breaker closing process as the basis for analyzing and evaluating the circuit breaker arc extinction performance; signal analysis, decomposing the acquired multiple closing current signals, extracting intrinsic terms representing high-frequency and transient components in the signal, and extracting long-term trend terms representing the signal; performing sequence point accumulation calculation on the current trend term sequence obtained from multiple measurements, and using the K-means clustering algorithm to perform cluster analysis on these accumulated trend term sequences. The clustering results can reflect the distribution characteristics of the closing current under different arc extinction states, thereby determining the state of the circuit breaker arc extinction performance. This invention can effectively reflect the trend of circuit breaker arc extinction performance by utilizing the dynamic change characteristics of the closing current generated during circuit breaker closing, enabling online real-time detection of circuit breaker arc extinction performance. The principle is simple, and timely detection of circuit breaker arc extinction performance problems is beneficial to improving the stability of power grid operation.

[0093] This invention uses a current transformer or Rogowski coil installed on the circuit breaker to measure the current signal, and synchronously monitors the arc-extinguishing performance of the circuit breaker online by observing the dynamic changes in the current signal. Using the trend of current signal change as a characteristic parameter, it is easy to operate, intuitive and easy to understand, and can quickly identify and warn of potential arc-extinguishing performance problems. It is of great significance for improving the overall operational stability and safety of the power grid.

[0094] The above-disclosed embodiments are merely preferred embodiments of the present invention and should not be construed as limiting the scope of the invention. Those skilled in the art will understand that implementing all or part of the above-described embodiments and making equivalent changes in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for online testing of the arc-extinguishing performance of a circuit breaker, characterized in that, include: Step S1: Obtain the circuit breaker closing current i(t); Step S2: Select the sampling point corresponding to the instant when i(t) > 0.01kA as the initial value for sampling the closing current and sample backward to form the closing current sequence {i(m)}. Step S2: Perform empirical mode decomposition on each term in the sequence {i(m)} to obtain the trend term sequence {i} of the closing current sequence. r (m)}; Step S3, accumulate sequence {i r The cumulative result k(N) is obtained from each item in (m)}, and k(N) is added to the sequence k(n=N), where N represents the total number of records, and k(n=N)={k(n)}; Step S4: Determine if N≥15 is satisfied. If not, set N=N+1 and jump to step S1, waiting for the circuit breaker to close again. If satisfied, continue to step S5. Step S5: The K-means algorithm is used to cluster the sequence k (n=N), and the number of cluster centers is determined to be greater than 1 based on the threshold condition of the sum of squared clustering errors. If the result is negative, it is determined that the current arc-extinguishing performance of the circuit breaker is good; if the result is positive, it is determined that the current arc-extinguishing performance of the circuit breaker is abnormal. Step S6: When the arc-extinguishing performance of the circuit breaker is abnormal, an early warning signal is generated to provide an early warning.

2. The online detection method for the arc-extinguishing performance of a circuit breaker as described in claim 1, characterized in that, The closing current i(t) is collected by measuring the closing current through the current transformer installed on the circuit breaker or by mounting a Rogowski coil on the circuit breaker. The measurement sampling rate f is greater than 10kHz, and the total number of sampling points m is greater than 0.003*f.

3. The online testing method for the arc-extinguishing performance of a circuit breaker as described in claim 2, characterized in that, For the closing current i collected in the Nth time N The expression for empirical mode decomposition (m) is: In the formula, i Nqj (m) represents the Nth closing current sequence i N (m) is a sequence of q-order eigenterms; i Nr (m) is the trend term sequence of the Nth closing current sequence.

4. The online detection method for the arc-extinguishing performance of a circuit breaker as described in claim 3, characterized in that, The expression for the accumulated result k(N) corresponding to the Nth closing is:

5. The online detection method for the arc extinction performance of a circuit breaker as described in claim 4, characterized in that, Step S5 analyzes whether the number of cluster centers of sequence k (n=N) is greater than 1. The steps include: Step S51: Randomly select P data points from the sequence k(n = N) as the initial clustering centers C of P clustering clusters I p , where p ∈ [1, P] and P < N. Let the initial value of P be 1; p ​ Step S52, calculate the relationship between each term in sequence k (n=N) and each cluster center C. p The distance d(k(n),C p ): Then, each data point in sequence k (n=N) is assigned to the nearest cluster center, forming P clusters I. p : {k(n)∈I p |min[d(k(n),C p )]} Calculate each of the cluster I p The mean of all data points in the cluster is used as the cluster I. p The new cluster center; Step S53: Calculate the sum of squared errors SSE for the clustering of sequence k (n=N). P : Step S54: Let P = P+1, repeat steps S51-S53, use P+1 as the total number of cluster centers to perform clustering and calculate SSE. P+1 ; Step S55, calculate the ratio D, D = SSE P / SSE P+1 When D>1.5, the number of cluster centers of sequence k (n=N) is 1; when D>1.5 is not satisfied, the number of cluster centers of sequence k (n=N) is greater than 1.

6. An online testing device for the arc-extinguishing performance of a circuit breaker, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

7. The online detection device for arc extinction performance of circuit breakers as described in claim 6, characterized in that, It is also used to: reflect the arc-extinguishing capacity of the circuit breaker's arc-extinguishing chamber by the trend of closing current change, based on the principle of arc gap energy balance.

8. An online testing system for the arc-extinguishing performance of a circuit breaker, characterized in that, include: The circuit breaker arc extinction performance online detection device and the current acquisition device according to any one of claims 6-7, wherein the current acquisition device is used to acquire the circuit breaker closing current.

9. The online detection system for arc extinction performance of circuit breakers as described in claim 8, characterized in that, The current acquisition device uses a current transformer or a Rogowski coil.

10. The online detection system for arc extinction performance of circuit breakers as described in claim 9, characterized in that, It also includes a cloud server and a receiving terminal. The online detection device for the arc extinction performance of the circuit breaker sends an early warning signal to the cloud server; the online detection device for the arc extinction performance of the circuit breaker sends the early warning signal to the receiving terminal.

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